A Hybrid Constraint Representation and Reasoning Framework

NASA Technical Reports Server (NTRS)

Golden, Keith; Pang, Wanlin

2004-01-01

In this paper, we introduce JNET, a novel constraint representation and reasoning framework that supports procedural constraints and constraint attachments, providing a flexible way of integrating the constraint system with a runtime software environment and improving its applicability. We describe how JNET is applied to a real-world problem - NASA's Earth-science data processing domain, and demonstrate how JNET can be extended, without any knowledge of how it is implemented, to meet the growing demands of real-world applications.

Understanding the Role of Representation in Interorganizational Knowledge Integration: A Case Study of an IT Outsourcing Project

ERIC Educational Resources Information Center

Werr, Andreas; Runsten, Philip

2013-01-01

Purpose: The current paper aims at contributing to the understanding of interorganizational knowledge integration by highlighting the role of individuals' understandings of the task and how they shape knowledge integrating behaviours. Design/methodology/approach: The paper presents a framework of knowledge integration as heedful interrelating.…

Knowledge representation in fuzzy logic

NASA Technical Reports Server (NTRS)

Zadeh, Lotfi A.

1989-01-01

The author presents a summary of the basic concepts and techniques underlying the application of fuzzy logic to knowledge representation. He then describes a number of examples relating to its use as a computational system for dealing with uncertainty and imprecision in the context of knowledge, meaning, and inference. It is noted that one of the basic aims of fuzzy logic is to provide a computational framework for knowledge representation and inference in an environment of uncertainty and imprecision. In such environments, fuzzy logic is effective when the solutions need not be precise and/or it is acceptable for a conclusion to have a dispositional rather than categorical validity. The importance of fuzzy logic derives from the fact that there are many real-world applications which fit these conditions, especially in the realm of knowledge-based systems for decision-making and control.

Reflections on Assessment

ERIC Educational Resources Information Center

DiRanna, Kathy; Osmundson, Ellen; Topps, Jo; Gearhart, Maryl

2008-01-01

In this article, the authors describe an assessment-centered teaching (ACT) framework they developed to communicate the integration of assessment and instruction. The framework is a visual representation of the relationships among the fundamental elements of assessment knowledge: the characteristics of quality goals for student learning and…

Supporting Self-Regulated Personalised Learning through Competence-Based Knowledge Space Theory

ERIC Educational Resources Information Center

Steiner, Christina M.; Nussbaumer, Alexander; Albert, Dietrich

2009-01-01

This article presents two current research trends in e-learning that at first sight appear to compete. Competence-Based Knowledge Space Theory (CBKST) provides a knowledge representation framework which, since its invention by Doignon & Falmagne, has been successfully applied in various e-learning systems (for example, Adaptive Learning with…

Modeling a flexible representation machinery of human concept learning.

PubMed

Matsuka, Toshihiko; Sakamoto, Yasuaki; Chouchourelou, Arieta

2008-01-01

It is widely acknowledged that categorically organized abstract knowledge plays a significant role in high-order human cognition. Yet, there are many unknown issues about the nature of how categories are internally represented in our mind. Traditionally, it has been considered that there is a single innate internal representation system for categorical knowledge, such as Exemplars, Prototypes, or Rules. However, results of recent empirical and computational studies collectively suggest that the human internal representation system is apparently capable of exhibiting behaviors consistent with various types of internal representation schemes. We, then, hypothesized that humans' representational system as a dynamic mechanism, capable of selecting a representation scheme that meets situational characteristics, including complexities of category structure. The present paper introduces a framework for a cognitive model that integrates robust and flexible internal representation machinery. Three simulation studies were conducted. The results showed that SUPERSET, our new model, successfully exhibited cognitive behaviors that are consistent with three main theories of the human internal representation system. Furthermore, a simulation study on social cognitive behaviors showed that the model was capable of acquiring knowledge with high commonality, even for a category structure with numerous valid conceptualizations.

Linking Somatic and Symbolic Representation in Semantic Memory: The Dynamic Multilevel Reactivation Framework

PubMed Central

Reilly, Jamie; Peelle, Jonathan E; Garcia, Amanda; Crutch, Sebastian J

2016-01-01

Biological plausibility is an essential constraint for any viable model of semantic memory. Yet, we have only the most rudimentary understanding of how the human brain conducts abstract symbolic transformations that underlie word and object meaning. Neuroscience has evolved a sophisticated arsenal of techniques for elucidating the architecture of conceptual representation. Nevertheless, theoretical convergence remains elusive. Here we describe several contrastive approaches to the organization of semantic knowledge, and in turn we offer our own perspective on two recurring questions in semantic memory research: 1) to what extent are conceptual representations mediated by sensorimotor knowledge (i.e., to what degree is semantic memory embodied)? 2) How might an embodied semantic system represent abstract concepts such as modularity, symbol, or proposition? To address these questions, we review the merits of sensorimotor (i.e., embodied) and amodal (i.e., disembodied) semantic theories and address the neurobiological constraints underlying each. We conclude that the shortcomings of both perspectives in their extreme forms necessitate a hybrid middle ground. We accordingly propose the Dynamic Multilevel Reactivation Framework, an integrative model premised upon flexible interplay between sensorimotor and amodal symbolic representations mediated by multiple cortical hubs. We discuss applications of the Dynamic Multilevel Reactivation Framework to abstract and concrete concept representation and describe how a multidimensional conceptual topography based on emotion, sensation, and magnitude can successfully frame a semantic space containing meanings for both abstract and concrete words. The consideration of ‘abstract conceptual features’ does not diminish the role of logical and/or executive processing in activating, manipulating and using information stored in conceptual representations. Rather, it proposes that the material on which these processes operate necessarily combine pure sensorimotor information and higher-order cognitive dimensions involved in symbolic representation. PMID:27294419

Linking somatic and symbolic representation in semantic memory: the dynamic multilevel reactivation framework.

PubMed

Reilly, Jamie; Peelle, Jonathan E; Garcia, Amanda; Crutch, Sebastian J

2016-08-01

Biological plausibility is an essential constraint for any viable model of semantic memory. Yet, we have only the most rudimentary understanding of how the human brain conducts abstract symbolic transformations that underlie word and object meaning. Neuroscience has evolved a sophisticated arsenal of techniques for elucidating the architecture of conceptual representation. Nevertheless, theoretical convergence remains elusive. Here we describe several contrastive approaches to the organization of semantic knowledge, and in turn we offer our own perspective on two recurring questions in semantic memory research: (1) to what extent are conceptual representations mediated by sensorimotor knowledge (i.e., to what degree is semantic memory embodied)? (2) How might an embodied semantic system represent abstract concepts such as modularity, symbol, or proposition? To address these questions, we review the merits of sensorimotor (i.e., embodied) and amodal (i.e., disembodied) semantic theories and address the neurobiological constraints underlying each. We conclude that the shortcomings of both perspectives in their extreme forms necessitate a hybrid middle ground. We accordingly propose the Dynamic Multilevel Reactivation Framework-an integrative model predicated upon flexible interplay between sensorimotor and amodal symbolic representations mediated by multiple cortical hubs. We discuss applications of the dynamic multilevel reactivation framework to abstract and concrete concept representation and describe how a multidimensional conceptual topography based on emotion, sensation, and magnitude can successfully frame a semantic space containing meanings for both abstract and concrete words. The consideration of 'abstract conceptual features' does not diminish the role of logical and/or executive processing in activating, manipulating and using information stored in conceptual representations. Rather, it proposes that the materials upon which these processes operate necessarily combine pure sensorimotor information and higher-order cognitive dimensions involved in symbolic representation.

Towards a category theory approach to analogy: Analyzing re-representation and acquisition of numerical knowledge.

PubMed

Navarrete, Jairo A; Dartnell, Pablo

2017-08-01

Category Theory, a branch of mathematics, has shown promise as a modeling framework for higher-level cognition. We introduce an algebraic model for analogy that uses the language of category theory to explore analogy-related cognitive phenomena. To illustrate the potential of this approach, we use this model to explore three objects of study in cognitive literature. First, (a) we use commutative diagrams to analyze an effect of playing particular educational board games on the learning of numbers. Second, (b) we employ a notion called coequalizer as a formal model of re-representation that explains a property of computational models of analogy called "flexibility" whereby non-similar representational elements are considered matches and placed in structural correspondence. Finally, (c) we build a formal learning model which shows that re-representation, language processing and analogy making can explain the acquisition of knowledge of rational numbers. These objects of study provide a picture of acquisition of numerical knowledge that is compatible with empirical evidence and offers insights on possible connections between notions such as relational knowledge, analogy, learning, conceptual knowledge, re-representation and procedural knowledge. This suggests that the approach presented here facilitates mathematical modeling of cognition and provides novel ways to think about analogy-related cognitive phenomena.

Towards a category theory approach to analogy: Analyzing re-representation and acquisition of numerical knowledge

PubMed Central

2017-01-01

Category Theory, a branch of mathematics, has shown promise as a modeling framework for higher-level cognition. We introduce an algebraic model for analogy that uses the language of category theory to explore analogy-related cognitive phenomena. To illustrate the potential of this approach, we use this model to explore three objects of study in cognitive literature. First, (a) we use commutative diagrams to analyze an effect of playing particular educational board games on the learning of numbers. Second, (b) we employ a notion called coequalizer as a formal model of re-representation that explains a property of computational models of analogy called “flexibility” whereby non-similar representational elements are considered matches and placed in structural correspondence. Finally, (c) we build a formal learning model which shows that re-representation, language processing and analogy making can explain the acquisition of knowledge of rational numbers. These objects of study provide a picture of acquisition of numerical knowledge that is compatible with empirical evidence and offers insights on possible connections between notions such as relational knowledge, analogy, learning, conceptual knowledge, re-representation and procedural knowledge. This suggests that the approach presented here facilitates mathematical modeling of cognition and provides novel ways to think about analogy-related cognitive phenomena. PMID:28841643

Whiteboard Confessionals: Investigating a New Model Using Student Representations in Teaching Astro 101

NASA Astrophysics Data System (ADS)

Prather, Edward

2018-01-01

Astronomy education researchers in the Department of Astronomy at the University of Arizona have been investigating a new framework for getting students to engage in discussions about fundamental astronomy topics. This framework is intended to also provide students with explicit feedback on the correctness and coherency of their mental models on these topics. This framework builds upon our prior efforts to create productive Pedagogical Discipline Representations (PDR). Students are asked to work collaboratively to generate their own representations (drawings, graphs, data tables, etc.) that reflect important characteristics of astrophysical scenarios presented in class. We have found these representation tasks offer tremendous insight into the broad range of ideas and knowledge students possess after instruction that includes both traditional lecture and actively learning strategies. In particular, we find that some of our students are able to correctly answer challenging multiple-choice questions on topics, however, they struggle to accurately create representations of these same topics themselves. Our work illustrates that some of our students are not developing a robust level of discipline fluency with many core ideas in astronomy, even after engaging with active learning strategies.

Towards a Framework for Evaluating and Comparing Diagnosis Algorithms

NASA Technical Reports Server (NTRS)

Kurtoglu, Tolga; Narasimhan, Sriram; Poll, Scott; Garcia,David; Kuhn, Lukas; deKleer, Johan; vanGemund, Arjan; Feldman, Alexander

2009-01-01

Diagnostic inference involves the detection of anomalous system behavior and the identification of its cause, possibly down to a failed unit or to a parameter of a failed unit. Traditional approaches to solving this problem include expert/rule-based, model-based, and data-driven methods. Each approach (and various techniques within each approach) use different representations of the knowledge required to perform the diagnosis. The sensor data is expected to be combined with these internal representations to produce the diagnosis result. In spite of the availability of various diagnosis technologies, there have been only minimal efforts to develop a standardized software framework to run, evaluate, and compare different diagnosis technologies on the same system. This paper presents a framework that defines a standardized representation of the system knowledge, the sensor data, and the form of the diagnosis results and provides a run-time architecture that can execute diagnosis algorithms, send sensor data to the algorithms at appropriate time steps from a variety of sources (including the actual physical system), and collect resulting diagnoses. We also define a set of metrics that can be used to evaluate and compare the performance of the algorithms, and provide software to calculate the metrics.

Translational systems biology using an agent-based approach for dynamic knowledge representation: An evolutionary paradigm for biomedical research.

PubMed

An, Gary C

2010-01-01

The greatest challenge facing the biomedical research community is the effective translation of basic mechanistic knowledge into clinically effective therapeutics. This challenge is most evident in attempts to understand and modulate "systems" processes/disorders, such as sepsis, cancer, and wound healing. Formulating an investigatory strategy for these issues requires the recognition that these are dynamic processes. Representation of the dynamic behavior of biological systems can aid in the investigation of complex pathophysiological processes by augmenting existing discovery procedures by integrating disparate information sources and knowledge. This approach is termed Translational Systems Biology. Focusing on the development of computational models capturing the behavior of mechanistic hypotheses provides a tool that bridges gaps in the understanding of a disease process by visualizing "thought experiments" to fill those gaps. Agent-based modeling is a computational method particularly well suited to the translation of mechanistic knowledge into a computational framework. Utilizing agent-based models as a means of dynamic hypothesis representation will be a vital means of describing, communicating, and integrating community-wide knowledge. The transparent representation of hypotheses in this dynamic fashion can form the basis of "knowledge ecologies," where selection between competing hypotheses will apply an evolutionary paradigm to the development of community knowledge.

Students' understandings of electrochemistry

NASA Astrophysics Data System (ADS)

O'Grady-Morris, Kathryn

Electrochemistry is considered by students to be a difficult topic in chemistry. This research was a mixed methods study guided by the research question: At the end of a unit of study, what are students' understandings of electrochemistry? The framework of analysis used for the qualitative and quantitative data collected in this study was comprised of three categories: types of knowledge used in problem solving, levels of representation of knowledge in chemistry (macroscopic, symbolic, and particulate), and alternative conceptions. Although individually each of the three categories has been reported in previous studies, the contribution of this study is the inter-relationships among them. Semi-structured, task-based interviews were conducted while students were setting up and operating electrochemical cells in the laboratory, and a two-tiered, multiple-choice diagnostic instrument was designed to identify alternative conceptions that students held at the end of the unit. For familiar problems, those involving routine voltaic cells, students used a working-forwards problem-solving strategy, two or three levels of representation of knowledge during explanations, scored higher on both procedural and conceptual knowledge questions in the diagnostic instrument, and held fewer alternative conceptions related to the operation of these cells. For less familiar problems, those involving non-routine voltaic cells and electrolytic cells, students approached problem-solving with procedural knowledge, used only one level of representation of knowledge when explaining the operation of these cells, scored higher on procedural knowledge than conceptual knowledge questions in the diagnostic instrument, and held a greater number of alternative conceptions. Decision routines that involved memorized formulas and procedures were used to solve both quantitative and qualitative problems and the main source of alternative conceptions in this study was the overgeneralization of theory related to the particulate level of representation of knowledge. The findings from this study may contribute further to our understanding of students' conceptions in electrochemistry. Furthermore, understanding the influence of the three categories in the framework of analysis and their inter-relationships on how students make sense of this field may result in a better understanding of classroom practice that could promote the acquisition of conceptual knowledge --- knowledge that is "rich in relationships".

Cross-domain Collaborative Research and People Interoperability: Beyond Knowledge Representation Frameworks

NASA Astrophysics Data System (ADS)

Fox, P. A.; Diviacco, P.; Busato, A.

2016-12-01

Geo-scientific research collaboration commonly faces of complex systems where multiple skills and competences are needed at the same time. Efficacy of such collaboration among researchers then becomes of paramount importance. Multidisciplinary studies draw from domains that are far from each other. Researchers also need to understand: how to extract what data they need and eventually produce something that can be used by others. The management of information and knowledge in this perspective is non-trivial. Interoperability is frequently sought in computer-to-computer environements, so-as to overcome mismatches in vocabulary, data formats, coordinate reference system and so on. Successful researcher collaboration also relies on interoperability of the people! Smaller, synchronous and face-to-face settings for researchers are knownn to enhance people interoperability. However changing settings; either geographically; temporally; or with increasing the team size, diversity, and expertise requires people-computer-people-computer (...) interoperability. To date, knowledge representation framework have been proposed but not proven as necessary and sufficient to achieve multi-way interoperability. In this contribution, we address epistemology and sociology of science advocating for a fluid perspective where science is mostly a social construct, conditioned by cognitive issues; especially cognitive bias. Bias cannot be obliterated. On the contrary it must be carefully taken into consideration. Information-centric interfaces built from different perspectives and ways of thinking by actors with different point of views, approaches and aims, are proposed as a means for enhancing people interoperability in computer-based settings. The contribution will provide details on the approach of augmenting and interfacing to knowledge representation frameworks to the cognitive-conceptual frameworks for people that are needed to meet and exceed collaborative research goals in the 21st century. A web based collaborative portal has been developed that integrates both approaches and will be presented. Reports will be given on initial tests that have encouraging results.

A knowledge based software engineering environment testbed

NASA Technical Reports Server (NTRS)

Gill, C.; Reedy, A.; Baker, L.

1985-01-01

The Carnegie Group Incorporated and Boeing Computer Services Company are developing a testbed which will provide a framework for integrating conventional software engineering tools with Artifical Intelligence (AI) tools to promote automation and productivity. The emphasis is on the transfer of AI technology to the software development process. Experiments relate to AI issues such as scaling up, inference, and knowledge representation. In its first year, the project has created a model of software development by representing software activities; developed a module representation formalism to specify the behavior and structure of software objects; integrated the model with the formalism to identify shared representation and inheritance mechanisms; demonstrated object programming by writing procedures and applying them to software objects; used data-directed and goal-directed reasoning to, respectively, infer the cause of bugs and evaluate the appropriateness of a configuration; and demonstrated knowledge-based graphics. Future plans include introduction of knowledge-based systems for rapid prototyping or rescheduling; natural language interfaces; blackboard architecture; and distributed processing

MediaNet: a multimedia information network for knowledge representation

NASA Astrophysics Data System (ADS)

Benitez, Ana B.; Smith, John R.; Chang, Shih-Fu

2000-10-01

In this paper, we present MediaNet, which is a knowledge representation framework that uses multimedia content for representing semantic and perceptual information. The main components of MediaNet include conceptual entities, which correspond to real world objects, and relationships among concepts. MediaNet allows the concepts and relationships to be defined or exemplified by multimedia content such as images, video, audio, graphics, and text. MediaNet models the traditional relationship types such as generalization and aggregation but adds additional functionality by modeling perceptual relationships based on feature similarity. For example, MediaNet allows a concept such as car to be defined as a type of a transportation vehicle, but which is further defined and illustrated through example images, videos and sounds of cars. In constructing the MediaNet framework, we have built on the basic principles of semiotics and semantic networks in addition to utilizing the audio-visual content description framework being developed as part of the MPEG-7 multimedia content description standard. By integrating both conceptual and perceptual representations of knowledge, MediaNet has potential to impact a broad range of applications that deal with multimedia content at the semantic and perceptual levels. In particular, we have found that MediaNet can improve the performance of multimedia retrieval applications by using query expansion, refinement and translation across multiple content modalities. In this paper, we report on experiments that use MediaNet in searching for images. We construct the MediaNet knowledge base using both WordNet and an image network built from multiple example images and extracted color and texture descriptors. Initial experimental results demonstrate improved retrieval effectiveness using MediaNet in a content-based retrieval system.

Exploring Conceptual Frameworks of Models of Atomic Structures and Periodic Variations, Chemical Bonding, and Molecular Shape and Polarity: A Comparison of Undergraduate General Chemistry Students with High and Low Levels of Content Knowledge

ERIC Educational Resources Information Center

Wang, Chia-Yu; Barrow, Lloyd H.

2013-01-01

The purpose of the study was to explore students' conceptual frameworks of models of atomic structure and periodic variations, chemical bonding, and molecular shape and polarity, and how these conceptual frameworks influence their quality of explanations and ability to shift among chemical representations. This study employed a purposeful sampling…

Dynamic knowledge representation using agent-based modeling: ontology instantiation and verification of conceptual models.

PubMed

An, Gary

2009-01-01

The sheer volume of biomedical research threatens to overwhelm the capacity of individuals to effectively process this information. Adding to this challenge is the multiscale nature of both biological systems and the research community as a whole. Given this volume and rate of generation of biomedical information, the research community must develop methods for robust representation of knowledge in order for individuals, and the community as a whole, to "know what they know." Despite increasing emphasis on "data-driven" research, the fact remains that researchers guide their research using intuitively constructed conceptual models derived from knowledge extracted from publications, knowledge that is generally qualitatively expressed using natural language. Agent-based modeling (ABM) is a computational modeling method that is suited to translating the knowledge expressed in biomedical texts into dynamic representations of the conceptual models generated by researchers. The hierarchical object-class orientation of ABM maps well to biomedical ontological structures, facilitating the translation of ontologies into instantiated models. Furthermore, ABM is suited to producing the nonintuitive behaviors that often "break" conceptual models. Verification in this context is focused at determining the plausibility of a particular conceptual model, and qualitative knowledge representation is often sufficient for this goal. Thus, utilized in this fashion, ABM can provide a powerful adjunct to other computational methods within the research process, as well as providing a metamodeling framework to enhance the evolution of biomedical ontologies.

A knowledge engineering framework towards clinical support for adverse drug event prevention: the PSIP approach.

PubMed

Koutkias, Vassilis; Stalidis, George; Chouvarda, Ioanna; Lazou, Katerina; Kilintzis, Vassilis; Maglaveras, Nicos

2009-01-01

Adverse Drug Events (ADEs) are currently considered as a major public health issue, endangering patients' safety and causing significant healthcare costs. Several research efforts are currently concentrating on the reduction of preventable ADEs by employing Information Technology (IT) solutions, which aim to provide healthcare professionals and patients with relevant knowledge and decision support tools. In this context, we present a knowledge engineering approach towards the construction of a Knowledge-based System (KBS) regarded as the core part of a CDSS (Clinical Decision Support System) for ADE prevention, all developed in the context of the EU-funded research project PSIP (Patient Safety through Intelligent Procedures in Medication). In the current paper, we present the knowledge sources considered in PSIP and the implications they pose to knowledge engineering, the methodological approach followed, as well as the components defining the knowledge engineering framework based on relevant state-of-the-art technologies and representation formalisms.

Concepts, Control, and Context: A Connectionist Account of Normal and Disordered Semantic Cognition

PubMed Central

2018-01-01

Semantic cognition requires conceptual representations shaped by verbal and nonverbal experience and executive control processes that regulate activation of knowledge to meet current situational demands. A complete model must also account for the representation of concrete and abstract words, of taxonomic and associative relationships, and for the role of context in shaping meaning. We present the first major attempt to assimilate all of these elements within a unified, implemented computational framework. Our model combines a hub-and-spoke architecture with a buffer that allows its state to be influenced by prior context. This hybrid structure integrates the view, from cognitive neuroscience, that concepts are grounded in sensory-motor representation with the view, from computational linguistics, that knowledge is shaped by patterns of lexical co-occurrence. The model successfully codes knowledge for abstract and concrete words, associative and taxonomic relationships, and the multiple meanings of homonyms, within a single representational space. Knowledge of abstract words is acquired through (a) their patterns of co-occurrence with other words and (b) acquired embodiment, whereby they become indirectly associated with the perceptual features of co-occurring concrete words. The model accounts for executive influences on semantics by including a controlled retrieval mechanism that provides top-down input to amplify weak semantic relationships. The representational and control elements of the model can be damaged independently, and the consequences of such damage closely replicate effects seen in neuropsychological patients with loss of semantic representation versus control processes. Thus, the model provides a wide-ranging and neurally plausible account of normal and impaired semantic cognition. PMID:29733663

A Pedagogical Challenge: Integrative Thinking.

ERIC Educational Resources Information Center

Peterson, Nils S.; And Others

An instructional framework which provides opportunities for students to synthesize knowledge and recognize general approaches to problem representation and solution is advocated and discussed in this paper. The need for a combination of an interdisciplinary approach with the new technologies of the information age is emphasized. Major ideas…

Development of description framework of pharmacodynamics ontology and its application to possible drug-drug interaction reasoning.

PubMed

Imai, Takeshi; Hayakawa, Masayo; Ohe, Kazuhiko

2013-01-01

Prediction of synergistic or antagonistic effects of drug-drug interaction (DDI) in vivo has been of considerable interest over the years. Formal representation of pharmacological knowledge such as ontology is indispensable for machine reasoning of possible DDIs. However, current pharmacology knowledge bases are not sufficient to provide formal representation of DDI information. With this background, this paper presents: (1) a description framework of pharmacodynamics ontology; and (2) a methodology to utilize pharmacodynamics ontology to detect different types of possible DDI pairs with supporting information such as underlying pharmacodynamics mechanisms. We also evaluated our methodology in the field of drugs related to noradrenaline signal transduction process and 11 different types of possible DDI pairs were detected. The main features of our methodology are the explanation capability of the reason for possible DDIs and the distinguishability of different types of DDIs. These features will not only be useful for providing supporting information to prescribers, but also for large-scale monitoring of drug safety.

Framework Support For Knowledge-Based Software Development

NASA Astrophysics Data System (ADS)

Huseth, Steve

1988-03-01

The advent of personal engineering workstations has brought substantial information processing power to the individual programmer. Advanced tools and environment capabilities supporting the software lifecycle are just beginning to become generally available. However, many of these tools are addressing only part of the software development problem by focusing on rapid construction of self-contained programs by a small group of talented engineers. Additional capabilities are required to support the development of large programming systems where a high degree of coordination and communication is required among large numbers of software engineers, hardware engineers, and managers. A major player in realizing these capabilities is the framework supporting the software development environment. In this paper we discuss our research toward a Knowledge-Based Software Assistant (KBSA) framework. We propose the development of an advanced framework containing a distributed knowledge base that can support the data representation needs of tools, provide environmental support for the formalization and control of the software development process, and offer a highly interactive and consistent user interface.

The ventral visual pathway: an expanded neural framework for the processing of object quality.

PubMed

Kravitz, Dwight J; Saleem, Kadharbatcha S; Baker, Chris I; Ungerleider, Leslie G; Mishkin, Mortimer

2013-01-01

Since the original characterization of the ventral visual pathway, our knowledge of its neuroanatomy, functional properties, and extrinsic targets has grown considerably. Here we synthesize this recent evidence and propose that the ventral pathway is best understood as a recurrent occipitotemporal network containing neural representations of object quality both utilized and constrained by at least six distinct cortical and subcortical systems. Each system serves its own specialized behavioral, cognitive, or affective function, collectively providing the raison d'être for the ventral visual pathway. This expanded framework contrasts with the depiction of the ventral visual pathway as a largely serial staged hierarchy culminating in singular object representations and more parsimoniously incorporates attentional, contextual, and feedback effects. Published by Elsevier Ltd.

Using Knowledge Space Theory To Assess Student Understanding of Stoichiometry

NASA Astrophysics Data System (ADS)

Arasasingham, Ramesh D.; Taagepera, Mare; Potter, Frank; Lonjers, Stacy

2004-10-01

Using the concept of stoichiometry we examined the ability of beginning college chemistry students to make connections among the molecular, symbolic, and graphical representations of chemical phenomena, as well as to conceptualize, visualize, and solve numerical problems. Students took a test designed to follow conceptual development; we then analyzed student responses and the connectivities of their responses, or the cognitive organization of the material or thinking patterns, applying knowledge space theory (KST). The results reveal that the students' logical frameworks of conceptual understanding were very weak and lacked an integrated understanding of some of the fundamental aspects of chemical reactivity. Analysis of response states indicates that the overall thinking patterns began with symbolic representations, moved to numerical problem solving, and then lastly to visualization: the acquisition of visualization skills comes later in the knowledge structure. The results strongly suggest the need for teaching approaches that help students integrate their knowledge by emphasizing the relationships between the different representations and presenting them concurrently during instruction. Also, the results indicate that KST is a useful tool for revealing various aspects of students' cognitive structure in chemistry and can be used as an assessment tool or as a pedagogical tool to address a number of student-learning issues.

Generic Educational Knowledge Representation for Adaptive and Cognitive Systems

ERIC Educational Resources Information Center

Caravantes, Arturo; Galan, Ramon

2011-01-01

The interoperability of educational systems, encouraged by the development of specifications, standards and tools related to the Semantic Web is limited to the exchange of information in domain and student models. High system interoperability requires that a common framework be defined that represents the functional essence of educational systems.…

Causal Connections in Beginning Reading: The Importance of Rhyme.

ERIC Educational Resources Information Center

Goswami, Usha

2000-01-01

Discusses the implications of Goswami and Bryant's (1990) theory about important causal connections in reading for classroom teaching, and reviews more recent "rhyme and analogy" research within this framework. Discusses new research on the nature of the English spelling system and the representation of linguistic knowledge. Emphasizes the…

Action semantics: A unifying conceptual framework for the selective use of multimodal and modality-specific object knowledge

NASA Astrophysics Data System (ADS)

van Elk, Michiel; van Schie, Hein; Bekkering, Harold

2014-06-01

Our capacity to use tools and objects is often considered one of the hallmarks of the human species. Many objects greatly extend our bodily capabilities to act in the physical world, such as when using a hammer or a saw. In addition, humans have the remarkable capability to use objects in a flexible fashion and to combine multiple objects in complex actions. We prepare coffee, cook dinner and drive our car. In this review we propose that humans have developed declarative and procedural knowledge, i.e. action semantics that enables us to use objects in a meaningful way. A state-of-the-art review of research on object use is provided, involving behavioral, developmental, neuropsychological and neuroimaging studies. We show that research in each of these domains is characterized by similar discussions regarding (1) the role of object affordances, (2) the relation between goals and means in object use and (3) the functional and neural organization of action semantics. We propose a novel conceptual framework of action semantics to address these issues and to integrate the previous findings. We argue that action semantics entails both multimodal object representations and modality-specific sub-systems, involving manipulation knowledge, functional knowledge and representations of the sensory and proprioceptive consequences of object use. Furthermore, we argue that action semantics are hierarchically organized and selectively activated and used depending on the action intention of the actor and the current task context. Our framework presents an integrative account of multiple findings and perspectives on object use that may guide future studies in this interdisciplinary domain.

Toward a computational theory for motion understanding: The expert animators model

NASA Technical Reports Server (NTRS)

Mohamed, Ahmed S.; Armstrong, William W.

1988-01-01

Artificial intelligence researchers claim to understand some aspect of human intelligence when their model is able to emulate it. In the context of computer graphics, the ability to go from motion representation to convincing animation should accordingly be treated not simply as a trick for computer graphics programmers but as important epistemological and methodological goal. In this paper we investigate a unifying model for animating a group of articulated bodies such as humans and robots in a three-dimensional environment. The proposed model is considered in the framework of knowledge representation and processing, with special reference to motion knowledge. The model is meant to help setting the basis for a computational theory for motion understanding applied to articulated bodies.

Theory-based Bayesian models of inductive learning and reasoning.

PubMed

Tenenbaum, Joshua B; Griffiths, Thomas L; Kemp, Charles

2006-07-01

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.

The ventral visual pathway: An expanded neural framework for the processing of object quality

PubMed Central

Kravitz, Dwight J.; Saleem, Kadharbatcha S.; Baker, Chris I.; Ungerleider, Leslie G.; Mishkin, Mortimer

2012-01-01

Since the original characterization of the ventral visual pathway our knowledge of its neuroanatomy, functional properties, and extrinsic targets has grown considerably. Here we synthesize this recent evidence and propose that the ventral pathway is best understood as a recurrent occipitotemporal network containing neural representations of object quality both utilized and constrained by at least six distinct cortical and subcortical systems. Each system serves its own specialized behavioral, cognitive, or affective function, collectively providing the raison d’etre for the ventral visual pathway. This expanded framework contrasts with the depiction of the ventral visual pathway as a largely serial staged hierarchy that culminates in singular object representations for utilization mainly by ventrolateral prefrontal cortex and, more parsimoniously than this account, incorporates attentional, contextual, and feedback effects. PMID:23265839

CHAMPION: Intelligent Hierarchical Reasoning Agents for Enhanced Decision Support

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hohimer, Ryan E.; Greitzer, Frank L.; Noonan, Christine F.

2011-11-15

We describe the design and development of an advanced reasoning framework employing semantic technologies, organized within a hierarchy of computational reasoning agents that interpret domain specific information. Designed based on an inspirational metaphor of the pattern recognition functions performed by the human neocortex, the CHAMPION reasoning framework represents a new computational modeling approach that derives invariant knowledge representations through memory-prediction belief propagation processes that are driven by formal ontological language specification and semantic technologies. The CHAMPION framework shows promise for enhancing complex decision making in diverse problem domains including cyber security, nonproliferation and energy consumption analysis.

Object oriented studies into artificial space debris

NASA Technical Reports Server (NTRS)

Adamson, J. M.; Marshall, G.

1988-01-01

A prototype simulation is being developed under contract to the Royal Aerospace Establishment (RAE), Farnborough, England, to assist in the discrimination of artificial space objects/debris. The methodology undertaken has been to link Object Oriented programming, intelligent knowledge based system (IKBS) techniques and advanced computer technology with numeric analysis to provide a graphical, symbolic simulation. The objective is to provide an additional layer of understanding on top of conventional classification methods. Use is being made of object and rule based knowledge representation, multiple reasoning, truth maintenance and uncertainty. Software tools being used include Knowledge Engineering Environment (KEE) and SymTactics for knowledge representation. Hooks are being developed within the SymTactics framework to incorporate mathematical models describing orbital motion and fragmentation. Penetration and structural analysis can also be incorporated. SymTactics is an Object Oriented discrete event simulation tool built as a domain specific extension to the KEE environment. The tool provides facilities for building, debugging and monitoring dynamic (military) simulations.

Design and Development of a Sharable Clinical Decision Support System Based on a Semantic Web Service Framework.

PubMed

Zhang, Yi-Fan; Gou, Ling; Tian, Yu; Li, Tian-Chang; Zhang, Mao; Li, Jing-Song

2016-05-01

Clinical decision support (CDS) systems provide clinicians and other health care stakeholders with patient-specific assessments or recommendations to aid in the clinical decision-making process. Despite their demonstrated potential for improving health care quality, the widespread availability of CDS systems has been limited mainly by the difficulty and cost of sharing CDS knowledge among heterogeneous healthcare information systems. The purpose of this study was to design and develop a sharable clinical decision support (S-CDS) system that meets this challenge. The fundamental knowledge base consists of independent and reusable knowledge modules (KMs) to meet core CDS needs, wherein each KM is semantically well defined based on the standard information model, terminologies, and representation formalisms. A semantic web service framework was developed to identify, access, and leverage these KMs across diverse CDS applications and care settings. The S-CDS system has been validated in two distinct client CDS applications. Model-level evaluation results confirmed coherent knowledge representation. Application-level evaluation results reached an overall accuracy of 98.66 % and a completeness of 96.98 %. The evaluation results demonstrated the technical feasibility and application prospect of our approach. Compared with other CDS engineering efforts, our approach facilitates system development and implementation and improves system maintainability, scalability and efficiency, which contribute to the widespread adoption of effective CDS within the healthcare domain.

Integration Framework of Process Planning based on Resource Independent Operation Summary to Support Collaborative Manufacturing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kulvatunyou, Boonserm; Wysk, Richard A.; Cho, Hyunbo

2004-06-01

In today's global manufacturing environment, manufacturing functions are distributed as never before. Design, engineering, fabrication, and assembly of new products are done routinely in many different enterprises scattered around the world. Successful business transactions require the sharing of design and engineering data on an unprecedented scale. This paper describes a framework that facilitates the collaboration of engineering tasks, particularly process planning and analysis, to support such globalized manufacturing activities. The information models of data and the software components that integrate those information models are described. The integration framework uses an Integrated Product and Process Data (IPPD) representation called a Resourcemore » Independent Operation Summary (RIOS) to facilitate the communication of business and manufacturing requirements. Hierarchical process modeling, process planning decomposition and an augmented AND/OR directed graph are used in this representation. The Resource Specific Process Planning (RSPP) module assigns required equipment and tools, selects process parameters, and determines manufacturing costs based on two-level hierarchical RIOS data. The shop floor knowledge (resource and process knowledge) and a hybrid approach (heuristic and linear programming) to linearize the AND/OR graph provide the basis for the planning. Finally, a prototype system is developed and demonstrated with an exemplary part. Java and XML (Extensible Markup Language) are used to ensure software and information portability.« less

Induction as Knowledge Integration

NASA Technical Reports Server (NTRS)

Smith, Benjamin D.; Rosenbloom, Paul S.

1996-01-01

Two key issues for induction algorithms are the accuracy of the learned hypothesis and the computational resources consumed in inducing that hypothesis. One of the most promising ways to improve performance along both dimensions is to make use of additional knowledge. Multi-strategy learning algorithms tackle this problem by employing several strategies for handling different kinds of knowledge in different ways. However, integrating knowledge into an induction algorithm can be difficult when the new knowledge differs significantly from the knowledge the algorithm already uses. In many cases the algorithm must be rewritten. This paper presents Knowledge Integration framework for Induction (KII), a KII, that provides a uniform mechanism for integrating knowledge into induction. In theory, arbitrary knowledge can be integrated with this mechanism, but in practice the knowledge representation language determines both the knowledge that can be integrated, and the costs of integration and induction. By instantiating KII with various set representations, algorithms can be generated at different trade-off points along these dimensions. One instantiation of KII, called RS-KII, is presented that can implement hybrid induction algorithms, depending on which knowledge it utilizes. RS-KII is demonstrated to implement AQ-11, as well as a hybrid algorithm that utilizes a domain theory and noisy examples. Other algorithms are also possible.

Four-Wheel Drivescapes: Embodied Understandings of the Kimberley

ERIC Educational Resources Information Center

Waitt, Gordon; Lane, Ruth

2007-01-01

In this paper, we explore understandings of the Kimberley as wilderness through the embodied knowledge of sites encountered on the travels of four-wheel drivers. We critically review attempts to conceptualise the social role of automobiles in touring practices then turn to non-representational theory to develop our own conceptual framework of…

Learner-Information Interaction: A Macro-Level Framework Characterizing Visual Cognitive Tools

ERIC Educational Resources Information Center

Sedig, Kamran; Liang, Hai-Ning

2008-01-01

Visual cognitive tools (VCTs) are external mental aids that maintain and display visual representations (VRs) of information (i.e., structures, objects, concepts, ideas, and problems). VCTs allow learners to operate upon the VRs to perform epistemic (i.e., reasoning and knowledge-based) activities. In VCTs, the mechanism by which learners operate…

Panacea, a semantic-enabled drug recommendations discovery framework.

PubMed

Doulaverakis, Charalampos; Nikolaidis, George; Kleontas, Athanasios; Kompatsiaris, Ioannis

2014-03-06

Personalized drug prescription can be benefited from the use of intelligent information management and sharing. International standard classifications and terminologies have been developed in order to provide unique and unambiguous information representation. Such standards can be used as the basis of automated decision support systems for providing drug-drug and drug-disease interaction discovery. Additionally, Semantic Web technologies have been proposed in earlier works, in order to support such systems. The paper presents Panacea, a semantic framework capable of offering drug-drug and drug-diseases interaction discovery. For enabling this kind of service, medical information and terminology had to be translated to ontological terms and be appropriately coupled with medical knowledge of the field. International standard classifications and terminologies, provide the backbone of the common representation of medical data while the medical knowledge of drug interactions is represented by a rule base which makes use of the aforementioned standards. Representation is based on a lightweight ontology. A layered reasoning approach is implemented where at the first layer ontological inference is used in order to discover underlying knowledge, while at the second layer a two-step rule selection strategy is followed resulting in a computationally efficient reasoning approach. Details of the system architecture are presented while also giving an outline of the difficulties that had to be overcome. Panacea is evaluated both in terms of quality of recommendations against real clinical data and performance. The quality recommendation gave useful insights regarding requirements for real world deployment and revealed several parameters that affected the recommendation results. Performance-wise, Panacea is compared to a previous published work by the authors, a service for drug recommendations named GalenOWL, and presents their differences in modeling and approach to the problem, while also pinpointing the advantages of Panacea. Overall, the paper presents a framework for providing an efficient drug recommendations service where Semantic Web technologies are coupled with traditional business rule engines.

Representations of everyday life: a proposal for capturing social values from the Marxist perspective of knowledge production.

PubMed

Soares, Cássia Baldini; Santos, Vilmar Ezequiel Dos; Campos, Célia Maria Sivalli; Lachtim, Sheila Aparecida Ferreira; Campos, Fernanda Cristina

2011-12-01

We propose from the Marxist perspective of the construction of knowledge, a theoretical and methodological framework for understanding social values by capturing everyday representations. We assume that scientific research brings together different dimensions: epistemological, theoretical and methodological that consistently to the other instances, proposes a set of operating procedures and techniques for capturing and analyzing the reality under study in order to expose the investigated object. The study of values reveals the essentiality of the formation of judgments and choices, there are values that reflect the dominant ideology, spanning all social classes, but there are values that reflect class interests, these are not universal, they are formed in relationships and social activities. Basing on the Marxist theory of consciousness, representations are discursive formulations of everyday life - opinion or conviction - issued by subjects about their reality, being a coherent way of understanding and exposure social values: focus groups show is suitable for grasping opinions while interviews show potential to expose convictions.

Representing energy efficiency diagnosis strategies in cognitive work analysis.

PubMed

Hilliard, Antony; Jamieson, Greg A

2017-03-01

This article describes challenges encountered in applying Jens Rasmussen's Cognitive Work Analysis (CWA) framework to the practice of energy efficiency Monitoring & Targeting (M&T). Eight theoretic issues encountered in the analysis are described with respect to Rasmussen's work and the modeling solutions we adopted. We grappled with how to usefully apply Work Domain Analysis (WDA) to analyze categories of domains with secondary purposes and no ideal grain of decomposition. This difficulty encouraged us to pursue Control Task (ConTA) and Strategies (StrA) analysis, which are under-explored as bases for interface design. In ConTA we found M&T was best represented by two interlinked work functions; one controlling energy, the other maintaining knowledge representations. From StrA, we identified a popular representation-dependent strategy and inferred information required to diagnose faults in system performance and knowledge representation. This article presents and discusses excerpts from our analysis, and outlines their application to diagnosis support tools. Copyright © 2015 Elsevier Ltd. All rights reserved.

Action semantics: A unifying conceptual framework for the selective use of multimodal and modality-specific object knowledge.

PubMed

van Elk, Michiel; van Schie, Hein; Bekkering, Harold

2014-06-01

Our capacity to use tools and objects is often considered one of the hallmarks of the human species. Many objects greatly extend our bodily capabilities to act in the physical world, such as when using a hammer or a saw. In addition, humans have the remarkable capability to use objects in a flexible fashion and to combine multiple objects in complex actions. We prepare coffee, cook dinner and drive our car. In this review we propose that humans have developed declarative and procedural knowledge, i.e. action semantics that enables us to use objects in a meaningful way. A state-of-the-art review of research on object use is provided, involving behavioral, developmental, neuropsychological and neuroimaging studies. We show that research in each of these domains is characterized by similar discussions regarding (1) the role of object affordances, (2) the relation between goals and means in object use and (3) the functional and neural organization of action semantics. We propose a novel conceptual framework of action semantics to address these issues and to integrate the previous findings. We argue that action semantics entails both multimodal object representations and modality-specific sub-systems, involving manipulation knowledge, functional knowledge and representations of the sensory and proprioceptive consequences of object use. Furthermore, we argue that action semantics are hierarchically organized and selectively activated and used depending on the action intention of the actor and the current task context. Our framework presents an integrative account of multiple findings and perspectives on object use that may guide future studies in this interdisciplinary domain. Copyright © 2013 Elsevier B.V. All rights reserved.

Ontology to relational database transformation for web application development and maintenance

NASA Astrophysics Data System (ADS)

Mahmudi, Kamal; Inggriani Liem, M. M.; Akbar, Saiful

2018-03-01

Ontology is used as knowledge representation while database is used as facts recorder in a KMS (Knowledge Management System). In most applications, data are managed in a database system and updated through the application and then they are transformed to knowledge as needed. Once a domain conceptor defines the knowledge in the ontology, application and database can be generated from the ontology. Most existing frameworks generate application from its database. In this research, ontology is used for generating the application. As the data are updated through the application, a mechanism is designed to trigger an update to the ontology so that the application can be rebuilt based on the newest ontology. By this approach, a knowledge engineer has a full flexibility to renew the application based on the latest ontology without dependency to a software developer. In many cases, the concept needs to be updated when the data changed. The framework is built and tested in a spring java environment. A case study was conducted to proof the concepts.

Understanding visualization: a formal approach using category theory and semiotics.

PubMed

Vickers, Paul; Faith, Joe; Rossiter, Nick

2013-06-01

This paper combines the vocabulary of semiotics and category theory to provide a formal analysis of visualization. It shows how familiar processes of visualization fit the semiotic frameworks of both Saussure and Peirce, and extends these structures using the tools of category theory to provide a general framework for understanding visualization in practice, including: Relationships between systems, data collected from those systems, renderings of those data in the form of representations, the reading of those representations to create visualizations, and the use of those visualizations to create knowledge and understanding of the system under inspection. The resulting framework is validated by demonstrating how familiar information visualization concepts (such as literalness, sensitivity, redundancy, ambiguity, generalizability, and chart junk) arise naturally from it and can be defined formally and precisely. This paper generalizes previous work on the formal characterization of visualization by, inter alia, Ziemkiewicz and Kosara and allows us to formally distinguish properties of the visualization process that previous work does not.

The Service Environment for Enhanced Knowledge and Research (SEEKR) Framework

NASA Astrophysics Data System (ADS)

King, T. A.; Walker, R. J.; Weigel, R. S.; Narock, T. W.; McGuire, R. E.; Candey, R. M.

2011-12-01

The Service Environment for Enhanced Knowledge and Research (SEEKR) Framework is a configurable service oriented framework to enable the discovery, access and analysis of data shared in a community. The SEEKR framework integrates many existing independent services through the use of web technologies and standard metadata. Services are hosted on systems by using an application server and are callable by using REpresentational State Transfer (REST) protocols. Messages and metadata are transferred with eXtensible Markup Language (XML) encoding which conform to a published XML schema. Space Physics Archive Search and Extract (SPASE) metadata is central to utilizing the services. Resources (data, documents, software, etc.) are described with SPASE and the associated Resource Identifier is used to access and exchange resources. The configurable options for the service can be set by using a web interface. Services are packaged as web application resource (WAR) files for direct deployment on application services such as Tomcat or Jetty. We discuss the composition of the SEEKR framework, how new services can be integrated and the steps necessary to deploying the framework. The SEEKR Framework emerged from NASA's Virtual Magnetospheric Observatory (VMO) and other systems and we present an overview of these systems from a SEEKR Framework perspective.

Spatiotemporal integration of molecular and anatomical data in virtual reality using semantic mapping.

PubMed

Soh, Jung; Turinsky, Andrei L; Trinh, Quang M; Chang, Jasmine; Sabhaney, Ajay; Dong, Xiaoli; Gordon, Paul Mk; Janzen, Ryan Pw; Hau, David; Xia, Jianguo; Wishart, David S; Sensen, Christoph W

2009-01-01

We have developed a computational framework for spatiotemporal integration of molecular and anatomical datasets in a virtual reality environment. Using two case studies involving gene expression data and pharmacokinetic data, respectively, we demonstrate how existing knowledge bases for molecular data can be semantically mapped onto a standardized anatomical context of human body. Our data mapping methodology uses ontological representations of heterogeneous biomedical datasets and an ontology reasoner to create complex semantic descriptions of biomedical processes. This framework provides a means to systematically combine an increasing amount of biomedical imaging and numerical data into spatiotemporally coherent graphical representations. Our work enables medical researchers with different expertise to simulate complex phenomena visually and to develop insights through the use of shared data, thus paving the way for pathological inference, developmental pattern discovery and biomedical hypothesis testing.

Handling knowledge via Concept Maps: a space weather use case

NASA Astrophysics Data System (ADS)

Messerotti, Mauro; Fox, Peter

Concept Maps (Cmaps) are powerful means for knowledge coding in graphical form. As flexible software tools exist to manipulate the knowledge embedded in Cmaps in machine-readable form, such complex entities are suitable candidates not only for the representation of ontologies and semantics in Virtual Observatory (VO) architectures, but also for knowledge handling and knowledge discovery. In this work, we present a use case relevant to space weather applications and we elaborate on its possible implementation and adavanced use in Semantic Virtual Observatories dedicated to Sun-Earth Connections. This analysis was carried out in the framework of the Electronic Geophysical Year (eGY) and represents an achievement synergized by the eGY Virtual Observatories Working Group.

A Model Based Framework for Semantic Interpretation of Architectural Construction Drawings

ERIC Educational Resources Information Center

Babalola, Olubi Oluyomi

2011-01-01

The study addresses the automated translation of architectural drawings from 2D Computer Aided Drafting (CAD) data into a Building Information Model (BIM), with emphasis on the nature, possible role, and limitations of a drafting language Knowledge Representation (KR) on the problem and process. The central idea is that CAD to BIM translation is a…

Considerations of Multicultural Science and Curriculum Reform: A Content Analysis of State-Adopted Biology Textbooks in Florida

ERIC Educational Resources Information Center

Delgato, Margaret H.

2009-01-01

The purpose of this investigation was to determine the extent to which multicultural science education, including indigenous knowledge representations, had been infused within the content of high school biology textbooks. The study evaluated the textbook as an instructional tool and framework for multicultural science education instruction by…

Representation of illness in Familial Amyloidotic Polyneuropathy Portuguese Association newspaper: A documental study.

PubMed

Novais, Sónia Alexandra de Lemos; Mendes, Felismina Rosa Parreira

2016-03-01

This study explores illness representations within Familial Amyloidotic Polyneuropathy Portuguese Association newspaper . A content analysis was performed of the issue data using provisional coding related to the conceptual framework of the study. All dimensions of illness representation in Leventhal's Common Sense Model of illness cognitions and behaviors are present in the data and reflect the experience of living with this disease. Understanding how a person living with an hereditary, rare, neurodegenerative illness is important for developing community nursing interventions. In conclusion, we suggest an integration of common sense knowledge with other approaches for designing an intervention program centered on people living with an hereditary neurodegenerative illness, such as familial amyloidotic polyneuropathy. © 2015 Wiley Publishing Asia Pty Ltd.

Standard model of knowledge representation

NASA Astrophysics Data System (ADS)

Yin, Wensheng

2016-09-01

Knowledge representation is the core of artificial intelligence research. Knowledge representation methods include predicate logic, semantic network, computer programming language, database, mathematical model, graphics language, natural language, etc. To establish the intrinsic link between various knowledge representation methods, a unified knowledge representation model is necessary. According to ontology, system theory, and control theory, a standard model of knowledge representation that reflects the change of the objective world is proposed. The model is composed of input, processing, and output. This knowledge representation method is not a contradiction to the traditional knowledge representation method. It can express knowledge in terms of multivariate and multidimensional. It can also express process knowledge, and at the same time, it has a strong ability to solve problems. In addition, the standard model of knowledge representation provides a way to solve problems of non-precision and inconsistent knowledge.

Model Based Analysis and Test Generation for Flight Software

NASA Technical Reports Server (NTRS)

Pasareanu, Corina S.; Schumann, Johann M.; Mehlitz, Peter C.; Lowry, Mike R.; Karsai, Gabor; Nine, Harmon; Neema, Sandeep

2009-01-01

We describe a framework for model-based analysis and test case generation in the context of a heterogeneous model-based development paradigm that uses and combines Math- Works and UML 2.0 models and the associated code generation tools. This paradigm poses novel challenges to analysis and test case generation that, to the best of our knowledge, have not been addressed before. The framework is based on a common intermediate representation for different modeling formalisms and leverages and extends model checking and symbolic execution tools for model analysis and test case generation, respectively. We discuss the application of our framework to software models for a NASA flight mission.

Ontological Representation of Light Wave Camera Data to Support Vision-Based AmI

PubMed Central

Serrano, Miguel Ángel; Gómez-Romero, Juan; Patricio, Miguel Ángel; García, Jesús; Molina, José Manuel

2012-01-01

Recent advances in technologies for capturing video data have opened a vast amount of new application areas in visual sensor networks. Among them, the incorporation of light wave cameras on Ambient Intelligence (AmI) environments provides more accurate tracking capabilities for activity recognition. Although the performance of tracking algorithms has quickly improved, symbolic models used to represent the resulting knowledge have not yet been adapted to smart environments. This lack of representation does not allow to take advantage of the semantic quality of the information provided by new sensors. This paper advocates for the introduction of a part-based representational level in cognitive-based systems in order to accurately represent the novel sensors' knowledge. The paper also reviews the theoretical and practical issues in part-whole relationships proposing a specific taxonomy for computer vision approaches. General part-based patterns for human body and transitive part-based representation and inference are incorporated to an ontology-based previous framework to enhance scene interpretation in the area of video-based AmI. The advantages and new features of the model are demonstrated in a Social Signal Processing (SSP) application for the elaboration of live market researches.

Computable visually observed phenotype ontological framework for plants

PubMed Central

2011-01-01

Background The ability to search for and precisely compare similar phenotypic appearances within and across species has vast potential in plant science and genetic research. The difficulty in doing so lies in the fact that many visual phenotypic data, especially visually observed phenotypes that often times cannot be directly measured quantitatively, are in the form of text annotations, and these descriptions are plagued by semantic ambiguity, heterogeneity, and low granularity. Though several bio-ontologies have been developed to standardize phenotypic (and genotypic) information and permit comparisons across species, these semantic issues persist and prevent precise analysis and retrieval of information. A framework suitable for the modeling and analysis of precise computable representations of such phenotypic appearances is needed. Results We have developed a new framework called the Computable Visually Observed Phenotype Ontological Framework for plants. This work provides a novel quantitative view of descriptions of plant phenotypes that leverages existing bio-ontologies and utilizes a computational approach to capture and represent domain knowledge in a machine-interpretable form. This is accomplished by means of a robust and accurate semantic mapping module that automatically maps high-level semantics to low-level measurements computed from phenotype imagery. The framework was applied to two different plant species with semantic rules mined and an ontology constructed. Rule quality was evaluated and showed high quality rules for most semantics. This framework also facilitates automatic annotation of phenotype images and can be adopted by different plant communities to aid in their research. Conclusions The Computable Visually Observed Phenotype Ontological Framework for plants has been developed for more efficient and accurate management of visually observed phenotypes, which play a significant role in plant genomics research. The uniqueness of this framework is its ability to bridge the knowledge of informaticians and plant science researchers by translating descriptions of visually observed phenotypes into standardized, machine-understandable representations, thus enabling the development of advanced information retrieval and phenotype annotation analysis tools for the plant science community. PMID:21702966

Template construction grammar: from visual scene description to language comprehension and agrammatism.

PubMed

Barrès, Victor; Lee, Jinyong

2014-01-01

How does the language system coordinate with our visual system to yield flexible integration of linguistic, perceptual, and world-knowledge information when we communicate about the world we perceive? Schema theory is a computational framework that allows the simulation of perceptuo-motor coordination programs on the basis of known brain operating principles such as cooperative computation and distributed processing. We present first its application to a model of language production, SemRep/TCG, which combines a semantic representation of visual scenes (SemRep) with Template Construction Grammar (TCG) as a means to generate verbal descriptions of a scene from its associated SemRep graph. SemRep/TCG combines the neurocomputational framework of schema theory with the representational format of construction grammar in a model linking eye-tracking data to visual scene descriptions. We then offer a conceptual extension of TCG to include language comprehension and address data on the role of both world knowledge and grammatical semantics in the comprehension performances of agrammatic aphasic patients. This extension introduces a distinction between heavy and light semantics. The TCG model of language comprehension offers a computational framework to quantitatively analyze the distributed dynamics of language processes, focusing on the interactions between grammatical, world knowledge, and visual information. In particular, it reveals interesting implications for the understanding of the various patterns of comprehension performances of agrammatic aphasics measured using sentence-picture matching tasks. This new step in the life cycle of the model serves as a basis for exploring the specific challenges that neurolinguistic computational modeling poses to the neuroinformatics community.

An Efficient Framework for Development of Task-Oriented Dialog Systems in a Smart Home Environment.

PubMed

Park, Youngmin; Kang, Sangwoo; Seo, Jungyun

2018-05-16

In recent times, with the increasing interest in conversational agents for smart homes, task-oriented dialog systems are being actively researched. However, most of these studies are focused on the individual modules of such a system, and there is an evident lack of research on a dialog framework that can integrate and manage the entire dialog system. Therefore, in this study, we propose a framework that enables the user to effectively develop an intelligent dialog system. The proposed framework ontologically expresses the knowledge required for the task-oriented dialog system's process and can build a dialog system by editing the dialog knowledge. In addition, the framework provides a module router that can indirectly run externally developed modules. Further, it enables a more intelligent conversation by providing a hierarchical argument structure (HAS) to manage the various argument representations included in natural language sentences. To verify the practicality of the framework, an experiment was conducted in which developers without any previous experience in developing a dialog system developed task-oriented dialog systems using the proposed framework. The experimental results show that even beginner dialog system developers can develop a high-level task-oriented dialog system.

An Efficient Framework for Development of Task-Oriented Dialog Systems in a Smart Home Environment

PubMed Central

Park, Youngmin; Kang, Sangwoo; Seo, Jungyun

2018-01-01

In recent times, with the increasing interest in conversational agents for smart homes, task-oriented dialog systems are being actively researched. However, most of these studies are focused on the individual modules of such a system, and there is an evident lack of research on a dialog framework that can integrate and manage the entire dialog system. Therefore, in this study, we propose a framework that enables the user to effectively develop an intelligent dialog system. The proposed framework ontologically expresses the knowledge required for the task-oriented dialog system’s process and can build a dialog system by editing the dialog knowledge. In addition, the framework provides a module router that can indirectly run externally developed modules. Further, it enables a more intelligent conversation by providing a hierarchical argument structure (HAS) to manage the various argument representations included in natural language sentences. To verify the practicality of the framework, an experiment was conducted in which developers without any previous experience in developing a dialog system developed task-oriented dialog systems using the proposed framework. The experimental results show that even beginner dialog system developers can develop a high-level task-oriented dialog system. PMID:29772668

Automatic event recognition and anomaly detection with attribute grammar by learning scene semantics

NASA Astrophysics Data System (ADS)

Qi, Lin; Yao, Zhenyu; Li, Li; Dong, Junyu

2007-11-01

In this paper we present a novel framework for automatic event recognition and abnormal behavior detection with attribute grammar by learning scene semantics. This framework combines learning scene semantics by trajectory analysis and constructing attribute grammar-based event representation. The scene and event information is learned automatically. Abnormal behaviors that disobey scene semantics or event grammars rules are detected. By this method, an approach to understanding video scenes is achieved. Further more, with this prior knowledge, the accuracy of abnormal event detection is increased.

Using Bourdieu’s Theoretical Framework to Examine How the Pharmacy Educator Views Pharmacy Knowledge

PubMed Central

2015-01-01

Objective. To explore how different pharmacy educators view pharmacy knowledge within the United Kingdom MPharm program and to relate these findings to Pierre Bourdieu’s theoretical framework. Methods. Twelve qualitative interviews were conducted with 4 faculty members from 3 different types of schools of pharmacy in the United Kingdom: a newer school, an established teaching-based school, and an established research-intensive school. Selection was based on a representation of both science-based and practice-based disciplines, gender balance, and teaching experience. Results. The interview transcripts indicated how these members of the academic community describe knowledge. There was a polarization between science-based and practice-based educators in terms of Bourdieu’s description of field, species of capital, and habitus. Conclusion. A Bourdieusian perspective on the differences among faculty member responses supports our understanding of curriculum integration and offers some practical implications for the future development of pharmacy programs. PMID:26889065

Using Bourdieu's Theoretical Framework to Examine How the Pharmacy Educator Views Pharmacy Knowledge.

PubMed

Waterfield, Jon

2015-12-25

To explore how different pharmacy educators view pharmacy knowledge within the United Kingdom MPharm program and to relate these findings to Pierre Bourdieu's theoretical framework. Twelve qualitative interviews were conducted with 4 faculty members from 3 different types of schools of pharmacy in the United Kingdom: a newer school, an established teaching-based school, and an established research-intensive school. Selection was based on a representation of both science-based and practice-based disciplines, gender balance, and teaching experience. The interview transcripts indicated how these members of the academic community describe knowledge. There was a polarization between science-based and practice-based educators in terms of Bourdieu's description of field, species of capital, and habitus. A Bourdieusian perspective on the differences among faculty member responses supports our understanding of curriculum integration and offers some practical implications for the future development of pharmacy programs.

Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Utilization of Statistical Data and Domain Knowledge in Complex Cases.

PubMed

Zhang, Qin; Yao, Quanying

2018-05-01

The dynamic uncertain causality graph (DUCG) is a newly presented framework for uncertain causality representation and probabilistic reasoning. It has been successfully applied to online fault diagnoses of large, complex industrial systems, and decease diagnoses. This paper extends the DUCG to model more complex cases than what could be previously modeled, e.g., the case in which statistical data are in different groups with or without overlap, and some domain knowledge and actions (new variables with uncertain causalities) are introduced. In other words, this paper proposes to use -mode, -mode, and -mode of the DUCG to model such complex cases and then transform them into either the standard -mode or the standard -mode. In the former situation, if no directed cyclic graph is involved, the transformed result is simply a Bayesian network (BN), and existing inference methods for BNs can be applied. In the latter situation, an inference method based on the DUCG is proposed. Examples are provided to illustrate the methodology.

LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition.

PubMed

Katić, Darko; Julliard, Chantal; Wekerle, Anna-Laura; Kenngott, Hannes; Müller-Stich, Beat Peter; Dillmann, Rüdiger; Speidel, Stefanie; Jannin, Pierre; Gibaud, Bernard

2015-09-01

The rise of intraoperative information threatens to outpace our abilities to process it. Context-aware systems, filtering information to automatically adapt to the current needs of the surgeon, are necessary to fully profit from computerized surgery. To attain context awareness, representation of medical knowledge is crucial. However, most existing systems do not represent knowledge in a reusable way, hindering also reuse of data. Our purpose is therefore to make our computational models of medical knowledge sharable, extensible and interoperational with established knowledge representations in the form of the LapOntoSPM ontology. To show its usefulness, we apply it to situation interpretation, i.e., the recognition of surgical phases based on surgical activities. Considering best practices in ontology engineering and building on our ontology for laparoscopy, we formalized the workflow of laparoscopic adrenalectomies, cholecystectomies and pancreatic resections in the framework of OntoSPM, a new standard for surgical process models. Furthermore, we provide a rule-based situation interpretation algorithm based on SQWRL to recognize surgical phases using the ontology. The system was evaluated on ground-truth data from 19 manually annotated surgeries. The aim was to show that the phase recognition capabilities are equal to a specialized solution. The recognition rates of the new system were equal to the specialized one. However, the time needed to interpret a situation rose from 0.5 to 1.8 s on average which is still viable for practical application. We successfully integrated medical knowledge for laparoscopic surgeries into OntoSPM, facilitating knowledge and data sharing. This is especially important for reproducibility of results and unbiased comparison of recognition algorithms. The associated recognition algorithm was adapted to the new representation without any loss of classification power. The work is an important step to standardized knowledge and data representation in the field on context awareness and thus toward unified benchmark data sets.

Bayes Nets and Babies: Infants' Developing Statistical Reasoning Abilities and Their Representation of Causal Knowledge

ERIC Educational Resources Information Center

Sobel, David M.; Kirkham, Natasha Z.

2007-01-01

A fundamental assumption of the causal graphical model framework is the Markov assumption, which posits that learners can discriminate between two events that are dependent because of a direct causal relation between them and two events that are independent conditional on the value of another event(s). Sobel and Kirkham (2006) demonstrated that…

From Content Knowledge to Community Change: A Review of Representations of Environmental Health Literacy

PubMed Central

Gray, Kathleen M.

2018-01-01

Environmental health literacy (EHL) is a relatively new framework for conceptualizing how people understand and use information about potentially harmful environmental exposures and their influence on health. As such, information on the characterization and measurement of EHL is limited. This review provides an overview of EHL as presented in peer-reviewed literature and aggregates studies based on whether they represent individual level EHL or community level EHL or both. A range of assessment tools has been used to measure EHL, with many studies relying on pre-/post-assessment; however, a broader suite of assessment tools may be needed to capture community-wide outcomes. This review also suggests that the definition of EHL should explicitly include community change or collective action as an important longer-term outcome and proposes a refinement of previous representations of EHL as a theoretical framework, to include self-efficacy. PMID:29518955

Meaningful main effects or intriguing interactions? Examining the influences of epistemic beliefs and knowledge representations on cognitive processing and conceptual change when learning physics

NASA Astrophysics Data System (ADS)

Franco, Gina M.

The purpose of this study was to investigate the role of epistemic beliefs and knowledge representations in cognitive and metacognitive processing and conceptual change when learning about physics concepts through text. Specifically, I manipulated the representation of physics concepts in texts about Newtonian mechanics and explored how these texts interacted with individuals' epistemic beliefs to facilitate or constrain learning. In accordance with definitions from Royce's (1983) framework of psychological epistemology, texts were developed to present Newtonian concepts in either a rational or a metaphorical format. Seventy-five undergraduate students completed questionnaires designed to measure their epistemic beliefs and their misconceptions about Newton's laws of motion. Participants then read the first of two instructional texts (in either a rational or metaphorical format), and were asked to think aloud while reading. After reading the text, participants completed a recall task and a post-test of selected items regarding Newtonian concepts. These steps were repeated with a second instructional text (in either a rational or metaphorical format, depending on which format was assigned previously). Participants' think-aloud sessions were audio-recorded, transcribed, and then blindly coded, and their recalls were scored for total number of correctly recalled ideas from the text. Changes in misconceptions were analyzed by examining changes in participants' responses to selected questions about Newtonian concepts from pretest to posttest. Results revealed that when individuals' epistemic beliefs were congruent with the knowledge representations in their assigned texts, they performed better on both online measures of learning (e.g., use of processing strategies) and offline products of learning (e.g., text recall, changes in misconceptions) than when their epistemic beliefs were incongruent with the knowledge representations. These results have implications for how researchers conceptualize epistemic beliefs and are in line with contemporary views regarding the context sensitivity of individuals' epistemic beliefs. Moreover, the findings from this study not only support current theory about the dynamic and interactive nature of conceptual change, but also advance empirical work in this area by identifying knowledge representations as a text characteristic that may play an important role in the change process.

Teaching Equivalent Fractions to Secondary Students with Disabilities via the Virtual-Representational-Abstract Instructional Sequence

ERIC Educational Resources Information Center

Bouck, Emily C.; Bassette, Laura; Shurr, Jordan; Park, Jiyoon; Kerr, Jackie; Whorley, Abbie

2017-01-01

Fractions are an important mathematical concept; however, fractions are also a struggle for many students with disabilities. This study explored a new framework adapted from the evidence-based concrete-representational-abstract framework: the virtual-representational-abstract (VRA) framework. The VRA framework involves teaching students to solve…

Taxonomy development and knowledge representation of nurses' personal cognitive artifacts.

PubMed

McLane, Sharon; Turley, James P

2009-11-14

Nurses prepare knowledge representations, or summaries of patient clinical data, each shift. These knowledge representations serve multiple purposes, including support of working memory, workload organization and prioritization, critical thinking, and reflection. This summary is integral to internal knowledge representations, working memory, and decision-making. Study of this nurse knowledge representation resulted in development of a taxonomy of knowledge representations necessary to nursing practice.This paper describes the methods used to elicit the knowledge representations and structures necessary for the work of clinical nurses, described the development of a taxonomy of this knowledge representation, and discusses translation of this methodology to the cognitive artifacts of other disciplines. Understanding the development and purpose of practitioner's knowledge representations provides important direction to informaticists seeking to create information technology alternatives. The outcome of this paper is to suggest a process template for transition of cognitive artifacts to an information system.

Knowledge Acquisition of Generic Queries for Information Retrieval

PubMed Central

Seol, Yoon-Ho; Johnson, Stephen B.; Cimino, James J.

2002-01-01

Several studies have identified clinical questions posed by health care professionals to understand the nature of information needs during clinical practice. To support access to digital information sources, it is necessary to integrate the information needs with a computer system. We have developed a conceptual guidance approach in information retrieval, based on a knowledge base that contains the patterns of information needs. The knowledge base uses a formal representation of clinical questions based on the UMLS knowledge sources, called the Generic Query model. To improve the coverage of the knowledge base, we investigated a method for extracting plausible clinical questions from the medical literature. This poster presents the Generic Query model, shows how it is used to represent the patterns of clinical questions, and describes the framework used to extract knowledge from the medical literature.

Predit: A temporal predictive framework for scheduling systems

NASA Technical Reports Server (NTRS)

Paolucci, E.; Patriarca, E.; Sem, M.; Gini, G.

1992-01-01

Scheduling can be formalized as a Constraint Satisfaction Problem (CSP). Within this framework activities belonging to a plan are interconnected via temporal constraints that account for slack among them. Temporal representation must include methods for constraints propagation and provide a logic for symbolic and numerical deductions. In this paper we describe a support framework for opportunistic reasoning in constraint directed scheduling. In order to focus the attention of an incremental scheduler on critical problem aspects, some discrete temporal indexes are presented. They are also useful for the prediction of the degree of resources contention. The predictive method expressed through our indexes can be seen as a Knowledge Source for an opportunistic scheduler with a blackboard architecture.

Addressing the translational dilemma: dynamic knowledge representation of inflammation using agent-based modeling.

PubMed

An, Gary; Christley, Scott

2012-01-01

Given the panoply of system-level diseases that result from disordered inflammation, such as sepsis, atherosclerosis, cancer, and autoimmune disorders, understanding and characterizing the inflammatory response is a key target of biomedical research. Untangling the complex behavioral configurations associated with a process as ubiquitous as inflammation represents a prototype of the translational dilemma: the ability to translate mechanistic knowledge into effective therapeutics. A critical failure point in the current research environment is a throughput bottleneck at the level of evaluating hypotheses of mechanistic causality; these hypotheses represent the key step toward the application of knowledge for therapy development and design. Addressing the translational dilemma will require utilizing the ever-increasing power of computers and computational modeling to increase the efficiency of the scientific method in the identification and evaluation of hypotheses of mechanistic causality. More specifically, development needs to focus on facilitating the ability of non-computer trained biomedical researchers to utilize and instantiate their knowledge in dynamic computational models. This is termed "dynamic knowledge representation." Agent-based modeling is an object-oriented, discrete-event, rule-based simulation method that is well suited for biomedical dynamic knowledge representation. Agent-based modeling has been used in the study of inflammation at multiple scales. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggest that this modeling framework is well suited for addressing the translational dilemma. This review describes agent-based modeling, gives examples of its applications in the study of inflammation, and introduces a proposed general expansion of the use of modeling and simulation to augment the generation and evaluation of knowledge by the biomedical research community at large.

Probability and possibility-based representations of uncertainty in fault tree analysis.

PubMed

Flage, Roger; Baraldi, Piero; Zio, Enrico; Aven, Terje

2013-01-01

Expert knowledge is an important source of input to risk analysis. In practice, experts might be reluctant to characterize their knowledge and the related (epistemic) uncertainty using precise probabilities. The theory of possibility allows for imprecision in probability assignments. The associated possibilistic representation of epistemic uncertainty can be combined with, and transformed into, a probabilistic representation; in this article, we show this with reference to a simple fault tree analysis. We apply an integrated (hybrid) probabilistic-possibilistic computational framework for the joint propagation of the epistemic uncertainty on the values of the (limiting relative frequency) probabilities of the basic events of the fault tree, and we use possibility-probability (probability-possibility) transformations for propagating the epistemic uncertainty within purely probabilistic and possibilistic settings. The results of the different approaches (hybrid, probabilistic, and possibilistic) are compared with respect to the representation of uncertainty about the top event (limiting relative frequency) probability. Both the rationale underpinning the approaches and the computational efforts they require are critically examined. We conclude that the approaches relevant in a given setting depend on the purpose of the risk analysis, and that further research is required to make the possibilistic approaches operational in a risk analysis context. © 2012 Society for Risk Analysis.

Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge

PubMed Central

2014-01-01

Background Combining different sources of knowledge to build improved structure activity relationship models is not easy owing to the variety of knowledge formats and the absence of a common framework to interoperate between learning techniques. Most of the current approaches address this problem by using consensus models that operate at the prediction level. We explore the possibility to directly combine these sources at the knowledge level, with the aim to harvest potentially increased synergy at an earlier stage. Our goal is to design a general methodology to facilitate knowledge discovery and produce accurate and interpretable models. Results To combine models at the knowledge level, we propose to decouple the learning phase from the knowledge application phase using a pivot representation (lingua franca) based on the concept of hypothesis. A hypothesis is a simple and interpretable knowledge unit. Regardless of its origin, knowledge is broken down into a collection of hypotheses. These hypotheses are subsequently organised into hierarchical network. This unification permits to combine different sources of knowledge into a common formalised framework. The approach allows us to create a synergistic system between different forms of knowledge and new algorithms can be applied to leverage this unified model. This first article focuses on the general principle of the Self Organising Hypothesis Network (SOHN) approach in the context of binary classification problems along with an illustrative application to the prediction of mutagenicity. Conclusion It is possible to represent knowledge in the unified form of a hypothesis network allowing interpretable predictions with performances comparable to mainstream machine learning techniques. This new approach offers the potential to combine knowledge from different sources into a common framework in which high level reasoning and meta-learning can be applied; these latter perspectives will be explored in future work. PMID:24959206

Core Themes in Music Therapy Clinical Improvisation: An Arts-Informed Qualitative Research Synthesis.

PubMed

Meadows, Anthony; Wimpenny, Katherine

2017-07-01

Although clinical improvisation continues to be an important focus of music therapy research and practice, less attention has been given to integrating qualitative research in this area. As a result, this knowledge base tends to be contained within specific areas of practice rather than integrated across practices and approaches. This qualitative research synthesis profiles, integrates, and re-presents qualitative research focused on the ways music therapists and clients engage in, and make meaning from, clinical improvisation. Further, as a conduit for broadening dialogues, opening up this landscape fully, and sharing our response to the analysis and interpretation process, we present an arts-informed re-presentation of this synthesis. Following an eight-step methodological sequence, 13 qualitative studies were synthesized. This included reciprocal and refutational processes associated with synthesizing the primary studies, and additional steps associated with an arts-informed representation. Three themes, professional artistry, performing self, and meaning-making, are presented. Each theme is explored and exemplified through the selected articles, and discussed within a larger theoretical framework. An artistic re-presentation of the data is also presented. Music therapists use complex frameworks through which to engage clients in, and make meaning from, improvisational experiences. Artistic representation of the findings offers an added dimension to the synthesis process, challenging our understanding of representation, and thereby advancing synthesis methodology. © the American Music Therapy Association 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Organization and integration of biomedical knowledge with concept maps for key peroxisomal pathways.

PubMed

Willemsen, A M; Jansen, G A; Komen, J C; van Hooff, S; Waterham, H R; Brites, P M T; Wanders, R J A; van Kampen, A H C

2008-08-15

One important area of clinical genomics research involves the elucidation of molecular mechanisms underlying (complex) disorders which eventually may lead to new diagnostic or drug targets. To further advance this area of clinical genomics one of the main challenges is the acquisition and integration of data, information and expert knowledge for specific biomedical domains and diseases. Currently the required information is not very well organized but scattered over biological and biomedical databases, basic text books, scientific literature and experts' minds and may be highly specific, heterogeneous, complex and voluminous. We present a new framework to construct knowledge bases with concept maps for presentation of information and the web ontology language OWL for the representation of information. We demonstrate this framework through the construction of a peroxisomal knowledge base, which focuses on four key peroxisomal pathways and several related genetic disorders. All 155 concept maps in our knowledge base are linked to at least one other concept map, which allows the visualization of one big network of related pieces of information. The peroxisome knowledge base is available from www.bioinformaticslaboratory.nl (Support-->Web applications). Supplementary data is available from www.bioinformaticslaboratory.nl (Research-->Output--> Publications--> KB_SuppInfo)

Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

PubMed Central

Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C.

2015-01-01

Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data,, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked auto-encoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework image registration experiments were conducted on 7.0-tesla brain MR images. In all experiments, the results showed the new image registration framework consistently demonstrated more accurate registration results when compared to state-of-the-art. PMID:26552069

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning.

PubMed

Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C; Shen, Dinggang

2016-07-01

Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked autoencoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework, image registration experiments were conducted on 7.0-T brain MR images. In all experiments, the results showed that the new image registration framework consistently demonstrated more accurate registration results when compared to state of the art.

The Delta Connectome: A network-based framework for studying connectivity in river deltas

NASA Astrophysics Data System (ADS)

Passalacqua, Paola

2017-01-01

Many deltas, including the Mississippi River Delta, have been losing land at fast rates compromising the safety and sustainability of their ecosystems. Knowledge of delta vulnerability has raised global concern and stimulated active interdisciplinary research as deltas are densely populated landscapes, rich in agriculture, fisheries, oil and gas, and important means for navigation. There are many ways of looking at this problem which all contribute to a deeper understanding of the functioning of coastal systems. One aspect that has been overlooked thus far, yet fundamental for advancing delta science is connectivity, both physical (how different portions of the system interact with each other) as well as conceptual (pathways of process coupling). In this paper, I propose a framework called Delta Connectome for studying connectivity in river deltas based on different representations of a delta as a network. After analyzing the classic network representation as a set of nodes (e.g., bifurcations and junctions or regions with distinct physical or statistical behavior) and links (e.g., channels), I show that from connectivity considerations the delta emerges as a leaky network that continuously exchanges fluxes of matter, energy, and information with its surroundings and evolves over time. I explore each network representation and show through several examples how quantifying connectivity can bring to light aspects of deltaic systems so far unexplored and yet fundamental to understanding system functioning and informing coastal management and restoration. This paper serves both as an introduction to the Delta Connectome framework as well as a review of recent applications of the concepts of network and connectivity to deltaic systems within the Connectome framework.

Conceptual Model-Based Systems Biology: Mapping Knowledge and Discovering Gaps in the mRNA Transcription Cycle

PubMed Central

Somekh, Judith; Choder, Mordechai; Dori, Dov

2012-01-01

We propose a Conceptual Model-based Systems Biology framework for qualitative modeling, executing, and eliciting knowledge gaps in molecular biology systems. The framework is an adaptation of Object-Process Methodology (OPM), a graphical and textual executable modeling language. OPM enables concurrent representation of the system's structure—the objects that comprise the system, and behavior—how processes transform objects over time. Applying a top-down approach of recursively zooming into processes, we model a case in point—the mRNA transcription cycle. Starting with this high level cell function, we model increasingly detailed processes along with participating objects. Our modeling approach is capable of modeling molecular processes such as complex formation, localization and trafficking, molecular binding, enzymatic stimulation, and environmental intervention. At the lowest level, similar to the Gene Ontology, all biological processes boil down to three basic molecular functions: catalysis, binding/dissociation, and transporting. During modeling and execution of the mRNA transcription model, we discovered knowledge gaps, which we present and classify into various types. We also show how model execution enhances a coherent model construction. Identification and pinpointing knowledge gaps is an important feature of the framework, as it suggests where research should focus and whether conjectures about uncertain mechanisms fit into the already verified model. PMID:23308089

Towards a Semantic Web of Things: A Hybrid Semantic Annotation, Extraction, and Reasoning Framework for Cyber-Physical System.

PubMed

Wu, Zhenyu; Xu, Yuan; Yang, Yunong; Zhang, Chunhong; Zhu, Xinning; Ji, Yang

2017-02-20

Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability. In this paper, to addresses these limitations, we propose the semantic Web of Things (SWoT) framework for CPS (SWoT4CPS). SWoT4CPS provides a hybrid solution with both ontological engineering methods by extending SSN and machine learning methods based on an entity linking (EL) model. To testify to the feasibility and performance, we demonstrate the framework by implementing a temperature anomaly diagnosis and automatic control use case in a building automation system. Evaluation results on the EL method show that linking domain knowledge to DBpedia has a relative high accuracy and the time complexity is at a tolerant level. Advantages and disadvantages of SWoT4CPS with future work are also discussed.

Incorporating linguistic knowledge for learning distributed word representations.

PubMed

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining.

Incorporating Linguistic Knowledge for Learning Distributed Word Representations

PubMed Central

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining. PMID:25874581

Knowledge synthesis with maps of neural connectivity.

PubMed

Tallis, Marcelo; Thompson, Richard; Russ, Thomas A; Burns, Gully A P C

2011-01-01

This paper describes software for neuroanatomical knowledge synthesis based on neural connectivity data. This software supports a mature methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus, and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macro connections using the Swanson third edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the data mapping components within a unified web-application. As a step toward developing an accurate sub-regional account of neural connectivity, we provide navigational access between the data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called "Knowledge Engineering from Experimental Design" (KEfED) model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web-application that allows anatomical data sets to be described within a standard experimental context and thus indexed by non-spatial experimental design features.

A Canadian perspective on documentary film: Drug Addict.

PubMed

Boyd, Susan

2013-11-01

In 1948 the first National Film Board (NFB) documentary in Canada about illegal drugs, trafficking, and addiction was produced. The documentary is titled Drug Addict, and was directed by Robert Anderson. This paper provides a socio-historical context for the documentary Drug Addict. Viewing the film through the lens of Canadian history gives readers a better context to understand the claims and representations in the film about law enforcement, people who use illegal drugs and treatment. To examine Drug Addict, a socio-historical analysis and case study were conducted. This project's qualitative methodological framework is consistent with its critical theoretical perspective, drawing from Stuart Hall's perspectives on visual and textual representation and cultural criminology. Drug Addict is a significant documentary because it provides insight into early foundational law enforcement discourses and practices about illegal drugs, addiction, and treatment, including obstacles to drug substitution and maintenance programs. It also highlights the emergence of psychiatry as a new knowledge producer in the area of drug treatment. The film also transmits ideas about the criminal nature of addicts and the need for punitive criminal justice control. Drug Addict captures some past and contemporary tensions related to Canadian drug policy. The film also provides another lens to understand some of the foundational frameworks of Canadian drug policy such as the dominance of criminal justice, and its practices of knowledge production, the resistance espoused by institutions to diverse models of treatment such as drug maintenance programs, and the power of visual representation. Copyright © 2013 Elsevier B.V. All rights reserved.

Semantic framework for mapping object-oriented model to semantic web languages

PubMed Central

Ježek, Petr; Mouček, Roman

2015-01-01

The article deals with and discusses two main approaches in building semantic structures for electrophysiological metadata. It is the use of conventional data structures, repositories, and programming languages on one hand and the use of formal representations of ontologies, known from knowledge representation, such as description logics or semantic web languages on the other hand. Although knowledge engineering offers languages supporting richer semantic means of expression and technological advanced approaches, conventional data structures and repositories are still popular among developers, administrators and users because of their simplicity, overall intelligibility, and lower demands on technical equipment. The choice of conventional data resources and repositories, however, raises the question of how and where to add semantics that cannot be naturally expressed using them. As one of the possible solutions, this semantics can be added into the structures of the programming language that accesses and processes the underlying data. To support this idea we introduced a software prototype that enables its users to add semantically richer expressions into a Java object-oriented code. This approach does not burden users with additional demands on programming environment since reflective Java annotations were used as an entry for these expressions. Moreover, additional semantics need not to be written by the programmer directly to the code, but it can be collected from non-programmers using a graphic user interface. The mapping that allows the transformation of the semantically enriched Java code into the Semantic Web language OWL was proposed and implemented in a library named the Semantic Framework. This approach was validated by the integration of the Semantic Framework in the EEG/ERP Portal and by the subsequent registration of the EEG/ERP Portal in the Neuroscience Information Framework. PMID:25762923

A generic biogeochemical module for earth system models

NASA Astrophysics Data System (ADS)

Fang, Y.; Huang, M.; Liu, C.; Li, H.-Y.; Leung, L. R.

2013-06-01

Physical and biogeochemical processes regulate soil carbon dynamics and CO2 flux to and from the atmosphere, influencing global climate changes. Integration of these processes into earth system models (e.g. community land models - CLM), however, currently faces three major challenges: (1) extensive efforts are required to modify modeling structures and to rewrite computer programs to incorporate new or updated processes as new knowledge is being generated, (2) computational cost is prohibitively expensive to simulate biogeochemical processes in land models due to large variations in the rates of biogeochemical processes, and (3) various mathematical representations of biogeochemical processes exist to incorporate different aspects of fundamental mechanisms, but systematic evaluation of the different mathematical representations is difficult, if not impossible. To address these challenges, we propose a new computational framework to easily incorporate physical and biogeochemical processes into land models. The new framework consists of a new biogeochemical module with a generic algorithm and reaction database so that new and updated processes can be incorporated into land models without the need to manually set up the ordinary differential equations to be solved numerically. The reaction database consists of processes of nutrient flow through the terrestrial ecosystems in plants, litter and soil. This framework facilitates effective comparison studies of biogeochemical cycles in an ecosystem using different conceptual models under the same land modeling framework. The approach was first implemented in CLM and benchmarked against simulations from the original CLM-CN code. A case study was then provided to demonstrate the advantages of using the new approach to incorporate a phosphorus cycle into the CLM model. To our knowledge, the phosphorus-incorporated CLM is a new model that can be used to simulate phosphorus limitation on the productivity of terrestrial ecosystems.

Semantic framework for mapping object-oriented model to semantic web languages.

PubMed

Ježek, Petr; Mouček, Roman

2015-01-01

The article deals with and discusses two main approaches in building semantic structures for electrophysiological metadata. It is the use of conventional data structures, repositories, and programming languages on one hand and the use of formal representations of ontologies, known from knowledge representation, such as description logics or semantic web languages on the other hand. Although knowledge engineering offers languages supporting richer semantic means of expression and technological advanced approaches, conventional data structures and repositories are still popular among developers, administrators and users because of their simplicity, overall intelligibility, and lower demands on technical equipment. The choice of conventional data resources and repositories, however, raises the question of how and where to add semantics that cannot be naturally expressed using them. As one of the possible solutions, this semantics can be added into the structures of the programming language that accesses and processes the underlying data. To support this idea we introduced a software prototype that enables its users to add semantically richer expressions into a Java object-oriented code. This approach does not burden users with additional demands on programming environment since reflective Java annotations were used as an entry for these expressions. Moreover, additional semantics need not to be written by the programmer directly to the code, but it can be collected from non-programmers using a graphic user interface. The mapping that allows the transformation of the semantically enriched Java code into the Semantic Web language OWL was proposed and implemented in a library named the Semantic Framework. This approach was validated by the integration of the Semantic Framework in the EEG/ERP Portal and by the subsequent registration of the EEG/ERP Portal in the Neuroscience Information Framework.

What kind of computation is intelligence. A framework for integrating different kinds of expertise

NASA Technical Reports Server (NTRS)

Chandrasekaran, B.

1989-01-01

The view that the deliberative aspect of intelligent behavior is a distinct type of algorithm; in particular, a goal-seeking exploratory process using qualitative representations of knowledge and inference is elaborated. There are other kinds of algorithms that also embody expertise in domains. The different types of expertise and how they can and should be integrated to give full account of expert behavior are discussed.

SPARK: A Framework for Multi-Scale Agent-Based Biomedical Modeling.

PubMed

Solovyev, Alexey; Mikheev, Maxim; Zhou, Leming; Dutta-Moscato, Joyeeta; Ziraldo, Cordelia; An, Gary; Vodovotz, Yoram; Mi, Qi

2010-01-01

Multi-scale modeling of complex biological systems remains a central challenge in the systems biology community. A method of dynamic knowledge representation known as agent-based modeling enables the study of higher level behavior emerging from discrete events performed by individual components. With the advancement of computer technology, agent-based modeling has emerged as an innovative technique to model the complexities of systems biology. In this work, the authors describe SPARK (Simple Platform for Agent-based Representation of Knowledge), a framework for agent-based modeling specifically designed for systems-level biomedical model development. SPARK is a stand-alone application written in Java. It provides a user-friendly interface, and a simple programming language for developing Agent-Based Models (ABMs). SPARK has the following features specialized for modeling biomedical systems: 1) continuous space that can simulate real physical space; 2) flexible agent size and shape that can represent the relative proportions of various cell types; 3) multiple spaces that can concurrently simulate and visualize multiple scales in biomedical models; 4) a convenient graphical user interface. Existing ABMs of diabetic foot ulcers and acute inflammation were implemented in SPARK. Models of identical complexity were run in both NetLogo and SPARK; the SPARK-based models ran two to three times faster.

Social representations and normative beliefs of aging.

PubMed

Torres, Tatiana de Lucena; Camargo, Brigido Vizeu; Boulsfield, Andréa Barbará; Silva, Antônia Oliveira

2015-12-01

This study adopted the theory of social representations as a theoretical framework in order to characterize similarities and differences in social representations and normative beliefs of aging for different age groups. The 638 participants responded to self-administered questionnaire and were equally distributed by sex and age. The results show that aging is characterized by positive stereotypes (knowledge and experience); however, retirement is linked to aging, but in a negative way, particularly for men, involving illness, loneliness and disability. When age was considered, it was verified that the connections with the representational elements became more complex for older groups, showing social representation functionality, largely for the elderly. Adulthood seems to be preferred and old age is disliked. There were divergences related to the perception of the beginning of life phases, especially that of old age. Work was characterized as the opposite of aging, and it revealed the need for actions intended for the elderly and retired workers, with post-retirement projects. In addition, it suggests investment in public policies that encourage intergenerational contact, with efforts to reduce intolerance and discrimination based on age of people.

Effective domain-dependent reuse in medical knowledge bases.

PubMed

Dojat, M; Pachet, F

1995-12-01

Knowledge reuse is now a critical issue for most developers of medical knowledge-based systems. As a rule, reuse is addressed from an ambitious, knowledge-engineering perspective that focuses on reusable general purpose knowledge modules, concepts, and methods. However, such a general goal fails to take into account the specific aspects of medical practice. From the point of view of the knowledge engineer, whose goal is to capture the specific features and intricacies of a given domain, this approach addresses the wrong level of generality. In this paper, we adopt a more pragmatic viewpoint, introducing the less ambitious goal of "domain-dependent limited reuse" and suggesting effective means of achieving it in practice. In a knowledge representation framework combining objects and production rules, we propose three mechanisms emerging from the combination of object-oriented programming and rule-based programming. We show these mechanisms contribute to achieve limited reuse and to introduce useful limited variations in medical expertise.

The Digital Anatomist Distributed Framework and Its Applications to Knowledge-based Medical Imaging

PubMed Central

Brinkley, James F.; Rosse, Cornelius

1997-01-01

Abstract The domain of medical imaging is anatomy. Therefore, anatomic knowledge should be a rational basis for organizing and analyzing images. The goals of the Digital Anatomist Program at the University of Washington include the development of an anatomically based software framework for organizing, analyzing, visualizing and utilizing biomedical information. The framework is based on representations for both spatial and symbolic anatomic knowledge, and is being implemented in a distributed architecture in which multiple client programs on the Internet are used to update and access an expanding set of anatomical information resources. The development of this framework is driven by several practical applications, including symbolic anatomic reasoning, knowledge based image segmentation, anatomy information retrieval, and functional brain mapping. Since each of these areas involves many difficult image processing issues, our research strategy is an evolutionary one, in which applications are developed somewhat independently, and partial solutions are integrated in a piecemeal fashion, using the network as the substrate. This approach assumes that networks of interacting components can synergistically work together to solve problems larger than either could solve on its own. Each of the individual projects is described, along with evaluations that show that the individual components are solving the problems they were designed for, and are beginning to interact with each other in a synergistic manner. We argue that this synergy will increase, not only within our own group, but also among groups as the Internet matures, and that an anatomic knowledge base will be a useful means for fostering these interactions. PMID:9147337

Structured feedback on students' concept maps: the proverbial path to learning?

PubMed

Joseph, Conran; Conradsson, David; Nilsson Wikmar, Lena; Rowe, Michael

2017-05-25

Good conceptual knowledge is an essential requirement for health professions students, in that they are required to apply concepts learned in the classroom to a variety of different contexts. However, the use of traditional methods of assessment limits the educator's ability to correct students' conceptual knowledge prior to altering the educational context. Concept mapping (CM) is an educational tool for evaluating conceptual knowledge, but little is known about its use in facilitating the development of richer knowledge frameworks. In addition, structured feedback has the potential to develop good conceptual knowledge. The purpose of this study was to use Kinchin's criteria to assess the impact of structured feedback on the graphical complexity of CM's by observing the development of richer knowledge frameworks. Fifty-eight physiotherapy students created CM's targeting the integration of two knowledge domains within a case-based teaching paradigm. Each student received one round of structured feedback that addressed correction, reinforcement, forensic diagnosis, benchmarking, and longitudinal development on their CM's prior to the final submission. The concept maps were categorized according to Kinchin's criteria as either Spoke, Chain or Net representations, and then evaluated against defined traits of meaningful learning. The inter-rater reliability of categorizing CM's was good. Pre-feedback CM's were predominantly Chain structures (57%), with Net structures appearing least often. There was a significant reduction of the basic Spoke- structured CMs (P = 0.002) and a significant increase of Net-structured maps (P < 0.001) at the final evaluation (post-feedback). Changes in structural complexity of CMs appeared to be indicative of broader knowledge frameworks as assessed against the meaningful learning traits. Feedback on CM's seemed to have contributed towards improving conceptual knowledge and correcting naive conceptions of related knowledge. Educators in medical education could therefore consider using CM's to target individual student development.

An ontological knowledge framework for adaptive medical workflow.

PubMed

Dang, Jiangbo; Hedayati, Amir; Hampel, Ken; Toklu, Candemir

2008-10-01

As emerging technologies, semantic Web and SOA (Service-Oriented Architecture) allow BPMS (Business Process Management System) to automate business processes that can be described as services, which in turn can be used to wrap existing enterprise applications. BPMS provides tools and methodologies to compose Web services that can be executed as business processes and monitored by BPM (Business Process Management) consoles. Ontologies are a formal declarative knowledge representation model. It provides a foundation upon which machine understandable knowledge can be obtained, and as a result, it makes machine intelligence possible. Healthcare systems can adopt these technologies to make them ubiquitous, adaptive, and intelligent, and then serve patients better. This paper presents an ontological knowledge framework that covers healthcare domains that a hospital encompasses-from the medical or administrative tasks, to hospital assets, medical insurances, patient records, drugs, and regulations. Therefore, our ontology makes our vision of personalized healthcare possible by capturing all necessary knowledge for a complex personalized healthcare scenario involving patient care, insurance policies, and drug prescriptions, and compliances. For example, our ontology facilitates a workflow management system to allow users, from physicians to administrative assistants, to manage, even create context-aware new medical workflows and execute them on-the-fly.

Evaluation, Use, and Refinement of Knowledge Representations through Acquisition Modeling

ERIC Educational Resources Information Center

Pearl, Lisa

2017-01-01

Generative approaches to language have long recognized the natural link between theories of knowledge representation and theories of knowledge acquisition. The basic idea is that the knowledge representations provided by Universal Grammar enable children to acquire language as reliably as they do because these representations highlight the…

Research in Knowledge Representation for Natural Language Understanding

DTIC Science & Technology

1980-11-01

artificial intelligence, natural language understanding , parsing, syntax, semantics, speaker meaning, knowledge representation, semantic networks...TinB PAGE map M W006 1Report No. 4513 L RESEARCH IN KNOWLEDGE REPRESENTATION FOR NATURAL LANGUAGE UNDERSTANDING Annual Report 1 September 1979 to 31... understanding , knowledge representation, and knowledge based inference. The work that we have been doing falls into three classes, successively motivated by

Learning through Constructing Representations in Science: A Framework of Representational Construction Affordances

ERIC Educational Resources Information Center

Prain, Vaughan; Tytler, Russell

2012-01-01

Compared with research on the role of student engagement with expert representations in learning science, investigation of the use and theoretical justification of student-generated representations to learn science is less common. In this paper, we present a framework that aims to integrate three perspectives to explain how and why…

An Integrative Framework for the Analysis of Multiple and Multimodal Representations for Meaning-Making in Science Education

ERIC Educational Resources Information Center

Tang, Kok-Sing; Delgado, Cesar; Moje, Elizabeth Birr

2014-01-01

This paper presents an integrative framework for analyzing science meaning-making with representations. It integrates the research on multiple representations and multimodal representations by identifying and leveraging the differences in their units of analysis in two dimensions: timescale and compositional grain size. Timescale considers the…

A Complex Prime Numerical Representation of Amino Acids for Protein Function Comparison.

PubMed

Chen, Duo; Wang, Jiasong; Yan, Ming; Bao, Forrest Sheng

2016-08-01

Computationally assessing the functional similarity between proteins is an important task of bioinformatics research. It can help molecular biologists transfer knowledge on certain proteins to others and hence reduce the amount of tedious and costly benchwork. Representation of amino acids, the building blocks of proteins, plays an important role in achieving this goal. Compared with symbolic representation, representing amino acids numerically can expand our ability to analyze proteins, including comparing the functional similarity of them. Among the state-of-the-art methods, electro-ion interaction pseudopotential (EIIP) is widely adopted for the numerical representation of amino acids. However, it could suffer from degeneracy that two different amino acid sequences have the same numerical representation, due to the design of EIIP. In light of this challenge, we propose a complex prime numerical representation (CPNR) of amino acids, inspired by the similarity between a pattern among prime numbers and the number of codons of amino acids. To empirically assess the effectiveness of the proposed method, we compare CPNR against EIIP. Experimental results demonstrate that the proposed method CPNR always achieves better performance than EIIP. We also develop a framework to combine the advantages of CPNR and EIIP, which enables us to improve the performance and study the unique characteristics of different representations.

Formalizing nursing knowledge: from theories and models to ontologies.

PubMed

Peace, Jane; Brennan, Patricia Flatley

2009-01-01

Knowledge representation in nursing is poised to address the depth of nursing knowledge about the specific phenomena of importance to nursing. Nursing theories and models may provide a starting point for making this knowledge explicit in representations. We combined knowledge building methods from nursing and ontology design methods from biomedical informatics to create a nursing representation of family health history. Our experience provides an example of how knowledge representations may be created to facilitate electronic support for nursing practice and knowledge development.

Knowledge Construction and Knowledge Representation in High School Students' Design of Hypermedia Documents

ERIC Educational Resources Information Center

Chen, Pearl; McGrath, Diane

2003-01-01

This study documented the processes of knowledge construction and knowledge representation in high school students' hypermedia design projects. Analysis of knowledge construction in linking and structural building yielded distinct types and subtypes of hypermedia documents, which were characterized by four features of knowledge representation: (a)…

Making effective referrals: a knowledge-management approach.

PubMed Central

Einbinder, J. S.; Klein, D. A.; Safran, C. S.

1997-01-01

Patients and physicians often choose specially consultants with only limited knowledge of the available options. Access to information about specialists that was directly relevant to patient and clinician preferences could improve the effectiveness of the referral process. We have developed a prescriptive representation of the process of selecting consultants. This "referral map," based on decision theory, uses patient and provider preferences elicited through a literature review and interviews with physicians and provides a formal framework for representing referral knowledge and for evaluating referral options. Our method suggests that the goals and processes of selecting consultants can be managed more systematically using explicit repositories. Such systematic management promises to have a beneficial impact on the delivery of health care, as well as on patient satisfaction. PMID:9357642

Knowledge Synthesis with Maps of Neural Connectivity

PubMed Central

Tallis, Marcelo; Thompson, Richard; Russ, Thomas A.; Burns, Gully A. P. C.

2011-01-01

This paper describes software for neuroanatomical knowledge synthesis based on neural connectivity data. This software supports a mature methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus, and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macro connections using the Swanson third edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the data mapping components within a unified web-application. As a step toward developing an accurate sub-regional account of neural connectivity, we provide navigational access between the data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called “Knowledge Engineering from Experimental Design” (KEfED) model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web-application that allows anatomical data sets to be described within a standard experimental context and thus indexed by non-spatial experimental design features. PMID:22053155

Towards a Semantic Web of Things: A Hybrid Semantic Annotation, Extraction, and Reasoning Framework for Cyber-Physical System

PubMed Central

Wu, Zhenyu; Xu, Yuan; Yang, Yunong; Zhang, Chunhong; Zhu, Xinning; Ji, Yang

2017-01-01

Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability. In this paper, to addresses these limitations, we propose the semantic Web of Things (SWoT) framework for CPS (SWoT4CPS). SWoT4CPS provides a hybrid solution with both ontological engineering methods by extending SSN and machine learning methods based on an entity linking (EL) model. To testify to the feasibility and performance, we demonstrate the framework by implementing a temperature anomaly diagnosis and automatic control use case in a building automation system. Evaluation results on the EL method show that linking domain knowledge to DBpedia has a relative high accuracy and the time complexity is at a tolerant level. Advantages and disadvantages of SWoT4CPS with future work are also discussed. PMID:28230725

An Argument from Acquisition: Comparing English Metrical Stress Representations by How Learnable They Are from Child-Directed Speech

ERIC Educational Resources Information Center

Pearl, Lisa; Ho, Timothy; Detrano, Zephyr

2017-01-01

It has long been recognized that there is a natural dependence between theories of knowledge representation and theories of knowledge acquisition, with the idea that the right knowledge representation enables acquisition to happen as reliably as it does. Given this, a reasonable criterion for a theory of knowledge representation is that it be…

Research in Knowledge Representation for Natural Language Understanding.

DTIC Science & Technology

1984-09-01

TYPE OF REPORT & PERIOO COVERED RESEARCH IN KNOWLEDGE REPRESENTATION Annual Report FOR NATURAL LANGUAGE UNDERSTANDING 9/1/83 - 8/31/84 S. PERFORMING...nhaber) Artificial intelligence, natural language understanding , knowledge representation, semantics, semantic networks, KL-TWO, NIKL, belief and...attempting to understand and react to a complex, evolving situation. This report summarizes our research in knowledge representation and natural language

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis.

PubMed

El-Sappagh, Shaker; Elmogy, Mohammed; Riad, A M

2015-11-01

Case-based reasoning (CBR) is a problem-solving paradigm that uses past knowledge to interpret or solve new problems. It is suitable for experience-based and theory-less problems. Building a semantically intelligent CBR that mimic the expert thinking can solve many problems especially medical ones. Knowledge-intensive CBR using formal ontologies is an evolvement of this paradigm. Ontologies can be used for case representation and storage, and it can be used as a background knowledge. Using standard medical ontologies, such as SNOMED CT, enhances the interoperability and integration with the health care systems. Moreover, utilizing vague or imprecise knowledge further improves the CBR semantic effectiveness. This paper proposes a fuzzy ontology-based CBR framework. It proposes a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types. This framework is implemented and tested on the diabetes diagnosis problem. The fuzzy ontology is populated with 60 real diabetic cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies. The resulting system can answer complex medical queries related to semantic understanding of medical concepts and handling of vague terms. The resulting fuzzy case-base ontology has 63 concepts, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, 105 fuzzy datatypes, and 2640 instances. The system achieves an accuracy of 97.67%. We compare our framework with existing CBR systems and a set of five machine-learning classifiers; our system outperforms all of these systems. Building an integrated CBR system can improve its performance. Representing CBR knowledge using the fuzzy ontology and building a case retrieval algorithm that treats different features differently improves the accuracy of the resulting systems. Copyright © 2015 Elsevier B.V. All rights reserved.

Speech Recognition: Acoustic-Phonetic Knowledge Acquisition and Representation.

DTIC Science & Technology

1987-09-25

the release duration is the voice onset time, or VOT. For the purpose of this investigation, alveolar flaps ( as in "butter’) and and glottalized /t/’s...Cambridge, Massachusetts 02139 Abstract females and 8 males. The other sentence was said by 7 females We discuss a framework for an acoustic-phonetic...tarned a number of semivowels. One sentence was said by 6 vowels + + "jpporte.d by a Xerox Fellowhsp Table It Features which characterite

Effect of the Interaction of Text Structure, Background Knowledge and Purpose on Attention to Text.

DTIC Science & Technology

1982-04-01

in4 the sense proposed by Craik and Lockhart (1972). All levels of representation would entail such preliminary processing operations as perceptual...109. Craik , F. I., & Lockhart , R. S. Levels of processing : A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 1972, 11... processes this information to a deeper level than those text elements that are less important or irrelevant. The terminology "deeper" level is used here

Integrated Knowledge Elicitation and Representation Framework.

DTIC Science & Technology

1991-04-01

applications. Given the fast paced, time-stressed battlefield environment, expert and decision sup- port systems will play an important role in future...can weaken them a little bit, it will help." I: "Why would they use dummy guns to divert us?" E: "We use imagery a lot from a fast moving airplane. A...found that subjects are often slow to update their assessment of a situation, even when new information conflicts with their old impressions. Leddo and

Design of an extensive information representation scheme for clinical narratives.

PubMed

Deléger, Louise; Campillos, Leonardo; Ligozat, Anne-Laure; Névéol, Aurélie

2017-09-11

Knowledge representation frameworks are essential to the understanding of complex biomedical processes, and to the analysis of biomedical texts that describe them. Combined with natural language processing (NLP), they have the potential to contribute to retrospective studies by unlocking important phenotyping information contained in the narrative content of electronic health records (EHRs). This work aims to develop an extensive information representation scheme for clinical information contained in EHR narratives, and to support secondary use of EHR narrative data to answer clinical questions. We review recent work that proposed information representation schemes and applied them to the analysis of clinical narratives. We then propose a unifying scheme that supports the extraction of information to address a large variety of clinical questions. We devised a new information representation scheme for clinical narratives that comprises 13 entities, 11 attributes and 37 relations. The associated annotation guidelines can be used to consistently apply the scheme to clinical narratives and are https://cabernet.limsi.fr/annotation_guide_for_the_merlot_french_clinical_corpus-Sept2016.pdf . The information scheme includes many elements of the major schemes described in the clinical natural language processing literature, as well as a uniquely detailed set of relations.

Formal Representations of Eligibility Criteria: A Literature Review

PubMed Central

Weng, Chunhua; Tu, Samson W.; Sim, Ida; Richesson, Rachel

2010-01-01

Standards-based, computable knowledge representations for eligibility criteria are increasingly needed to provide computer-based decision support for automated research participant screening, clinical evidence application, and clinical research knowledge management. We surveyed the literature and identified five aspects of eligibility criteria knowledge representations that contribute to the various research and clinical applications: the intended use of computable eligibility criteria, the classification of eligibility criteria, the expression language for representing eligibility rules, the encoding of eligibility concepts, and the modeling of patient data. We consider three of them (expression language, codification of eligibility concepts, and patient data modeling), to be essential constructs of a formal knowledge representation for eligibility criteria. The requirements for each of the three knowledge constructs vary for different use cases, which therefore should inform the development and choice of the constructs toward cost-effective knowledge representation efforts. We discuss the implications of our findings for standardization efforts toward sharable knowledge representation of eligibility criteria. PMID:20034594

Towards a 3d Based Platform for Cultural Heritage Site Survey and Virtual Exploration

NASA Astrophysics Data System (ADS)

Seinturier, J.; Riedinger, C.; Mahiddine, A.; Peloso, D.; Boï, J.-M.; Merad, D.; Drap, P.

2013-07-01

This paper present a 3D platform that enables to make both cultural heritage site survey and its virtual exploration. It provides a single and easy way to use framework for merging multi scaled 3D measurements based on photogrammetry, documentation produced by experts and the knowledge of involved domains leaving the experts able to extract and choose the relevant information to produce the final survey. Taking into account the interpretation of the real world during the process of archaeological surveys is in fact the main goal of a survey. New advances in photogrammetry and the capability to produce dense 3D point clouds do not solve the problem of surveys. New opportunities for 3D representation are now available and we must to use them and find new ways to link geometry and knowledge. The new platform is able to efficiently manage and process large 3D data (points set, meshes) thanks to the implementation of space partition methods coming from the state of the art such as octrees and kd-trees and thus can interact with dense point clouds (thousands to millions of points) in real time. The semantisation of raw 3D data relies on geometric algorithms such as geodetic path computation, surface extraction from dense points cloud and geometrical primitive optimization. The platform provide an interface that enables expert to describe geometric representations of interesting objects like ashlar blocs, stratigraphic units or generic items (contour, lines, … ) directly onto the 3D representation of the site and without explicit links to underlying algorithms. The platform provide two ways for describing geometric representation. If oriented photographs are available, the expert can draw geometry on a photograph and the system computes its 3D representation by projection on the underlying mesh or the points cloud. If photographs are not available or if the expert wants to only use the 3D representation then he can simply draw objects shape on it. When 3D representations of objects of a surveyed site are extracted from the mesh, the link with domain related documentation is done by means of a set of forms designed by experts. Information from these forms are linked with geometry such that documentation can be attached to the viewed objects. Additional semantisation methods related to specific domains have been added to the platform. Beyond realistic rendering of surveyed site, the platform embeds non photorealistic rendering (NPR) algorithms. These algorithms enable to dynamically illustrate objects of interest that are related to knowledge with specific styles. The whole platform is implemented with a Java framework and relies on an actual and effective 3D engine that make available latest rendering methods. We illustrate this work on various photogrammetric survey, in medieval archaeology with the Shawbak castle in Jordan and in underwater archaeology on different marine sites.

Concept Representation Reflects Multimodal Abstraction: A Framework for Embodied Semantics

PubMed Central

Fernandino, Leonardo; Binder, Jeffrey R.; Desai, Rutvik H.; Pendl, Suzanne L.; Humphries, Colin J.; Gross, William L.; Conant, Lisa L.; Seidenberg, Mark S.

2016-01-01

Recent research indicates that sensory and motor cortical areas play a significant role in the neural representation of concepts. However, little is known about the overall architecture of this representational system, including the role played by higher level areas that integrate different types of sensory and motor information. The present study addressed this issue by investigating the simultaneous contributions of multiple sensory-motor modalities to semantic word processing. With a multivariate fMRI design, we examined activation associated with 5 sensory-motor attributes—color, shape, visual motion, sound, and manipulation—for 900 words. Regions responsive to each attribute were identified using independent ratings of the attributes' relevance to the meaning of each word. The results indicate that these aspects of conceptual knowledge are encoded in multimodal and higher level unimodal areas involved in processing the corresponding types of information during perception and action, in agreement with embodied theories of semantics. They also reveal a hierarchical system of abstracted sensory-motor representations incorporating a major division between object interaction and object perception processes. PMID:25750259

Weighted Description Logics Preference Formulas for Multiattribute Negotiation

NASA Astrophysics Data System (ADS)

Ragone, Azzurra; di Noia, Tommaso; Donini, Francesco M.; di Sciascio, Eugenio; Wellman, Michael P.

We propose a framework to compute the utility of an agreement w.r.t a preference set in a negotiation process. In particular, we refer to preferences expressed as weighted formulas in a decidable fragment of First-order Logic and agreements expressed as a formula. We ground our framework in Description Logics (DL) endowed with disjunction, to be compliant with Semantic Web technologies. A logic based approach to preference representation allows, when a background knowledge base is exploited, to relax the often unrealistic assumption of additive independence among attributes. We provide suitable definitions of the problem and present algorithms to compute utility in our setting. We also validate our approach through an experimental evaluation.

Compressed sampling and dictionary learning framework for wavelength-division-multiplexing-based distributed fiber sensing.

PubMed

Weiss, Christian; Zoubir, Abdelhak M

2017-05-01

We propose a compressed sampling and dictionary learning framework for fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is generated from a model for the reflected sensor signal. Imperfect prior knowledge is considered in terms of uncertain local and global parameters. To estimate a sparse representation and the dictionary parameters, we present an alternating minimization algorithm that is equipped with a preprocessing routine to handle dictionary coherence. The support of the obtained sparse signal indicates the reflection delays, which can be used to measure impairments along the sensing fiber. The performance is evaluated by simulations and experimental data for a fiber sensor system with common core architecture.

Considerations of multicultural science and curriculum reform: A content analysis of state-adopted biology textbooks in Florida

NASA Astrophysics Data System (ADS)

Delgato, Margaret H.

The purpose of this investigation was to determine the extent to which multicultural science education, including indigenous knowledge representations, had been infused within the content of high school biology textbooks. The study evaluated the textbook as an instructional tool and framework for multicultural science education instruction by comparing the mainstream content to indigenous knowledge perspectives portrayed in the student and teacher editions of 34 textbooks adopted in Florida within the last four adoption cycles occurring from 1990 to 2006. The investigation involved a content analysis framed from a mixed methods approach. Emphasis was placed, in consideration of the research questions and practicality of interpreting text with the potential for multiple meanings, within qualitative methods. The investigation incorporated five strategies to assess the extent of multicultural content: (1) calculation of frequency of indigenous representations through the use of a tally; (2) assessment of content in the teacher editions by coding the degree of incorporation of multicultural content; (3) development of an archaeology of statements to determine the ways in which indigenous representations were incorporated into the content; (4) use of the Evaluation Coefficient Analysis (ECO) to determine extent of multicultural terminologies within content; and (5) analysis of visuals and illustrations to gauge percentages of depictions of minority groups. Results indicated no solid trend in an increase of inclusion of multicultural content over the last four adoption cycles. Efforts at most reduced the inclusion of indigenous representations and other multicultural content to the level of the teacher edition distributed among the teacher-interleafed pages or as annotations in the margins. Degree of support of multicultural content to the specific goals and objectives remained limited across all four of the adoption cycles represented in the study. Emphasis on standardized testing appeared in the six textbooks representing the most recent adoption cycle. Recommendations included increased efforts to identify quality of content by including input from scholars in the field of multicultural education as well as indigenous peoples in the creation of textbook content. Recommendations also included further clarification of the definition of science within multicultural science education frameworks, indigenous knowledge as compared to Western science and pseudoscienc e, and scientific literacy as a central focus to a multicultural science education meant to address the needs of an increasingly diverse student population and prime-age workforce.

A Content-Adaptive Analysis and Representation Framework for Audio Event Discovery from "Unscripted" Multimedia

NASA Astrophysics Data System (ADS)

Radhakrishnan, Regunathan; Divakaran, Ajay; Xiong, Ziyou; Otsuka, Isao

2006-12-01

We propose a content-adaptive analysis and representation framework to discover events using audio features from "unscripted" multimedia such as sports and surveillance for summarization. The proposed analysis framework performs an inlier/outlier-based temporal segmentation of the content. It is motivated by the observation that "interesting" events in unscripted multimedia occur sparsely in a background of usual or "uninteresting" events. We treat the sequence of low/mid-level features extracted from the audio as a time series and identify subsequences that are outliers. The outlier detection is based on eigenvector analysis of the affinity matrix constructed from statistical models estimated from the subsequences of the time series. We define the confidence measure on each of the detected outliers as the probability that it is an outlier. Then, we establish a relationship between the parameters of the proposed framework and the confidence measure. Furthermore, we use the confidence measure to rank the detected outliers in terms of their departures from the background process. Our experimental results with sequences of low- and mid-level audio features extracted from sports video show that "highlight" events can be extracted effectively as outliers from a background process using the proposed framework. We proceed to show the effectiveness of the proposed framework in bringing out suspicious events from surveillance videos without any a priori knowledge. We show that such temporal segmentation into background and outliers, along with the ranking based on the departure from the background, can be used to generate content summaries of any desired length. Finally, we also show that the proposed framework can be used to systematically select "key audio classes" that are indicative of events of interest in the chosen domain.

Toward a comprehensive areal model of earthquake-induced landslides

USGS Publications Warehouse

Miles, S.B.; Keefer, D.K.

2009-01-01

This paper provides a review of regional-scale modeling of earthquake-induced landslide hazard with respect to the needs for disaster risk reduction and sustainable development. Based on this review, it sets out important research themes and suggests computing with words (CW), a methodology that includes fuzzy logic systems, as a fruitful modeling methodology for addressing many of these research themes. A range of research, reviewed here, has been conducted applying CW to various aspects of earthquake-induced landslide hazard zonation, but none facilitate comprehensive modeling of all types of earthquake-induced landslides. A new comprehensive areal model of earthquake-induced landslides (CAMEL) is introduced here that was developed using fuzzy logic systems. CAMEL provides an integrated framework for modeling all types of earthquake-induced landslides using geographic information systems. CAMEL is designed to facilitate quantitative and qualitative representation of terrain conditions and knowledge about these conditions on the likely areal concentration of each landslide type. CAMEL is highly modifiable and adaptable; new knowledge can be easily added, while existing knowledge can be changed to better match local knowledge and conditions. As such, CAMEL should not be viewed as a complete alternative to other earthquake-induced landslide models. CAMEL provides an open framework for incorporating other models, such as Newmark's displacement method, together with previously incompatible empirical and local knowledge. ?? 2009 ASCE.

Mapping of the Academic Production at Science and Mathematics Education Postgraduate about the Theory of Social Representations

NASA Astrophysics Data System (ADS)

Barbosa, José Isnaldo de Lima; Curi, Edda; Voelzke, Marcos Rincon

2016-12-01

The theory of social representations, appeared in 1961, arrived in Brazil in 1982, and since then has advanced significantly, been used in various areas of knowledge, assumed a significant role also in education. Thus, the aim of this article is to make a mapping of theses and dissertations in post-graduation programs, whose basic area is the Teaching of Science and Mathematics, and used as the theoretical foundation the theory of social representations, highlighted the social groups that are subject of this research. This is a documentary research, and lifting to the "state of knowledge" of two theses and 36 dissertations, defended in ten of the 37 existing programs in the basic area of Science and Mathematics Teaching, with the delimitation of academic masters and doctorates. The data collection was executed on December 2014 and was placed in the virtual libraries of these masters and doctoral programs, these elements were analysed according to some categories established after reading the summaries of the work, and the results showed that the theory of social representations has been used as a theoretical framework in various research groups, established in postgraduate programs in this area, for almost the entire Brazil. As for the subjects involved in this research, three groups were detected, which are: Middle school and high school students, teachers who are in full swing, spread from the early years to higher education, and undergraduates in Science and Mathematics.

A negotiation methodology and its application to cogeneration planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, S.M.; Liu, C.C.; Luu, S.

Power system planning has become a complex process in utilities today. This paper presents a methodology for integrated planning with multiple objectives. The methodology uses a graphical representation (Goal-Decision Network) to capture the planning knowledge. The planning process is viewed as a negotiation process that applies three negotiation operators to search for beneficial decisions in a GDN. Also, the negotiation framework is applied to the problem of planning for cogeneration interconnection. The simulation results are presented to illustrate the cogeneration planning process.

Information compression in the context model

NASA Technical Reports Server (NTRS)

Gebhardt, Joerg; Kruse, Rudolf; Nauck, Detlef

1992-01-01

The Context Model provides a formal framework for the representation, interpretation, and analysis of vague and uncertain data. The clear semantics of the underlying concepts make it feasible to compare well-known approaches to the modeling of imperfect knowledge like that given in Bayes Theory, Shafer's Evidence Theory, the Transferable Belief Model, and Possibility Theory. In this paper we present the basic ideas of the Context Model and show its applicability as an alternative foundation of Possibility Theory and the epistemic view of fuzzy sets.

Representation learning via Dual-Autoencoder for recommendation.

PubMed

Zhuang, Fuzhen; Zhang, Zhiqiang; Qian, Mingda; Shi, Chuan; Xie, Xing; He, Qing

2017-06-01

Recommendation has provoked vast amount of attention and research in recent decades. Most previous works employ matrix factorization techniques to learn the latent factors of users and items. And many subsequent works consider external information, e.g., social relationships of users and items' attributions, to improve the recommendation performance under the matrix factorization framework. However, matrix factorization methods may not make full use of the limited information from rating or check-in matrices, and achieve unsatisfying results. Recently, deep learning has proven able to learn good representation in natural language processing, image classification, and so on. Along this line, we propose a new representation learning framework called Recommendation via Dual-Autoencoder (ReDa). In this framework, we simultaneously learn the new hidden representations of users and items using autoencoders, and minimize the deviations of training data by the learnt representations of users and items. Based on this framework, we develop a gradient descent method to learn hidden representations. Extensive experiments conducted on several real-world data sets demonstrate the effectiveness of our proposed method compared with state-of-the-art matrix factorization based methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Wissensstrukturierung im Unterricht: Neuere Forschung zur Wissensreprasentation und ihre Anwendung in der Didaktik (Knowledge Structuring in Instruction: Recent Research on Knowledge Representation and Its Application in the Classroom).

ERIC Educational Resources Information Center

Einsiedler, Wolfgang

1996-01-01

Asks whether theories of knowledge representation provide a basis for the development of theories of knowledge structuring in instruction. Discusses codes of knowledge, surface versus deep structures, semantic networks, and multiple memory systems. Reviews research on teaching, external representation of cognitive structures, hierarchical…

MachineProse: an Ontological Framework for Scientific Assertions

PubMed Central

Dinakarpandian, Deendayal; Lee, Yugyung; Vishwanath, Kartik; Lingambhotla, Rohini

2006-01-01

Objective: The idea of testing a hypothesis is central to the practice of biomedical research. However, the results of testing a hypothesis are published mainly in the form of prose articles. Encoding the results as scientific assertions that are both human and machine readable would greatly enhance the synergistic growth and dissemination of knowledge. Design: We have developed MachineProse (MP), an ontological framework for the concise specification of scientific assertions. MP is based on the idea of an assertion constituting a fundamental unit of knowledge. This is in contrast to current approaches that use discrete concept terms from domain ontologies for annotation and assertions are only inferred heuristically. Measurements: We use illustrative examples to highlight the advantages of MP over the use of the Medical Subject Headings (MeSH) system and keywords in indexing scientific articles. Results: We show how MP makes it possible to carry out semantic annotation of publications that is machine readable and allows for precise search capabilities. In addition, when used by itself, MP serves as a knowledge repository for emerging discoveries. A prototype for proof of concept has been developed that demonstrates the feasibility and novel benefits of MP. As part of the MP framework, we have created an ontology of relationship types with about 100 terms optimized for the representation of scientific assertions. Conclusion: MachineProse is a novel semantic framework that we believe may be used to summarize research findings, annotate biomedical publications, and support sophisticated searches. PMID:16357355

Apes have culture but may not know that they do

PubMed Central

Gruber, Thibaud; Zuberbühler, Klaus; Clément, Fabrice; van Schaik, Carel

2015-01-01

There is good evidence that some ape behaviors can be transmitted socially and that this can lead to group-specific traditions. However, many consider animal traditions, including those in great apes, to be fundamentally different from human cultures, largely because of lack of evidence for cumulative processes and normative conformity, but perhaps also because current research on ape culture is usually restricted to behavioral comparisons. Here, we propose to analyze ape culture not only at the surface behavioral level but also at the underlying cognitive level. To this end, we integrate empirical findings in apes with theoretical frameworks developed in developmental psychology regarding the representation of tools and the development of metarepresentational abilities, to characterize the differences between ape and human cultures at the cognitive level. Current data are consistent with the notion of apes possessing mental representations of tools that can be accessed through re-representations: apes may reorganize their knowledge of tools in the form of categories or functional schemes. However, we find no evidence for metarepresentations of cultural knowledge: apes may not understand that they or others hold beliefs about their cultures. The resulting Jourdain Hypothesis, based on Molière’s character, argues that apes express their cultures without knowing that they are cultural beings because of cognitive limitations in their ability to represent knowledge, a determining feature of modern human cultures, allowing representing and modifying the current norms of the group. Differences in metarepresentational processes may thus explain fundamental differences between human and other animals’ cultures, notably limitations in cumulative behavior and normative conformity. Future empirical work should focus on how animals mentally represent their cultural knowledge to conclusively determine the ways by which humans are unique in their cultural behavior. PMID:25705199

A generic biogeochemical module for Earth system models: Next Generation BioGeoChemical Module (NGBGC), version 1.0

NASA Astrophysics Data System (ADS)

Fang, Y.; Huang, M.; Liu, C.; Li, H.; Leung, L. R.

2013-11-01

Physical and biogeochemical processes regulate soil carbon dynamics and CO2 flux to and from the atmosphere, influencing global climate changes. Integration of these processes into Earth system models (e.g., community land models (CLMs)), however, currently faces three major challenges: (1) extensive efforts are required to modify modeling structures and to rewrite computer programs to incorporate new or updated processes as new knowledge is being generated, (2) computational cost is prohibitively expensive to simulate biogeochemical processes in land models due to large variations in the rates of biogeochemical processes, and (3) various mathematical representations of biogeochemical processes exist to incorporate different aspects of fundamental mechanisms, but systematic evaluation of the different mathematical representations is difficult, if not impossible. To address these challenges, we propose a new computational framework to easily incorporate physical and biogeochemical processes into land models. The new framework consists of a new biogeochemical module, Next Generation BioGeoChemical Module (NGBGC), version 1.0, with a generic algorithm and reaction database so that new and updated processes can be incorporated into land models without the need to manually set up the ordinary differential equations to be solved numerically. The reaction database consists of processes of nutrient flow through the terrestrial ecosystems in plants, litter, and soil. This framework facilitates effective comparison studies of biogeochemical cycles in an ecosystem using different conceptual models under the same land modeling framework. The approach was first implemented in CLM and benchmarked against simulations from the original CLM-CN code. A case study was then provided to demonstrate the advantages of using the new approach to incorporate a phosphorus cycle into CLM. To our knowledge, the phosphorus-incorporated CLM is a new model that can be used to simulate phosphorus limitation on the productivity of terrestrial ecosystems. The method presented here could in theory be applied to simulate biogeochemical cycles in other Earth system models.

A Framework for Understanding Physics Students' Computational Modeling Practices

NASA Astrophysics Data System (ADS)

Lunk, Brandon Robert

With the growing push to include computational modeling in the physics classroom, we are faced with the need to better understand students' computational modeling practices. While existing research on programming comprehension explores how novices and experts generate programming algorithms, little of this discusses how domain content knowledge, and physics knowledge in particular, can influence students' programming practices. In an effort to better understand this issue, I have developed a framework for modeling these practices based on a resource stance towards student knowledge. A resource framework models knowledge as the activation of vast networks of elements called "resources." Much like neurons in the brain, resources that become active can trigger cascading events of activation throughout the broader network. This model emphasizes the connectivity between knowledge elements and provides a description of students' knowledge base. Together with resources resources, the concepts of "epistemic games" and "frames" provide a means for addressing the interaction between content knowledge and practices. Although this framework has generally been limited to describing conceptual and mathematical understanding, it also provides a means for addressing students' programming practices. In this dissertation, I will demonstrate this facet of a resource framework as well as fill in an important missing piece: a set of epistemic games that can describe students' computational modeling strategies. The development of this theoretical framework emerged from the analysis of video data of students generating computational models during the laboratory component of a Matter & Interactions: Modern Mechanics course. Student participants across two semesters were recorded as they worked in groups to fix pre-written computational models that were initially missing key lines of code. Analysis of this video data showed that the students' programming practices were highly influenced by their existing physics content knowledge, particularly their knowledge of analytic procedures. While this existing knowledge was often applied in inappropriate circumstances, the students were still able to display a considerable amount of understanding of the physics content and of analytic solution procedures. These observations could not be adequately accommodated by the existing literature of programming comprehension. In extending the resource framework to the task of computational modeling, I model students' practices in terms of three important elements. First, a knowledge base includes re- sources for understanding physics, math, and programming structures. Second, a mechanism for monitoring and control describes students' expectations as being directed towards numerical, analytic, qualitative or rote solution approaches and which can be influenced by the problem representation. Third, a set of solution approaches---many of which were identified in this study---describe what aspects of the knowledge base students use and how they use that knowledge to enact their expectations. This framework allows us as researchers to track student discussions and pinpoint the source of difficulties. This work opens up many avenues of potential research. First, this framework gives researchers a vocabulary for extending Resource Theory to other domains of instruction, such as modeling how physics students use graphs. Second, this framework can be used as the basis for modeling expert physicists' programming practices. Important instructional implications also follow from this research. Namely, as we broaden the use of computational modeling in the physics classroom, our instructional practices should focus on helping students understand the step-by-step nature of programming in contrast to the already salient analytic procedures.

A schema theory analysis of students' think aloud protocols in an STS biology context

NASA Astrophysics Data System (ADS)

Quinlan, Catherine Louise

This dissertation study is a conglomerate of the fields of Science Education and Applied Cognitive Psychology. The goal of this study is to determine what organizational features and knowledge representation patterns high school students exhibit over time for issues pertinent to science and society. Participants are thirteen tenth grade students in a diverse suburban-urban classroom in a northeastern state. Students' think alouds are recorded, pre-, post-, and late-post treatment. Treatment consists of instruction in three Science, Technology, and Society (STS) biology issues, namely the human genome project, nutrition and health, and stem cell research. Coding and analyses are performed using Marshall's knowledge representations---identification knowledge, elaboration knowledge, planning knowledge, and execution knowledge, as well as qualitative research analysis methods. Schema theory, information processing theory, and other applied cognitive theory provide a framework in which to understand and explain students' schema descriptions and progressions over time. The results show that students display five organizational features in their identification and elaboration knowledge. Students also fall into one of four categories according to if they display prior schema or no prior schema, and their orientation "for" or "against," some of the issues. Students with prior schema and orientation "against" display the most robust schema descriptions and schema progressions. Those with no prior schemas and orientation "against" show very modest schema progressions best characterized by their keyword searches. This study shows the importance in considering not only students' integrated schemas but also their individual schemes. A role for the use of a more schema-based instruction that scaffolds student learning is implicated.

Representations of the World in Language Textbooks

ERIC Educational Resources Information Center

Risager, Karen

2018-01-01

This book presents a new and comprehensive framework for the analysis of representations of culture, society and the world in textbooks for foreign and second language learning. The framework is transferable to other kinds of learning materials and to other subjects. The framework distinguishes between five approaches: national studies,…

Ontological modelling of knowledge management for human-machine integrated design of ultra-precision grinding machine

NASA Astrophysics Data System (ADS)

Hong, Haibo; Yin, Yuehong; Chen, Xing

2016-11-01

Despite the rapid development of computer science and information technology, an efficient human-machine integrated enterprise information system for designing complex mechatronic products is still not fully accomplished, partly because of the inharmonious communication among collaborators. Therefore, one challenge in human-machine integration is how to establish an appropriate knowledge management (KM) model to support integration and sharing of heterogeneous product knowledge. Aiming at the diversity of design knowledge, this article proposes an ontology-based model to reach an unambiguous and normative representation of knowledge. First, an ontology-based human-machine integrated design framework is described, then corresponding ontologies and sub-ontologies are established according to different purposes and scopes. Second, a similarity calculation-based ontology integration method composed of ontology mapping and ontology merging is introduced. The ontology searching-based knowledge sharing method is then developed. Finally, a case of human-machine integrated design of a large ultra-precision grinding machine is used to demonstrate the effectiveness of the method.

Towards a standardised representation of a knowledge base for adverse drug event prevention.

PubMed

Koutkias, Vassilis; Lazou, Katerina; de Clercq, Paul; Maglaveras, Nicos

2011-01-01

Knowledge representation is an important part of knowledge engineering activities that is crucial for enabling knowledge sharing and reuse. In this regard, standardised formalisms and technologies play a significant role. Especially for the medical domain, where knowledge may be tacit, not articulated and highly diverse, the development and adoption of standardised knowledge representations is highly challenging and of outmost importance to achieve knowledge interoperability. To this end, this paper presents a research effort towards the standardised representation of a Knowledge Base (KB) encapsulating rule-based signals and procedures for Adverse Drug Event (ADE) prevention. The KB constitutes an integral part of Clinical Decision Support Systems (CDSSs) to be used at the point of care. The paper highlights the requirements at the domain of discourse with respect to knowledge representation, according to which GELLO (an HL7 and ANSI standard) has been adopted. Results of our prototype implementation are presented along with the advantages and the limitations introduced by the employed approach.

An approach to knowledge engineering to support knowledge-based simulation of payload ground processing at the Kennedy Space Center

NASA Technical Reports Server (NTRS)

Mcmanus, Shawn; Mcdaniel, Michael

1989-01-01

Planning for processing payloads was always difficult and time-consuming. With the advent of Space Station Freedom and its capability to support a myriad of complex payloads, the planning to support this ground processing maze involves thousands of man-hours of often tedious data manipulation. To provide the capability to analyze various processing schedules, an object oriented knowledge-based simulation environment called the Advanced Generic Accomodations Planning Environment (AGAPE) is being developed. Having nearly completed the baseline system, the emphasis in this paper is directed toward rule definition and its relation to model development and simulation. The focus is specifically on the methodologies implemented during knowledge acquisition, analysis, and representation within the AGAPE rule structure. A model is provided to illustrate the concepts presented. The approach demonstrates a framework for AGAPE rule development to assist expert system development.

Knowledge Representation: A Brief Review.

ERIC Educational Resources Information Center

Vickery, B. C.

1986-01-01

Reviews different structures and techniques of knowledge representation: structure of database records and files, data structures in computer programming, syntatic and semantic structure of natural language, knowledge representation in artificial intelligence, and models of human memory. A prototype expert system that makes use of some of these…

Ologs: a categorical framework for knowledge representation.

PubMed

Spivak, David I; Kent, Robert E

2012-01-01

In this paper we introduce the olog, or ontology log, a category-theoretic model for knowledge representation (KR). Grounded in formal mathematics, ologs can be rigorously formulated and cross-compared in ways that other KR models (such as semantic networks) cannot. An olog is similar to a relational database schema; in fact an olog can serve as a data repository if desired. Unlike database schemas, which are generally difficult to create or modify, ologs are designed to be user-friendly enough that authoring or reconfiguring an olog is a matter of course rather than a difficult chore. It is hoped that learning to author ologs is much simpler than learning a database definition language, despite their similarity. We describe ologs carefully and illustrate with many examples. As an application we show that any primitive recursive function can be described by an olog. We also show that ologs can be aligned or connected together into a larger network using functors. The various methods of information flow and institutions can then be used to integrate local and global world-views. We finish by providing several different avenues for future research.

Computer-Aided Experiment Planning toward Causal Discovery in Neuroscience.

PubMed

Matiasz, Nicholas J; Wood, Justin; Wang, Wei; Silva, Alcino J; Hsu, William

2017-01-01

Computers help neuroscientists to analyze experimental results by automating the application of statistics; however, computer-aided experiment planning is far less common, due to a lack of similar quantitative formalisms for systematically assessing evidence and uncertainty. While ontologies and other Semantic Web resources help neuroscientists to assimilate required domain knowledge, experiment planning requires not only ontological but also epistemological (e.g., methodological) information regarding how knowledge was obtained. Here, we outline how epistemological principles and graphical representations of causality can be used to formalize experiment planning toward causal discovery. We outline two complementary approaches to experiment planning: one that quantifies evidence per the principles of convergence and consistency, and another that quantifies uncertainty using logical representations of constraints on causal structure. These approaches operationalize experiment planning as the search for an experiment that either maximizes evidence or minimizes uncertainty. Despite work in laboratory automation, humans must still plan experiments and will likely continue to do so for some time. There is thus a great need for experiment-planning frameworks that are not only amenable to machine computation but also useful as aids in human reasoning.

Ologs: A Categorical Framework for Knowledge Representation

PubMed Central

Spivak, David I.; Kent, Robert E.

2012-01-01

In this paper we introduce the olog, or ontology log, a category-theoretic model for knowledge representation (KR). Grounded in formal mathematics, ologs can be rigorously formulated and cross-compared in ways that other KR models (such as semantic networks) cannot. An olog is similar to a relational database schema; in fact an olog can serve as a data repository if desired. Unlike database schemas, which are generally difficult to create or modify, ologs are designed to be user-friendly enough that authoring or reconfiguring an olog is a matter of course rather than a difficult chore. It is hoped that learning to author ologs is much simpler than learning a database definition language, despite their similarity. We describe ologs carefully and illustrate with many examples. As an application we show that any primitive recursive function can be described by an olog. We also show that ologs can be aligned or connected together into a larger network using functors. The various methods of information flow and institutions can then be used to integrate local and global world-views. We finish by providing several different avenues for future research. PMID:22303434

Comprehensive mitigation framework for concurrent application of multiple clinical practice guidelines.

PubMed

Wilk, Szymon; Michalowski, Martin; Michalowski, Wojtek; Rosu, Daniela; Carrier, Marc; Kezadri-Hamiaz, Mounira

2017-02-01

In this work we propose a comprehensive framework based on first-order logic (FOL) for mitigating (identifying and addressing) interactions between multiple clinical practice guidelines (CPGs) applied to a multi-morbid patient while also considering patient preferences related to the prescribed treatment. With this framework we respond to two fundamental challenges associated with clinical decision support: (1) concurrent application of multiple CPGs and (2) incorporation of patient preferences into the decision making process. We significantly expand our earlier research by (1) proposing a revised and improved mitigation-oriented representation of CPGs and secondary medical knowledge for addressing adverse interactions and incorporating patient preferences and (2) introducing a new mitigation algorithm. Specifically, actionable graphs representing CPGs allow for parallel and temporal activities (decisions and actions). Revision operators representing secondary medical knowledge support temporal interactions and complex revisions across multiple actionable graphs. The mitigation algorithm uses the actionable graphs, revision operators and available (and possibly incomplete) patient information represented in FOL. It relies on a depth-first search strategy to find a valid sequence of revisions and uses theorem proving and model finding techniques to identify applicable revision operators and to establish a management scenario for a given patient if one exists. The management scenario defines a safe (interaction-free) and preferred set of activities together with possible patient states. We illustrate the use of our framework with a clinical case study describing two patients who suffer from chronic kidney disease, hypertension, and atrial fibrillation, and who are managed according to CPGs for these diseases. While in this paper we are primarily concerned with the methodological aspects of mitigation, we also briefly discuss a high-level proof of concept implementation of the proposed framework in the form of a clinical decision support system (CDSS). The proposed mitigation CDSS "insulates" clinicians from the complexities of the FOL representations and provides semantically meaningful summaries of mitigation results. Ultimately we plan to implement the mitigation CDSS within our MET (Mobile Emergency Triage) decision support environment. Copyright © 2016 Elsevier Inc. All rights reserved.

Differential involvement of knowledge representation and executive control in episodic memory performance in young and older adults.

PubMed

Bouazzaoui, Badiâa; Fay, Séverine; Taconnat, Laurence; Angel, Lucie; Vanneste, Sandrine; Isingrini, Michel

2013-06-01

Craik and Bialystok (2006, 2008) postulated that examining the evolution of knowledge representation and control processes across the life span could help in understanding age-related cognitive changes. The present study explored the hypothesis that knowledge representation and control processes are differentially involved in the episodic memory performance of young and older adults. Young and older adults were administered a cued-recall task and tests of crystallized knowledge and executive functioning to measure representation and control processes, respectively. Results replicate the classic finding that executive and cued-recall performance decline with age, but crystallized-knowledge performance does not. Factor analysis confirmed the independence of representation and control. Correlation analyses showed that the memory performance of younger adults was correlated with representation but not with control measures, whereas the memory performance of older adults was correlated with both representation and control measures. Regression analyses indicated that the control factor was the main predictor of episodic-memory performance for older adults, with the representation factor adding an independent contribution, but the representation factor was the sole predictor for young adults. This finding supports the view that factors sustaining episodic memory vary from young adulthood to old age; representation was shown to be important throughout adulthood, and control was also important for older adults. The results also indicated that control and representation modulate age-group-related variance in episodic memory.

Knowledge Organization through Multiple Representations in a Computer-Supported Collaborative Learning Environment

ERIC Educational Resources Information Center

Namdar, Bahadir; Shen, Ji

2018-01-01

Computer-supported collaborative learning (CSCL) environments provide learners with multiple representational tools for storing, sharing, and constructing knowledge. However, little is known about how learners organize knowledge through multiple representations about complex socioscientific issues. Therefore, the purpose of this study was to…

Computational framework to support integration of biomolecular and clinical data within a translational approach.

PubMed

Miyoshi, Newton Shydeo Brandão; Pinheiro, Daniel Guariz; Silva, Wilson Araújo; Felipe, Joaquim Cezar

2013-06-06

The use of the knowledge produced by sciences to promote human health is the main goal of translational medicine. To make it feasible we need computational methods to handle the large amount of information that arises from bench to bedside and to deal with its heterogeneity. A computational challenge that must be faced is to promote the integration of clinical, socio-demographic and biological data. In this effort, ontologies play an essential role as a powerful artifact for knowledge representation. Chado is a modular ontology-oriented database model that gained popularity due to its robustness and flexibility as a generic platform to store biological data; however it lacks supporting representation of clinical and socio-demographic information. We have implemented an extension of Chado - the Clinical Module - to allow the representation of this kind of information. Our approach consists of a framework for data integration through the use of a common reference ontology. The design of this framework has four levels: data level, to store the data; semantic level, to integrate and standardize the data by the use of ontologies; application level, to manage clinical databases, ontologies and data integration process; and web interface level, to allow interaction between the user and the system. The clinical module was built based on the Entity-Attribute-Value (EAV) model. We also proposed a methodology to migrate data from legacy clinical databases to the integrative framework. A Chado instance was initialized using a relational database management system. The Clinical Module was implemented and the framework was loaded using data from a factual clinical research database. Clinical and demographic data as well as biomaterial data were obtained from patients with tumors of head and neck. We implemented the IPTrans tool that is a complete environment for data migration, which comprises: the construction of a model to describe the legacy clinical data, based on an ontology; the Extraction, Transformation and Load (ETL) process to extract the data from the source clinical database and load it in the Clinical Module of Chado; the development of a web tool and a Bridge Layer to adapt the web tool to Chado, as well as other applications. Open-source computational solutions currently available for translational science does not have a model to represent biomolecular information and also are not integrated with the existing bioinformatics tools. On the other hand, existing genomic data models do not represent clinical patient data. A framework was developed to support translational research by integrating biomolecular information coming from different "omics" technologies with patient's clinical and socio-demographic data. This framework should present some features: flexibility, compression and robustness. The experiments accomplished from a use case demonstrated that the proposed system meets requirements of flexibility and robustness, leading to the desired integration. The Clinical Module can be accessed in http://dcm.ffclrp.usp.br/caib/pg=iptrans.

Scientific Disclosure: Social Representations of Brazilian Researchers Acting in the Field of Astronomy

NASA Astrophysics Data System (ADS)

Carneiro, D. L. C. M.

2014-10-01

Science dissemination has unquestioned role on intermediate science and society and it is a wide subject of research in education, considering that the construction of knowledge flows in different spaces, and, consequently, produces and disseminates representations. It presents as a motivator for reflection and as a necessary tool to prevent that knowledge do not become synonymous with domination and power. Thereby, the Astronomy assumes a remarkable role as a trigger of scientific dissemination process, due to its interdisciplinary character. From this viewpoint and the theoretical and methodological framework of the Theory of Social Representations (TRS), grounded by Serge Moscovici, this research, qualitative in nature, seek to answer: What are the social representations about scientific dissemination of Brazilian researchers that act in the field of astronomy? The work was based on Longhini, Gomide and Fernandes (2013) research, which delineate the Brazilian scientific community involved in Astronomy, identifying two groups of researchers with different training paths: one with postgraduate in education and related fields, and other with postgraduate in Physics or Astronomy. Thus, this study had the subquestion: Does the researchers of these groups have different conceptions about the practices of science dissemination? A sample was composed of six subjects, three of each formative course, who participated in semi-structured interviews analyzed following the steps outlined by Spink (2012). The results show that the science dissemination is part of the researches schedule's, with a positive image relative to promote scientific knowledge to population and similar on practical approach between the two groups. Point to two social representations of science dissemination: one for society in general, moved by passion, anchored in values and beliefs, in satisfaction of seeing the results that their actions bring to people's lives; and the other to their pairs. Regarding the first, the core of this work, emerge gaps that hinder the practice of science dissemination, such as lack of professionalism and difficulty of using language accessible to the lay public; the lack of appreciation, so far, of the area and the bureaucracy required in the execution of projects, which come from institutions and sponsoring agencies, and the negative representation about the media, in general, are added to the list of obstacles, inferring that science dissemination is a paradigm in construction. Other considerations are that astronomy is not part of basic education systematic way or the media in general and, not infrequently, in these areas, this science presents with misconceptions. And there is an intersection between science education and science dissemination, wherein the researcher must approach to the elementary school teachers and the population. There is an indication of expanding non-formal spaces of education and the creation of a specific policy for Astronomy. In short, the found representations ponder some of the concerns currently present in society and that are echoed in the theoretical framework of this study, demonstrating that, in Brazil, despite advances, in general, science dissemination, science education, and, specifically, Astronomy Education, are in a social fragility context.

A UML-based meta-framework for system design in public health informatics.

PubMed

Orlova, Anna O; Lehmann, Harold

2002-01-01

The National Agenda for Public Health Informatics calls for standards in data and knowledge representation within public health, which requires a multi-level framework that links all aspects of public health. The literature of public health informatics and public health informatics application were reviewed. A UML-based systems analysis was performed. Face validity of results was evaluated in analyzing the public health domain of lead poisoning. The core class of the UML-based system of public health is the Public Health Domain, which is associated with multiple Problems, for which Actors provide Perspectives. Actors take Actions that define, generate, utilize and/or evaluate Data Sources. The life cycle of the domain is a sequence of activities attributed to its problems that spirals through multiple iterations and realizations within a domain. The proposed Public Health Informatics Meta-Framework broadens efforts in applying informatics principles to the field of public health

Do Knowledge-Component Models Need to Incorporate Representational Competencies?

ERIC Educational Resources Information Center

Rau, Martina Angela

2017-01-01

Traditional knowledge-component models describe students' content knowledge (e.g., their ability to carry out problem-solving procedures or their ability to reason about a concept). In many STEM domains, instruction uses multiple visual representations such as graphs, figures, and diagrams. The use of visual representations implies a…

Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework.

PubMed

Yao, Jincao; Yu, Huimin; Hu, Roland

2017-01-01

This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a "wake-sleep"-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.

Abstraction in perceptual symbol systems.

PubMed Central

Barsalou, Lawrence W

2003-01-01

After reviewing six senses of abstraction, this article focuses on abstractions that take the form of summary representations. Three central properties of these abstractions are established: ( i ) type-token interpretation; (ii) structured representation; and (iii) dynamic realization. Traditional theories of representation handle interpretation and structure well but are not sufficiently dynamical. Conversely, connectionist theories are exquisitely dynamic but have problems with structure. Perceptual symbol systems offer an approach that implements all three properties naturally. Within this framework, a loose collection of property and relation simulators develops to represent abstractions. Type-token interpretation results from binding a property simulator to a region of a perceived or simulated category member. Structured representation results from binding a configuration of property and relation simulators to multiple regions in an integrated manner. Dynamic realization results from applying different subsets of property and relation simulators to category members on different occasions. From this standpoint, there are no permanent or complete abstractions of a category in memory. Instead, abstraction is the skill to construct temporary online interpretations of a category's members. Although an infinite number of abstractions are possible, attractors develop for habitual approaches to interpretation. This approach provides new ways of thinking about abstraction phenomena in categorization, inference, background knowledge and learning. PMID:12903648

A Conceptual Framework for Error Remediation with Multiple External Representations Applied to Learning Objects

ERIC Educational Resources Information Center

Leite, Maici Duarte; Marczal, Diego; Pimentel, Andrey Ricardo; Direne, Alexandre Ibrahim

2014-01-01

This paper presents the application of some concepts of Intelligent Tutoring Systems (ITS) to elaborate a conceptual framework that uses the remediation of errors with Multiple External Representations (MERs) in Learning Objects (LO). To this is demonstrated a development of LO for teaching the Pythagorean Theorem through this framework. This…

From knowledge presentation to knowledge representation to knowledge construction: Future directions for hypermedia

NASA Technical Reports Server (NTRS)

Palumbo, David B.

1990-01-01

Relationships between human memory systems and hypermedia systems are discussed with particular emphasis on the underlying importance of associational memory. The distinctions between knowledge presentation, knowledge representation, and knowledge constructions are addressed. Issues involved in actually developing individualizable hypermedia based knowledge construction tools are presented.

A novel probabilistic framework for event-based speech recognition

NASA Astrophysics Data System (ADS)

Juneja, Amit; Espy-Wilson, Carol

2003-10-01

One of the reasons for unsatisfactory performance of the state-of-the-art automatic speech recognition (ASR) systems is the inferior acoustic modeling of low-level acoustic-phonetic information in the speech signal. An acoustic-phonetic approach to ASR, on the other hand, explicitly targets linguistic information in the speech signal, but such a system for continuous speech recognition (CSR) is not known to exist. A probabilistic and statistical framework for CSR based on the idea of the representation of speech sounds by bundles of binary valued articulatory phonetic features is proposed. Multiple probabilistic sequences of linguistically motivated landmarks are obtained using binary classifiers of manner phonetic features-syllabic, sonorant and continuant-and the knowledge-based acoustic parameters (APs) that are acoustic correlates of those features. The landmarks are then used for the extraction of knowledge-based APs for source and place phonetic features and their binary classification. Probabilistic landmark sequences are constrained using manner class language models for isolated or connected word recognition. The proposed method could overcome the disadvantages encountered by the early acoustic-phonetic knowledge-based systems that led the ASR community to switch to systems highly dependent on statistical pattern analysis methods and probabilistic language or grammar models.

From infotainment to tools of interaction - A critical perspective on the use of film/video in geoscience

NASA Astrophysics Data System (ADS)

Ickert, Johanna

2017-04-01

In times of omnipresent digitisation and interconnectedness, the way how we generate and experience knowledge on geo-related themes is strongly influenced by audiovisual media representations. Moving images are powerful tools and have significant potential to communicate science in novel ways. Major research frameworks such as Horizon 2020 strongly encourage the use of audiovisual media in order to communicate science "more effectively" to the public. An increasing number of geoscientists produce films themselves, while most of them still delegate this task to media professionals to whom they provide their scientific expert knowledge. Usually, the intention behind these outreach efforts is to take advantage of the suitability of the medium to convey "scientific facts", or to motivate certain cognitive/behavioural responses of different target audiences. Undoubtedly, film has a great potential for representing geoscientific knowledge and thus has become a key instrument for geoscience communication. However, the use of images also raises fundamental ethical and representational concerns. While the latter have provoked intense debates in sub-disciplines such as visual anthropology or film geography, the geosciences have paid only little attention to questions on how distinct practices and disciplinary paradigms create filmic representations. Given the fact that the use of scientific images and film is far from being "objective" and that the way how we create and experience images is always context-specific and strongly influenced by the relationship between film maker, film subjects/informants and audience, a series of important question arises: What do we know about the use of film in geosciences beyond the realm of information and representational purposes? What can we learn from using film as a reflexive, process-oriented and dialogue-based medium? How can film help us to better understand ethical and representational dimensions of our interaction with the public? What are the phenomenological qualities of film and how can they be made productive for science communication? This article explores the potential for novel approaches to use film in geology not only as outreach tool, but also as method of joint knowledge production in inter- and transdisciplinary collaboration. In order to provide evidence for the above-mentioned observations, a historical perspective on the use of film in geosciences as well as an in-depth analysis of recent art-science-collaborations will be given.

Requirements analysis, domain knowledge, and design

NASA Technical Reports Server (NTRS)

Potts, Colin

1988-01-01

Two improvements to current requirements analysis practices are suggested: domain modeling, and the systematic application of analysis heuristics. Domain modeling is the representation of relevant application knowledge prior to requirements specification. Artificial intelligence techniques may eventually be applicable for domain modeling. In the short term, however, restricted domain modeling techniques, such as that in JSD, will still be of practical benefit. Analysis heuristics are standard patterns of reasoning about the requirements. They usually generate questions of clarification or issues relating to completeness. Analysis heuristics can be represented and therefore systematically applied in an issue-based framework. This is illustrated by an issue-based analysis of JSD's domain modeling and functional specification heuristics. They are discussed in the context of the preliminary design of simple embedded systems.

Information integration from heterogeneous data sources: a Semantic Web approach.

PubMed

Kunapareddy, Narendra; Mirhaji, Parsa; Richards, David; Casscells, S Ward

2006-01-01

Although the decentralized and autonomous implementation of health information systems has made it possible to extend the reach of surveillance systems to a variety of contextually disparate domains, public health use of data from these systems is not primarily anticipated. The Semantic Web has been proposed to address both representational and semantic heterogeneity in distributed and collaborative environments. We introduce a semantic approach for the integration of health data using the Resource Definition Framework (RDF) and the Simple Knowledge Organization System (SKOS) developed by the Semantic Web community.

Knowledge representation and management enabling intelligent interoperability - principles and standards.

PubMed

Blobel, Bernd

2013-01-01

Based on the paradigm changes for health, health services and underlying technologies as well as the need for at best comprehensive and increasingly automated interoperability, the paper addresses the challenge of knowledge representation and management for medical decision support. After introducing related definitions, a system-theoretical, architecture-centric approach to decision support systems (DSSs) and appropriate ways for representing them using systems of ontologies is given. Finally, existing and emerging knowledge representation and management standards are presented. The paper focuses on the knowledge representation and management part of DSSs, excluding the reasoning part from consideration.

A novel knowledge-based system for interpreting complex engineering drawings: theory, representation, and implementation.

PubMed

Lu, Tong; Tai, Chiew-Lan; Yang, Huafei; Cai, Shijie

2009-08-01

We present a novel knowledge-based system to automatically convert real-life engineering drawings to content-oriented high-level descriptions. The proposed method essentially turns the complex interpretation process into two parts: knowledge representation and knowledge-based interpretation. We propose a new hierarchical descriptor-based knowledge representation method to organize the various types of engineering objects and their complex high-level relations. The descriptors are defined using an Extended Backus Naur Form (EBNF), facilitating modification and maintenance. When interpreting a set of related engineering drawings, the knowledge-based interpretation system first constructs an EBNF-tree from the knowledge representation file, then searches for potential engineering objects guided by a depth-first order of the nodes in the EBNF-tree. Experimental results and comparisons with other interpretation systems demonstrate that our knowledge-based system is accurate and robust for high-level interpretation of complex real-life engineering projects.

Conceptual knowledge representation: A cross-section of current research.

PubMed

Rogers, Timothy T; Wolmetz, Michael

2016-01-01

How is conceptual knowledge encoded in the brain? This special issue of Cognitive Neuropsychology takes stock of current efforts to answer this question through a variety of methods and perspectives. Across this work, three questions recur, each fundamental to knowledge representation in the mind and brain. First, what are the elements of conceptual representation? Second, to what extent are conceptual representations embodied in sensory and motor systems? Third, how are conceptual representations shaped by context, especially linguistic context? In this introductory article we provide relevant background on these themes and introduce how they are addressed by our contributing authors.

A logical foundation for representation of clinical data.

PubMed Central

Campbell, K E; Das, A K; Musen, M A

1994-01-01

OBJECTIVE: A general framework for representation of clinical data that provides a declarative semantics of terms and that allows developers to define explicitly the relationships among both terms and combinations of terms. DESIGN: Use of conceptual graphs as a standard representation of logic and of an existing standardized vocabulary, the Systematized Nomenclature of Medicine (SNOMED International), for lexical elements. Concepts such as time, anatomy, and uncertainty must be modeled explicitly in a way that allows relation of these foundational concepts to surface-level clinical descriptions in a uniform manner. RESULTS: The proposed framework was used to model a simple radiology report, which included temporal references. CONCLUSION: Formal logic provides a framework for formalizing the representation of medical concepts. Actual implementations will be required to evaluate the practicality of this approach. PMID:7719805

Koopman Operator Framework for Time Series Modeling and Analysis

NASA Astrophysics Data System (ADS)

Surana, Amit

2018-01-01

We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.

Knowledge Representation Of CT Scans Of The Head

NASA Astrophysics Data System (ADS)

Ackerman, Laurens V.; Burke, M. W.; Rada, Roy

1984-06-01

We have been investigating diagnostic knowledge models which assist in the automatic classification of medical images by combining information extracted from each image with knowledge specific to that class of images. In a more general sense we are trying to integrate verbal and pictorial descriptions of disease via representations of knowledge, study automatic hypothesis generation as related to clinical medicine, evolve new mathematical image measures while integrating them into the total diagnostic process, and investigate ways to augment the knowledge of the physician. Specifically, we have constructed an artificial intelligence knowledge model using the technique of a production system blending pictorial and verbal knowledge about the respective CT scan and patient history. It is an attempt to tie together different sources of knowledge representation, picture feature extraction and hypothesis generation. Our knowledge reasoning and representation system (KRRS) works with data at the conscious reasoning level of the practicing physician while at the visual perceptional level we are building another production system, the picture parameter extractor (PPE). This paper describes KRRS and its relationship to PPE.

Investigating the Implementation of Knowledge Representation in the COMBATXXI System

DTIC Science & Technology

2015-06-01

mechanism. Finally, follow-on research can work towards more cognitive modeling in order to distinguish between manned systems and unmanned systems in...Approved for public release; distribution is unlimited INVESTIGATING THE IMPLEMENTATION OF KNOWLEDGE REPRESENTATION IN THE COMBATXXI SYSTEM by Mongi...INVESTIGATING THE IMPLEMENTATION OF KNOWLEDGE REPRESENTATION IN THE COMBATXXI SYSTEM 5. FUNDING NUMBERS GM10331601, National Institute of General

Progress in knowledge representation research

NASA Technical Reports Server (NTRS)

Lum, Henry

1985-01-01

Brief descriptions are given of research being carried out in the field of knowledge representation. Dynamic simulation and modelling of planning systems with real-time sensor inputs; development of domain-independent knowledge representation tools which can be used in the development of application-specific expert and planning systems; and development of a space-borne very high speed integrated circuit processor are among the projects discussed.

Integrating natural language processing and web GIS for interactive knowledge domain visualization

NASA Astrophysics Data System (ADS)

Du, Fangming

Recent years have seen a powerful shift towards data-rich environments throughout society. This has extended to a change in how the artifacts and products of scientific knowledge production can be analyzed and understood. Bottom-up approaches are on the rise that combine access to huge amounts of academic publications with advanced computer graphics and data processing tools, including natural language processing. Knowledge domain visualization is one of those multi-technology approaches, with its aim of turning domain-specific human knowledge into highly visual representations in order to better understand the structure and evolution of domain knowledge. For example, network visualizations built from co-author relations contained in academic publications can provide insight on how scholars collaborate with each other in one or multiple domains, and visualizations built from the text content of articles can help us understand the topical structure of knowledge domains. These knowledge domain visualizations need to support interactive viewing and exploration by users. Such spatialization efforts are increasingly looking to geography and GIS as a source of metaphors and practical technology solutions, even when non-georeferenced information is managed, analyzed, and visualized. When it comes to deploying spatialized representations online, web mapping and web GIS can provide practical technology solutions for interactive viewing of knowledge domain visualizations, from panning and zooming to the overlay of additional information. This thesis presents a novel combination of advanced natural language processing - in the form of topic modeling - with dimensionality reduction through self-organizing maps and the deployment of web mapping/GIS technology towards intuitive, GIS-like, exploration of a knowledge domain visualization. A complete workflow is proposed and implemented that processes any corpus of input text documents into a map form and leverages a web application framework to let users explore knowledge domain maps interactively. This workflow is implemented and demonstrated for a data set of more than 66,000 conference abstracts.

Social knowledge and the construction of drinking water preference.

PubMed

Soares, Ana Carolina Cordeiro; Carmo, Rose Ferraz; Bevilacqua, Paula Dias

2017-10-01

The analytical categories of Health Surveillance territorialization and daily life guided the design of this study, which aimed to understand from the methodological framework of qualitative research the factors involved in the use of individual supply solutions (ISS) as drinking water sources. We conducted semi-structured interviews with residents of 22 households set at a municipality in the Zona da Mata Mineira. Statements were fully transcribed, processed through content analysis and interpreted based on the psychosocial theory of social representations. It was possible to apprehend the social and affective components of social representations. The social component characterized by the representation of water from IWSS ISS water as clean and of good quality seemed to drive or justify the "resistance" of individuals to use water from public supply. The affective component referred to the use of IWSS water from ISS as a return to and protection of individuals' origins, a way to strengthen respondents' identity. The results pointed out that people's perceptions and demands might guide actions aimed to stimulate trust in the use of public system water and the choice of this source of supply, contributing to health protection.

Certainty grids for mobile robots

NASA Technical Reports Server (NTRS)

Moravec, H. P.

1987-01-01

A numerical representation of uncertain and incomplete sensor knowledge called Certainty Grids has been used successfully in several mobile robot control programs, and has proven itself to be a powerful and efficient unifying solution for sensor fusion, motion planning, landmark identification, and many other central problems. Researchers propose to build a software framework running on processors onboard the new Uranus mobile robot that will maintain a probabilistic, geometric map of the robot's surroundings as it moves. The certainty grid representation will allow this map to be incrementally updated in a uniform way from various sources including sonar, stereo vision, proximity and contact sensors. The approach can correctly model the fuzziness of each reading, while at the same time combining multiple measurements to produce sharper map features, and it can deal correctly with uncertainties in the robot's motion. The map will be used by planning programs to choose clear paths, identify locations (by correlating maps), identify well-known and insufficiently sensed terrain, and perhaps identify objects by shape. The certainty grid representation can be extended in the same dimension and used to detect and track moving objects.

The nature and development of preservice science teachers' conceptions of subject matter and pedagogy

NASA Astrophysics Data System (ADS)

Lederman, Norman G.; Gess-Newsome, Julie; Latz, Mark S.

The purpose of this study was to assess the development and changes in preservice science teachers' subject matter and pedagogy knowledge structures as they proceeded through a professional teacher education program. Twelve secondary preservice science teachers were asked to create representations of their subject matter and pedagogy knowledge structures periodically (four times spanning the entirety of their subject-specific teacher education program) and participate in a videotaped interview concerning the eight knowledge structure representations immediately following student teaching. Qualitative analyses of knowledge structure representations and transcribed interviews within and between subjects were performed by one of the researchers and blindly corroborated by the other two researchers. Initial knowledge structure representations were typically linear and lacked coherence. Both types of knowledge structure representations were highly susceptible to change as a consequence of the act of teaching. Although there was some overlap between subject matter and pedagogy knowledge structures, they were reported to exert separate influences on classroom practice, with the pedagogy knowledge structure having primary influence on instructional decisions. Furthermore, the complexity of one's subject matter structure appeared to be a critical factor in determining whether the structure directly influences classroom practice.Received: 5 February 1993; Revised: 28 July 1993;

Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

NASA Astrophysics Data System (ADS)

Jiang, Guo-Qian; Xie, Ping; Wang, Xiao; Chen, Meng; He, Qun

2017-11-01

The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning (MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies: motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

Knowledge Representation and Management, It's Time to Integrate!

PubMed

Dhombres, F; Charlet, J

2017-08-01

Objectives: To select, present, and summarize the best papers published in 2016 in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive and standardized review of the medical informatics literature was performed based on a PubMed query. Results: Among the 1,421 retrieved papers, the review process resulted in the selection of four best papers focused on the integration of heterogeneous data via the development and the alignment of terminological resources. In the first article, the authors provide a curated and standardized version of the publicly available US FDA Adverse Event Reporting System. Such a resource will improve the quality of the underlying data, and enable standardized analyses using common vocabularies. The second article describes a project developed in order to facilitate heterogeneous data integration in the i2b2 framework. The originality is to allow users integrate the data described in different terminologies and to build a new repository, with a unique model able to support the representation of the various data. The third paper is dedicated to model the association between multiple phenotypic traits described within the Human Phenotype Ontology (HPO) and the corresponding genotype in the specific context of rare diseases (rare variants). Finally, the fourth paper presents solutions to annotation-ontology mapping in genome-scale data. Of particular interest in this work is the Experimental Factor Ontology (EFO) and its generic association model, the Ontology of Biomedical AssociatioN (OBAN). Conclusion: Ontologies have started to show their efficiency to integrate medical data for various tasks in medical informatics: electronic health records data management, clinical research, and knowledge-based systems development. Georg Thieme Verlag KG Stuttgart.

The interaction of representation and reasoning.

PubMed

Bundy, Alan

2013-09-08

Automated reasoning is an enabling technology for many applications of informatics. These applications include verifying that a computer program meets its specification; enabling a robot to form a plan to achieve a task and answering questions by combining information from diverse sources, e.g. on the Internet, etc. How is automated reasoning possible? Firstly, knowledge of a domain must be stored in a computer, usually in the form of logical formulae. This knowledge might, for instance, have been entered manually, retrieved from the Internet or perceived in the environment via sensors, such as cameras. Secondly, rules of inference are applied to old knowledge to derive new knowledge. Automated reasoning techniques have been adapted from logic, a branch of mathematics that was originally designed to formalize the reasoning of humans, especially mathematicians. My special interest is in the way that representation and reasoning interact. Successful reasoning is dependent on appropriate representation of both knowledge and successful methods of reasoning. Failures of reasoning can suggest changes of representation. This process of representational change can also be automated. We will illustrate the automation of representational change by drawing on recent work in my research group.

Public health situation awareness: toward a semantic approach

NASA Astrophysics Data System (ADS)

Mirhaji, Parsa; Richesson, Rachel L.; Turley, James P.; Zhang, Jiajie; Smith, Jack W.

2004-04-01

We propose a knowledge-based public health situation awareness system. The basis for this system is an explicit representation of public health situation awareness concepts and their interrelationships. This representation is based upon the users" (public health decision makers) cognitive model of the world, and optimized towards the efficacy of performance and relevance to the public health situation awareness processes and tasks. In our approach, explicit domain knowledge is the foundation for interpretation of public health data, as apposed to conventional systems where the statistical methods are the essence of the processes. Objectives: To develop a prototype knowledge-based system for public health situation awareness and to demonstrate the utility of knowledge intensive approaches in integration of heterogeneous information, eliminating the effects of incomplete and poor quality surveillance data, uncertainty in syndrome and aberration detection and visualization of complex information structures in public health surveillance settings, particularly in the context of bioterrorism (BT) preparedness. The system employs the Resource Definition Framework (RDF) and additional layers of more expressive languages to explicate the knowledge of domain experts into machine interpretable and computable problem-solving modules that can then guide users and computer systems in sifting through the most "relevant" data for syndrome and outbreak detection and investigation of root cause of the event. The Center for Biosecurity and Public Health Informatics Research is developing a prototype knowledge-based system around influenza, which has complex natural disease patterns, many public health implications, and is a potential agent for bioterrorism. The preliminary data from this effort may demonstrate superior performance in information integration, syndrome and aberration detection, information access through information visualization, and cross-domain investigation of the root causes of public health events.

Detailed qualitative dynamic knowledge representation using a BioNetGen model of TLR-4 signaling and preconditioning.

PubMed

An, Gary C; Faeder, James R

2009-01-01

Intracellular signaling/synthetic pathways are being increasingly extensively characterized. However, while these pathways can be displayed in static diagrams, in reality they exist with a degree of dynamic complexity that is responsible for heterogeneous cellular behavior. Multiple parallel pathways exist and interact concurrently, limiting the ability to integrate the various identified mechanisms into a cohesive whole. Computational methods have been suggested as a means of concatenating this knowledge to aid in the understanding of overall system dynamics. Since the eventual goal of biomedical research is the identification and development of therapeutic modalities, computational representation must have sufficient detail to facilitate this 'engineering' process. Adding to the challenge, this type of representation must occur in a perpetual state of incomplete knowledge. We present a modeling approach to address this challenge that is both detailed and qualitative. This approach is termed 'dynamic knowledge representation,' and is intended to be an integrated component of the iterative cycle of scientific discovery. BioNetGen (BNG), a software platform for modeling intracellular signaling pathways, was used to model the toll-like receptor 4 (TLR-4) signal transduction cascade. The informational basis of the model was a series of reference papers on modulation of (TLR-4) signaling, and some specific primary research papers to aid in the characterization of specific mechanistic steps in the pathway. This model was detailed with respect to the components of the pathway represented, but qualitative with respect to the specific reaction coefficients utilized to execute the reactions. Responsiveness to simulated lipopolysaccharide (LPS) administration was measured by tumor necrosis factor (TNF) production. Simulation runs included evaluation of initial dose-dependent response to LPS administration at 10, 100, 1000 and 10,000, and a subsequent examination of preconditioning behavior with increasing LPS at 10, 100, 1000 and 10,000 and a secondary dose of LPS at 10,000 administered at approximately 27h of simulated time. Simulations of 'knockout' versions of the model allowed further examination of the interactions within the signaling cascade. The model demonstrated a dose-dependent TNF response curve to increasing stimulus by LPS. Preconditioning simulations demonstrated a similar dose-dependency of preconditioning doses leading to attenuation of response to subsequent LPS challenge - a 'tolerance' dynamic. These responses match dynamics reported in the literature. Furthermore, the simulated 'knockout' results suggested the existence and need for dual negative feedback control mechanisms, represented by the zinc ring-finger protein A20 and inhibitor kappa B proteins (IkappaB), in order for both effective attenuation of the initial stimulus signal and subsequent preconditioned 'tolerant' behavior. We present an example of detailed, qualitative dynamic knowledge representation using the TLR-4 signaling pathway, its control mechanisms and overall behavior with respect to preconditioning. The intent of this approach is to demonstrate a method of translating the extensive mechanistic knowledge being generated at the basic science level into an executable framework that can provide a means of 'conceptual model verification.' This allows for both the 'checking' of the dynamic consequences of a mechanistic hypothesis and the creation of a modular component of an overall model directed at the engineering goal of biomedical research. It is hoped that this paper will increase the use of knowledge representation and communication in this fashion, and facilitate the concatenation and integration of community-wide knowledge.

Of mental models, assumptions and heuristics: The case of acids and acid strength

NASA Astrophysics Data System (ADS)

McClary, Lakeisha Michelle

This study explored what cognitive resources (i.e., units of knowledge necessary to learn) first-semester organic chemistry students used to make decisions about acid strength and how those resources guided the prediction, explanation and justification of trends in acid strength. We were specifically interested in the identifying and characterizing the mental models, assumptions and heuristics that students relied upon to make their decisions, in most cases under time constraints. The views about acids and acid strength were investigated for twenty undergraduate students. Data sources for this study included written responses and individual interviews. The data was analyzed using a qualitative methodology to answer five research questions. Data analysis regarding these research questions was based on existing theoretical frameworks: problem representation (Chi, Feltovich & Glaser, 1981), mental models (Johnson-Laird, 1983); intuitive assumptions (Talanquer, 2006), and heuristics (Evans, 2008). These frameworks were combined to develop the framework from which our data were analyzed. Results indicated that first-semester organic chemistry students' use of cognitive resources was complex and dependent on their understanding of the behavior of acids. Expressed mental models were generated using prior knowledge and assumptions about acids and acid strength; these models were then employed to make decisions. Explicit and implicit features of the compounds in each task mediated participants' attention, which triggered the use of a very limited number of heuristics, or shortcut reasoning strategies. Many students, however, were able to apply more effortful analytic reasoning, though correct trends were predicted infrequently. Most students continued to use their mental models, assumptions and heuristics to explain a given trend in acid strength and to justify their predicted trends, but the tasks influenced a few students to shift from one model to another model. An emergent finding from this project was that the problem representation greatly influenced students' ability to make correct predictions in acid strength. Many students, however, were able to apply more effortful analytic reasoning, though correct trends were predicted infrequently. Most students continued to use their mental models, assumptions and heuristics to explain a given trend in acid strength and to justify their predicted trends, but the tasks influenced a few students to shift from one model to another model. An emergent finding from this project was that the problem representation greatly influenced students' ability to make correct predictions in acid strength.

Iconicity as structure mapping

PubMed Central

Emmorey, Karen

2014-01-01

Linguistic and psycholinguistic evidence is presented to support the use of structure-mapping theory as a framework for understanding effects of iconicity on sign language grammar and processing. The existence of structured mappings between phonological form and semantic mental representations has been shown to explain the nature of metaphor and pronominal anaphora in sign languages. With respect to processing, it is argued that psycholinguistic effects of iconicity may only be observed when the task specifically taps into such structured mappings. In addition, language acquisition effects may only be observed when the relevant cognitive abilities are in place (e.g. the ability to make structural comparisons) and when the relevant conceptual knowledge has been acquired (i.e. information key to processing the iconic mapping). Finally, it is suggested that iconicity is better understood as a structured mapping between two mental representations than as a link between linguistic form and human experience. PMID:25092669

Integrating knowledge representation and quantitative modelling in physiology.

PubMed

de Bono, Bernard; Hunter, Peter

2012-08-01

A wealth of potentially shareable resources, such as data and models, is being generated through the study of physiology by computational means. Although in principle the resources generated are reusable, in practice, few can currently be shared. A key reason for this disparity stems from the lack of consistent cataloguing and annotation of these resources in a standardised manner. Here, we outline our vision for applying community-based modelling standards in support of an automated integration of models across physiological systems and scales. Two key initiatives, the Physiome Project and the European contribution - the Virtual Phsysiological Human Project, have emerged to support this multiscale model integration, and we focus on the role played by two key components of these frameworks, model encoding and semantic metadata annotation. We present examples of biomedical modelling scenarios (the endocrine effect of atrial natriuretic peptide, and the implications of alcohol and glucose toxicity) to illustrate the role that encoding standards and knowledge representation approaches, such as ontologies, could play in the management, searching and visualisation of physiology models, and thus in providing a rational basis for healthcare decisions and contributing towards realising the goal of of personalized medicine. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

How Pictorial Knowledge Representations Mediate Collaborative Knowledge Construction in Groups

ERIC Educational Resources Information Center

Naykki, Piia; Jarvela, Sanna

2008-01-01

This study investigates the process of collaborative knowledge construction when technology and pictorial knowledge representations are used for visualizing individual and groups' shared ideas. The focus of the study is on how teacher-students contribute to the group's collaborative knowledge construction and use each other's ideas and tools as an…

Beyond rules: The next generation of expert systems

NASA Technical Reports Server (NTRS)

Ferguson, Jay C.; Wagner, Robert E.

1987-01-01

The PARAGON Representation, Management, and Manipulation system is introduced. The concepts of knowledge representation, knowledge management, and knowledge manipulation are combined in a comprehensive system for solving real world problems requiring high levels of expertise in a real time environment. In most applications the complexity of the problem and the representation used to describe the domain knowledge tend to obscure the information from which solutions are derived. This inhibits the acquisition of domain knowledge verification/validation, places severe constraints on the ability to extend and maintain a knowledge base while making generic problem solving strategies difficult to develop. A unique hybrid system was developed to overcome these traditional limitations.

Students' Construction of External Representations in Design-Based Learning Situations

ERIC Educational Resources Information Center

de Vries, Erica

2006-01-01

This article develops a theoretical framework for the study of students' construction of mixed multiple external representations in design-based learning situations involving an adaptation of professional tasks and tools to a classroom setting. The framework draws on research on professional design processes and on learning with multiple external…

Designing and Evaluating Representations to Model Pedagogy

ERIC Educational Resources Information Center

Masterman, Elizabeth; Craft, Brock

2013-01-01

This article presents the case for a theory-informed approach to designing and evaluating representations for implementation in digital tools to support Learning Design, using the framework of epistemic efficacy as an example. This framework, which is rooted in the literature of cognitive psychology, is operationalised through dimensions of fit…

Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles

NASA Astrophysics Data System (ADS)

Cook, Michelle Patrick

2006-11-01

Visual representations are essential for communicating ideas in the science classroom; however, the design of such representations is not always beneficial for learners. This paper presents instructional design considerations providing empirical evidence and integrating theoretical concepts related to cognitive load. Learners have a limited working memory, and instructional representations should be designed with the goal of reducing unnecessary cognitive load. However, cognitive architecture alone is not the only factor to be considered; individual differences, especially prior knowledge, are critical in determining what impact a visual representation will have on learners' cognitive structures and processes. Prior knowledge can determine the ease with which learners can perceive and interpret visual representations in working memory. Although a long tradition of research has compared experts and novices, more research is necessary to fully explore the expert-novice continuum and maximize the potential of visual representations.

EliXR-TIME: A Temporal Knowledge Representation for Clinical Research Eligibility Criteria.

PubMed

Boland, Mary Regina; Tu, Samson W; Carini, Simona; Sim, Ida; Weng, Chunhua

2012-01-01

Effective clinical text processing requires accurate extraction and representation of temporal expressions. Multiple temporal information extraction models were developed but a similar need for extracting temporal expressions in eligibility criteria (e.g., for eligibility determination) remains. We identified the temporal knowledge representation requirements of eligibility criteria by reviewing 100 temporal criteria. We developed EliXR-TIME, a frame-based representation designed to support semantic annotation for temporal expressions in eligibility criteria by reusing applicable classes from well-known clinical temporal knowledge representations. We used EliXR-TIME to analyze a training set of 50 new temporal eligibility criteria. We evaluated EliXR-TIME using an additional random sample of 20 eligibility criteria with temporal expressions that have no overlap with the training data, yielding 92.7% (76 / 82) inter-coder agreement on sentence chunking and 72% (72 / 100) agreement on semantic annotation. We conclude that this knowledge representation can facilitate semantic annotation of the temporal expressions in eligibility criteria.

Online Multi-Modal Robust Non-Negative Dictionary Learning for Visual Tracking

PubMed Central

Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

2015-01-01

Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality. PMID:25961715

Online multi-modal robust non-negative dictionary learning for visual tracking.

PubMed

Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

2015-01-01

Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.

Representing Medical Knowledge in a Terminological Language is Difficult1

PubMed Central

Haimowits, Ira J.; Patil, Ramesh S.; Szolovits, Peter

1988-01-01

We report on an experiment to use a modern knowledge representation language, NIKL, to express the knowledge of a sophisticated medical reasoning program, ABEL. We are attempting to put the development of more capable medical programs on firmer representational grounds by moving from the ad hoc representations typical of current programs toward more principled representation languages now in use or under construction. Our experience with the project reported here suggests caution, however. Attempts at cleanliness and efficiency in the design of representation languages lead to a poverty of expressiveness that makes it difficult if not impossible to say in such languages what needs to be stated to support the application.

Enhancing a Pathway-Genome Database (PGDB) to capture subcellular localization of metabolites and enzymes: the nucleotide-sugar biosynthetic pathways of Populus trichocarpa.

PubMed

Nag, Ambarish; Karpinets, Tatiana V; Chang, Christopher H; Bar-Peled, Maor

2012-01-01

Understanding how cellular metabolism works and is regulated requires that the underlying biochemical pathways be adequately represented and integrated with large metabolomic data sets to establish a robust network model. Genetically engineering energy crops to be less recalcitrant to saccharification requires detailed knowledge of plant polysaccharide structures and a thorough understanding of the metabolic pathways involved in forming and regulating cell-wall synthesis. Nucleotide-sugars are building blocks for synthesis of cell wall polysaccharides. The biosynthesis of nucleotide-sugars is catalyzed by a multitude of enzymes that reside in different subcellular organelles, and precise representation of these pathways requires accurate capture of this biological compartmentalization. The lack of simple localization cues in genomic sequence data and annotations however leads to missing compartmentalization information for eukaryotes in automatically generated databases, such as the Pathway-Genome Databases (PGDBs) of the SRI Pathway Tools software that drives much biochemical knowledge representation on the internet. In this report, we provide an informal mechanism using the existing Pathway Tools framework to integrate protein and metabolite sub-cellular localization data with the existing representation of the nucleotide-sugar metabolic pathways in a prototype PGDB for Populus trichocarpa. The enhanced pathway representations have been successfully used to map SNP abundance data to individual nucleotide-sugar biosynthetic genes in the PGDB. The manually curated pathway representations are more conducive to the construction of a computational platform that will allow the simulation of natural and engineered nucleotide-sugar precursor fluxes into specific recalcitrant polysaccharide(s). Database URL: The curated Populus PGDB is available in the BESC public portal at http://cricket.ornl.gov/cgi-bin/beocyc_home.cgi and the nucleotide-sugar biosynthetic pathways can be directly accessed at http://cricket.ornl.gov:1555/PTR/new-image?object=SUGAR-NUCLEOTIDES.

Enhancing a Pathway-Genome Database (PGDB) to capture subcellular localization of metabolites and enzymes: the nucleotide-sugar biosynthetic pathways of Populus trichocarpa

PubMed Central

Nag, Ambarish; Karpinets, Tatiana V.; Chang, Christopher H.; Bar-Peled, Maor

2012-01-01

Understanding how cellular metabolism works and is regulated requires that the underlying biochemical pathways be adequately represented and integrated with large metabolomic data sets to establish a robust network model. Genetically engineering energy crops to be less recalcitrant to saccharification requires detailed knowledge of plant polysaccharide structures and a thorough understanding of the metabolic pathways involved in forming and regulating cell-wall synthesis. Nucleotide-sugars are building blocks for synthesis of cell wall polysaccharides. The biosynthesis of nucleotide-sugars is catalyzed by a multitude of enzymes that reside in different subcellular organelles, and precise representation of these pathways requires accurate capture of this biological compartmentalization. The lack of simple localization cues in genomic sequence data and annotations however leads to missing compartmentalization information for eukaryotes in automatically generated databases, such as the Pathway-Genome Databases (PGDBs) of the SRI Pathway Tools software that drives much biochemical knowledge representation on the internet. In this report, we provide an informal mechanism using the existing Pathway Tools framework to integrate protein and metabolite sub-cellular localization data with the existing representation of the nucleotide-sugar metabolic pathways in a prototype PGDB for Populus trichocarpa. The enhanced pathway representations have been successfully used to map SNP abundance data to individual nucleotide-sugar biosynthetic genes in the PGDB. The manually curated pathway representations are more conducive to the construction of a computational platform that will allow the simulation of natural and engineered nucleotide-sugar precursor fluxes into specific recalcitrant polysaccharide(s). Database URL: The curated Populus PGDB is available in the BESC public portal at http://cricket.ornl.gov/cgi-bin/beocyc_home.cgi and the nucleotide-sugar biosynthetic pathways can be directly accessed at http://cricket.ornl.gov:1555/PTR/new-image?object=SUGAR-NUCLEOTIDES. PMID:22465851

VALUE - A Framework to Validate Downscaling Approaches for Climate Change Studies

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilke, Renate A. I.

2015-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. Here, we present the key ingredients of this framework. VALUE's main approach to validation is user-focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies.

VALUE: A framework to validate downscaling approaches for climate change studies

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilcke, Renate A. I.

2015-01-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. In this paper, we present the key ingredients of this framework. VALUE's main approach to validation is user- focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies.

Representation and Exchange of Knowledge as a Basis of Information Processes. Proceedings of the International Research Forum in Information Science (5th, Heidelberg, West Germany, September 5-7, 1983).

ERIC Educational Resources Information Center

Dietschmann, Hans, Ed.

This 22-paper collection addresses a variety of issues related to representation and transfer of knowledge. Individual papers include an explanation of the usefulness of general scientific models versus case-specific approaches and a discussion of different empirical approaches to the general problem of knowledge representation for information…

Origins and early development of human body knowledge.

PubMed

Slaughter, Virginia; Heron, Michelle

2004-01-01

As a knowable object, the human body is highly complex. Evidence from several converging lines of research, including psychological studies, neuroimaging and clinical neuropsychology, indicates that human body knowledge is widely distributed in the adult brain, and is instantiated in at least three partially independent levels of representation. Sensorimotor body knowledge is responsible for on-line control and movement of one's own body and may also contribute to the perception of others' moving bodies; visuo-spatial body knowledge specifies detailed structural descriptions of the spatial attributes of the human body; and lexical-semantic body knowledge contains language-based knowledge about the human body. In the first chapter of this Monograph, we outline the evidence for these three hypothesized levels of human body knowledge, then review relevant literature on infants' and young children's human body knowledge in terms of the three-level framework. In Chapters II and III, we report two complimentary series of studies that specifically investigate the emergence of visuo-spatial body knowledge in infancy. Our technique is to compare infants'responses to typical and scrambled human bodies, in order to evaluate when and how infants acquire knowledge about the canonical spatial layout of the human body. Data from a series of visual habituation studies indicate that infants first discriminate scrambled from typical human body picture sat 15 to 18 months of age. Data from object examination studies similarly indicate that infants are sensitive to violations of three-dimensional human body stimuli starting at 15-18 months of age. The overall pattern of data supports several conclusions about the early development of human body knowledge: (a) detailed visuo-spatial knowledge about the human body is first evident in the second year of life, (b) visuo-spatial knowledge of human faces and human bodies are at least partially independent in infancy and (c) infants' initial visuo-spatial human body representations appear to be highly schematic, becoming more detailed and specific with development. In the final chapter, we explore these conclusions and discuss how levels of body knowledge may interact in early development.

Teacher spatial skills are linked to differences in geometry instruction.

PubMed

Otumfuor, Beryl Ann; Carr, Martha

2017-12-01

Spatial skills have been linked to better performance in mathematics. The purpose of this study was to examine the relationship between teacher spatial skills and their instruction, including teacher content and pedagogical knowledge, use of pictorial representations, and use of gestures during geometry instruction. Fifty-six middle school teachers participated in the study. The teachers were administered spatial measures of mental rotations and spatial visualization. Next, a single geometry class was videotaped. Correlational analyses revealed that spatial skills significantly correlate with teacher's use of representational gestures and content and pedagogical knowledge during instruction of geometry. Spatial skills did not independently correlate with the use of pointing gestures or the use of pictorial representations. However, an interaction term between spatial skills and content and pedagogical knowledge did correlate significantly with the use of pictorial representations. Teacher experience as measured by the number of years of teaching and highest degree did not appear to affect the relationships among the variables with the exception of the relationship between spatial skills and teacher content and pedagogical knowledge. Teachers with better spatial skills are also likely to use representational gestures and to show better content and pedagogical knowledge during instruction. Spatial skills predict pictorial representation use only as a function of content and pedagogical knowledge. © 2017 The British Psychological Society.

Comparative analysis of knowledge representation and reasoning requirements across a range of life sciences textbooks.

PubMed

Chaudhri, Vinay K; Elenius, Daniel; Goldenkranz, Andrew; Gong, Allison; Martone, Maryann E; Webb, William; Yorke-Smith, Neil

2014-01-01

Using knowledge representation for biomedical projects is now commonplace. In previous work, we represented the knowledge found in a college-level biology textbook in a fashion useful for answering questions. We showed that embedding the knowledge representation and question-answering abilities in an electronic textbook helped to engage student interest and improve learning. A natural question that arises from this success, and this paper's primary focus, is whether a similar approach is applicable across a range of life science textbooks. To answer that question, we considered four different textbooks, ranging from a below-introductory college biology text to an advanced, graduate-level neuroscience textbook. For these textbooks, we investigated the following questions: (1) To what extent is knowledge shared between the different textbooks? (2) To what extent can the same upper ontology be used to represent the knowledge found in different textbooks? (3) To what extent can the questions of interest for a range of textbooks be answered by using the same reasoning mechanisms? Our existing modeling and reasoning methods apply especially well both to a textbook that is comparable in level to the text studied in our previous work (i.e., an introductory-level text) and to a textbook at a lower level, suggesting potential for a high degree of portability. Even for the overlapping knowledge found across the textbooks, the level of detail covered in each textbook was different, which requires that the representations must be customized for each textbook. We also found that for advanced textbooks, representing models and scientific reasoning processes was particularly important. With some additional work, our representation methodology would be applicable to a range of textbooks. The requirements for knowledge representation are common across textbooks, suggesting that a shared semantic infrastructure for the life sciences is feasible. Because our representation overlaps heavily with those already being used for biomedical ontologies, this work suggests a natural pathway to include such representations as part of the life sciences curriculum at different grade levels.

Implementation of workflow engine technology to deliver basic clinical decision support functionality.

PubMed

Huser, Vojtech; Rasmussen, Luke V; Oberg, Ryan; Starren, Justin B

2011-04-10

Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. We describe an implementation of a free workflow technology software suite (available at http://code.google.com/p/healthflow) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform.

Mental representation of normal subjects about the sources of knowledge in different semantic categories and unique entities.

PubMed

Gainotti, Guido; Ciaraffa, Francesca; Silveri, Maria Caterina; Marra, Camillo

2009-11-01

According to the "sensory-motor model of semantic knowledge," different categories of knowledge differ for the weight that different "sources of knowledge" have in their representation. Our study aimed to evaluate this model, checking if subjective evaluations given by normal subjects confirm the different weight that various sources of knowledge have in the representation of different biological and artifact categories and of unique entities, such as famous people or monuments. Results showed that the visual properties are considered as the main source of knowledge for all the living and nonliving categories (as well as for unique entities), but that the clustering of these "sources of knowledge" is different for biological and artifacts categories. Visual data are, indeed, mainly associated with other perceptual (auditory, olfactory, gustatory, and tactual) attributes in the mental representation of living beings and unique entities, whereas they are associated with action-related properties and tactile information in the case of artifacts.

Conceptualizations of postpartum depression by public-sector health care providers in Mexico.

PubMed

Place, Jean Marie S; Billings, Deborah L; Blake, Christine E; Frongillo, Edward A; Mann, Joshua R; deCastro, Filipa

2015-04-01

In this article we describe the knowledge frameworks that 61 physicians, nurses, social workers, and psychologists from five public-sector health care facilities in Mexico used to conceptualize postpartum depression. We also demonstrate how providers applied social and behavioral antecedents in their conceptualizations of postpartum depression. Using grounded theory, we identify two frameworks that providers used to conceptualize postpartum depression: biochemical and adjustment. We highlight an emerging model of the function of social and behavioral antecedents within the frameworks, as well as the representation of postpartum depression by symptoms of distress and the perception among providers that these symptoms affected responsibilities associated with motherhood. The results provide a foundation for future study of how providers' conceptualizations of postpartum depression might affect detection and treatment practices and might be useful in the development of training materials to enhance the quality of care for women who experience any form of distress in the postpartum period. © The Author(s) 2014.

Simulation of the microwave heating of a thin multilayered composite material: A parameter analysis

NASA Astrophysics Data System (ADS)

Tertrais, Hermine; Barasinski, Anaïs; Chinesta, Francisco

2018-05-01

Microwave (MW) technology relies on volumetric heating. Thermal energy is transferred to the material that can absorb it at specific frequencies. The complex physics involved in this process is far from being understood and that is why a simulation tool has been developed in order to solve the electromagnetic and thermal equations in such a complex material as a multilayered composite part. The code is based on the in-plane-out-of-plane separated representation within the Proper Generalized Decomposition framework. To improve the knowledge on the process, a parameter study in carried out in this paper.

Automatic Overset Grid Generation with Heuristic Feedback Control

NASA Technical Reports Server (NTRS)

Robinson, Peter I.

2001-01-01

An advancing front grid generation system for structured Overset grids is presented which automatically modifies Overset structured surface grids and control lines until user-specified grid qualities are achieved. The system is demonstrated on two examples: the first refines a space shuttle fuselage control line until global truncation error is achieved; the second advances, from control lines, the space shuttle orbiter fuselage top and fuselage side surface grids until proper overlap is achieved. Surface grids are generated in minutes for complex geometries. The system is implemented as a heuristic feedback control (HFC) expert system which iteratively modifies the input specifications for Overset control line and surface grids. It is developed as an extension of modern control theory, production rules systems and subsumption architectures. The methodology provides benefits over the full knowledge lifecycle of an expert system for knowledge acquisition, knowledge representation, and knowledge execution. The vector/matrix framework of modern control theory systematically acquires and represents expert system knowledge. Missing matrix elements imply missing expert knowledge. The execution of the expert system knowledge is performed through symbolic execution of the matrix algebra equations of modern control theory. The dot product operation of matrix algebra is generalized for heuristic symbolic terms. Constant time execution is guaranteed.

The interaction of representation and reasoning

PubMed Central

Bundy, Alan

2013-01-01

Automated reasoning is an enabling technology for many applications of informatics. These applications include verifying that a computer program meets its specification; enabling a robot to form a plan to achieve a task and answering questions by combining information from diverse sources, e.g. on the Internet, etc. How is automated reasoning possible? Firstly, knowledge of a domain must be stored in a computer, usually in the form of logical formulae. This knowledge might, for instance, have been entered manually, retrieved from the Internet or perceived in the environment via sensors, such as cameras. Secondly, rules of inference are applied to old knowledge to derive new knowledge. Automated reasoning techniques have been adapted from logic, a branch of mathematics that was originally designed to formalize the reasoning of humans, especially mathematicians. My special interest is in the way that representation and reasoning interact. Successful reasoning is dependent on appropriate representation of both knowledge and successful methods of reasoning. Failures of reasoning can suggest changes of representation. This process of representational change can also be automated. We will illustrate the automation of representational change by drawing on recent work in my research group. PMID:24062623

Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets

DTIC Science & Technology

2015-04-24

Feature Representations usingProbabilistic Quadtrees and Deep Belief Nets Learning sparse feature representations is a useful instru- ment for solving an...novel framework for the classifi cation of handwritten digits that learns sparse representations using probabilistic quadtrees and Deep Belief Nets... Learning Sparse Feature Representations usingProbabilistic Quadtrees and Deep Belief Nets Report Title Learning sparse feature representations is a useful

Knowledge Representation and Ontologies

NASA Astrophysics Data System (ADS)

Grimm, Stephan

Knowledge representation and reasoning aims at designing computer systems that reason about a machine-interpretable representation of the world. Knowledge-based systems have a computational model of some domain of interest in which symbols serve as surrogates for real world domain artefacts, such as physical objects, events, relationships, etc. [1]. The domain of interest can cover any part of the real world or any hypothetical system about which one desires to represent knowledge for com-putational purposes. A knowledge-based system maintains a knowledge base, which stores the symbols of the computational model in the form of statements about the domain, and it performs reasoning by manipulating these symbols. Applications can base their decisions on answers to domain-relevant questions posed to a knowledge base.

Segmentation of medical images using explicit anatomical knowledge

NASA Astrophysics Data System (ADS)

Wilson, Laurie S.; Brown, Stephen; Brown, Matthew S.; Young, Jeanne; Li, Rongxin; Luo, Suhuai; Brandt, Lee

1999-07-01

Knowledge-based image segmentation is defined in terms of the separation of image analysis procedures and representation of knowledge. Such architecture is particularly suitable for medical image segmentation, because of the large amount of structured domain knowledge. A general methodology for the application of knowledge-based methods to medical image segmentation is described. This includes frames for knowledge representation, fuzzy logic for anatomical variations, and a strategy for determining the order of segmentation from the modal specification. This method has been applied to three separate problems, 3D thoracic CT, chest X-rays and CT angiography. The application of the same methodology to such a range of applications suggests a major role in medical imaging for segmentation methods incorporating representation of anatomical knowledge.

Student Teachers' Knowledge about Chemical Representations

ERIC Educational Resources Information Center

Taskin, Vahide; Bernholt, Sascha; Parchmann, Ilka

2017-01-01

Chemical representations serve as a communication tool not only in exchanges between scientists but also in chemistry lessons. The goals of the present study were to measure the extent of student teachers' knowledge about chemical representations, focusing on chemical formulae and structures in particular, and to explore which factors related to…

Examining the Task and Knowledge Demands Needed to Teach with Representations

ERIC Educational Resources Information Center

Mitchell, Rebecca; Charalambous, Charalambos Y.; Hill, Heather C.

2014-01-01

Representations are often used in instruction to highlight key mathematical ideas and support student learning. Despite their centrality in scaffolding teaching and learning, most of our understanding about the tasks involved with using representations in instruction and the knowledge requirements imposed on teachers when using these aids is…

Interleaved Practice with Multiple Representations: Analyses with Knowledge Tracing Based Techniques

ERIC Educational Resources Information Center

Rau, Martina A.; Pardos, Zachary A.

2012-01-01

The goal of this paper is to use Knowledge Tracing to augment the results obtained from an experiment that investigated the effects of practice schedules using an intelligent tutoring system for fractions. Specifically, this experiment compared different practice schedules of multiple representations of fractions: representations were presented to…

Using Concrete & Representational Experiences to Understand the Structure of DNA: A Four-Step Instructional Framework

ERIC Educational Resources Information Center

Harrell, Pamela Esprivalo; Richards, Debbie; Collins, James; Taylor, Sarah

2005-01-01

A description of learning experience that uses a four-step instrumentational framework involving concrete and representational experiences to promote conceptual understanding of abstract biological concepts by a series of closely-related activities is presented. The students are introduced to the structure and implications of DNA using four…

Optimization and Quantization in Gradient Symbol Systems: A Framework for Integrating the Continuous and the Discrete in Cognition

ERIC Educational Resources Information Center

Smolensky, Paul; Goldrick, Matthew; Mathis, Donald

2014-01-01

Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The…

Optimization and quantization in gradient symbol systems: a framework for integrating the continuous and the discrete in cognition.

PubMed

Smolensky, Paul; Goldrick, Matthew; Mathis, Donald

2014-08-01

Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, have both combinatorial and gradient structure. They are processed through Subsymbolic Optimization-Quantization, in which an optimization process favoring representations that satisfy well-formedness constraints operates in parallel with a distributed quantization process favoring discrete symbolic structures. We apply a particular instantiation of this framework, λ-Diffusion Theory, to phonological production. Simulations of the resulting model suggest that Gradient Symbol Processing offers a way to unify accounts of grammatical competence with both discrete and continuous patterns in language performance. Copyright © 2013 Cognitive Science Society, Inc.

NetWeaver for EMDS user guide (version 1.1): a knowledge base development system.

Treesearch

Keith M. Reynolds

1999-01-01

The guide describes use of the NetWeaver knowledge base development system. Knowledge representation in NetWeaver is based on object-oriented fuzzy-logic networks that offer several significant advantages over the more traditional rulebased representation. Compared to rule-based knowledge bases, NetWeaver knowledge bases are easier to build, test, and maintain because...

Conceptualizations of Representation Forms and Knowledge Organization of High School Teachers in Finland: "Magnetostatics"

ERIC Educational Resources Information Center

Majidi, Sharareh; Emden, Markus

2013-01-01

One of the main components of teachers' pedagogical content knowledge refers to their use of representation forms. In a similar vein, organizing concepts logically and meaningfully is an essential element of teachers' subject matter knowledge. Since subject matter and pedagogical content knowledge of teachers are tightly connected as categories…

The Underrepresentation of Women in the Engineering Element of STEM Occupations and Influencers Contributing to the Persistent Gap

NASA Astrophysics Data System (ADS)

Holl, David

Within Science, Technology, Engineering, and Mathematics (STEM) careers fields, the representation of women remains at an inequitable level when compared to men and to women's representation in other professions. Given the current state of women representing 52% of the professional and management-related workforce (U.S. Bureau of Labor and Statistics, 2015), their representation at only 15% of employed engineers nationwide appears to be a problem. When considering the fact that recent graduation data show women earn over 19% of Bachelor's degrees in engineering each year, the low number becomes increasingly puzzling. What factors are contributing to this low number of women in engineering professions? One of the contributing factors is clearly women's choice of education and career paths. However, empirical literature suggests, after pursuing and entering the engineer profession, women often are victim to gender schema, cognitive bias, and an absence of family-friendly work policies, an insufficient number of female mentors, social exclusion, and other drivers potentially leading to their higher turnover rate compared to their male counterparts. This project looks within one military-related organization to uncover reasons for the low representation of female engineers. The combination of a mixed-methods approach to data collection and the Knowledge, Motivation, and Organization (KMO) framework developed by Clark and Estes (2008) for analysis is employed by this project. Comparison of the analysis results to widely accepted learning and motivation principles presented in the reviewed literature led to a proposal of research-based solutions to address the representation gap and ultimately increase women's representation in engineering and other STEM career fields.

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning.

PubMed

Zhao, Chao; Jiang, Jingchi; Guan, Yi; Guo, Xitong; He, Bin

2018-05-01

Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS tasks-test recommendation, initial diagnosis, and treatment plan recommendation-given the condition of a patient. We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a record. Three bipartite subgraphs (bigraphs) were extracted from the EMKN, one to support each task. One part of the bigraph was the given condition (e.g., symptoms), and the other was the condition to be inferred (e.g., diseases). Each bigraph was regarded as a Markov random field (MRF) to support the inference. We proposed three graph-based energy functions and three likelihood-based energy functions. Two of these functions are based on knowledge representation learning and can provide distributed representations of medical entities. Two EMR datasets and three metrics were utilized to evaluate the performance. As a whole, the evaluation results indicate that the proposed system outperformed the baseline methods. The distributed representation of medical entities does reflect similarity relationships with respect to knowledge level. Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require individually designed energy functions. Copyright © 2018 Elsevier B.V. All rights reserved.

From needs to goals and representations: Foundations for a unified theory of motivation, personality, and development.

PubMed

Dweck, Carol S

2017-11-01

Drawing on both classic and current approaches, I propose a theory that integrates motivation, personality, and development within one framework, using a common set of principles and mechanisms. The theory begins by specifying basic needs and by suggesting how, as people pursue need-fulfilling goals, they build mental representations of their experiences (beliefs, representations of emotions, and representations of action tendencies). I then show how these needs, goals, and representations can serve as the basis of both motivation and personality, and can help to integrate disparate views of personality. The article builds on this framework to provide a new perspective on development, particularly on the forces that propel development and the roles of nature and nurture. I argue throughout that the focus on representations provides an important entry point for change and growth. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

EXPECT: Explicit Representations for Flexible Acquisition

NASA Technical Reports Server (NTRS)

Swartout, BIll; Gil, Yolanda

1995-01-01

To create more powerful knowledge acquisition systems, we not only need better acquisition tools, but we need to change the architecture of the knowledge based systems we create so that their structure will provide better support for acquisition. Current acquisition tools permit users to modify factual knowledge but they provide limited support for modifying problem solving knowledge. In this paper, the authors argue that this limitation (and others) stem from the use of incomplete models of problem-solving knowledge and inflexible specification of the interdependencies between problem-solving and factual knowledge. We describe the EXPECT architecture which addresses these problems by providing an explicit representation for problem-solving knowledge and intent. Using this more explicit representation, EXPECT can automatically derive the interdependencies between problem-solving and factual knowledge. By deriving these interdependencies from the structure of the knowledge-based system itself EXPECT supports more flexible and powerful knowledge acquisition.

A sensemaking perspective on framing the mental picture of air traffic controllers.

PubMed

Malakis, Stathis; Kontogiannis, Tom

2013-03-01

It has long been recognized that controller strategies are based on a 'mental picture' or representation of traffic situations. Earlier studies indicated that controllers tend to maintain a selective representation of traffic flows based on a few salient traffic features that point out to interesting events (e.g., potential conflicts). A field study is presented in this paper that examines salient features or 'knowledge variables' that constitute the building blocks of controller mental pictures. Verbal reports from participants, a field experiment and observations of real-life scenarios provided insights into the cognitive processes that shape and reframe the mental pictures of controllers. Several cognitive processes (i.e., problem detection, elaboration, reframing and replanning) have been explored within a particular framework of sensemaking stemming from the data/frame theory (Klein et al., 2007). Cognitive maps, representing standard and non-standard air traffic flows, emerged as an explanatory framework for making sense of traffic patterns and for reframing mental pictures. The data/frame theory proved to be a useful theoretical tool for investigating complex cognitive phenomena. The findings of the study have implications for the design of training curricula and decision support systems in air traffic control systems. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Gesture as Representational Action: A paper about function

PubMed Central

Novack, Miriam A.; Goldin-Meadow, Susan

2016-01-01

A great deal of attention has recently been paid to gesture and its effects on thinking and learning. It is well established that the hand movements that accompany speech are an integral part of communication, ubiquitous across cultures, and a unique feature of human behavior. In an attempt to understand this intriguing phenomenon, researchers have focused on pinpointing the mechanisms that underlie gesture production. One proposal—that gesture arises from simulated action (see Hostetter & Alibali, 2008)—has opened up discussions about action, gesture, and the relation between the two. However, there is another side to understanding a phenomenon, and that is to understand its function. A phenomenon’s function is its purpose rather than its precipitating cause—the why rather than the how. This paper sets forth a theoretical framework for exploring why gesture serves the functions that it does, and reviews where the current literature fits, and fails to fit, this proposal. Our framework proposes that whether or not gesture is simulated action in terms of its mechanism—it is clearly not reducible to action in terms of its function. Most notably, because gestures are abstracted representations and are not actions tied to particular events and objects, they can play a powerful role in thinking and learning beyond the particular, specifically, in supporting generalization and transfer of knowledge. PMID:27604493

Beyond Re/Presentation: A Case for Updating the Epistemology of Schooling

ERIC Educational Resources Information Center

Biesta, Gert J. J.; Osberg, Deborah

2007-01-01

In this paper we wish to argue that despite strong challenges to representational epistemology in the last two centuries, modern schooling is still organised around a representational view of knowledge. This is the case despite teaching practices being modified to accommodate different views of knowledge that have emerged in the last two…

Why Use Multiple Representations in the Mathematics Classroom? Views of English and German Preservice Teachers

ERIC Educational Resources Information Center

Dreher, Anika; Kuntze, Sebastian; Lerman, Stephen

2016-01-01

Dealing with multiple representations and their connections plays a key role for learners to build up conceptual knowledge in the mathematics classroom. Hence, professional knowledge and views of mathematics teachers regarding the use of multiple representations certainly merit attention. In particular, investigating such views of preservice…

Knowledge-based vision and simple visual machines.

PubMed Central

Cliff, D; Noble, J

1997-01-01

The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong. PMID:9304684

Representation and matching of knowledge to design digital systems

NASA Technical Reports Server (NTRS)

Jones, J. U.; Shiva, S. G.

1988-01-01

A knowledge-based expert system is described that provides an approach to solve a problem requiring an expert with considerable domain expertise and facts about available digital hardware building blocks. To design digital hardware systems from their high level VHDL (Very High Speed Integrated Circuit Hardware Description Language) representation to their finished form, a special data representation is required. This data representation as well as the functioning of the overall system is described.

Arctic indigenous peoples as representations and representatives of climate change.

PubMed

Martello, Marybeth Long

2008-06-01

Recent scientific findings, as presented in the Arctic Climate Impact Assessment (ACIA), indicate that climate change in the Arctic is happening now, at a faster rate than elsewhere in the world, and with major implications for peoples of the Arctic (especially indigenous peoples) and the rest of the planet. This paper examines scientific and political representations of Arctic indigenous peoples that have been central to the production and articulation of these claims. ACIA employs novel forms and strategies of representation that reflect changing conceptual models and practices of global change science and depict indigenous peoples as expert, exotic, and at-risk. These portrayals emerge alongside the growing political activism of Arctic indigenous peoples who present themselves as representatives or embodiments of climate change itself as they advocate for climate change mitigation policies. These mutually constitutive forms of representation suggest that scientific ways of seeing the global environment shape and are shaped by the public image and voice of global citizens. Likewise, the authority, credibility, and visibility of Arctic indigenous activists derive, in part, from their status as at-risk experts, a status buttressed by new scientific frameworks and methods that recognize and rely on the local experiences and knowledges of indigenous peoples. Analyses of these relationships linking scientific and political representations of Arctic climate change build upon science and technology studies (STS) scholarship on visualization, challenge conventional notions of globalization, and raise questions about power and accountability in global climate change research.

Use of Representation Mapping to Capture Abstraction in Problem Solving in Different Courses in Chemistry

ERIC Educational Resources Information Center

Sevian, H.; Bernholt, S.; Szteinberg, G. A.; Auguste, S.; Pérez, L. C.

2015-01-01

A perspective is presented on how the representation mapping framework by Hahn and Chater (1998) may be used to characterize reasoning during problem solving in chemistry. To provide examples for testing the framework, an exploratory study was conducted with students and professors from three different courses in the middle of the undergraduate…

A Hybrid Constraint Representation and Reasoning Framework

NASA Technical Reports Server (NTRS)

Golden, Keith; Pang, Wan-Lin

2003-01-01

This paper introduces JNET, a novel constraint representation and reasoning framework that supports procedural constraints and constraint attachments, providing a flexible way of integrating the constraint reasoner with a run- time software environment. Attachments in JNET are constraints over arbitrary Java objects, which are defined using Java code, at runtime, with no changes to the JNET source code.

A Model of Factors Determining Students' Ability to Interpret External Representations in Biochemistry

ERIC Educational Resources Information Center

Schonborn, Konrad J.; Anderson, Trevor R.

2009-01-01

The aim of this research was to develop a model of factors affecting students' ability to interpret external representations (ERs) in biochemistry. The study was qualitative in design and was guided by the modelling framework of Justi and Gilbert. Application of the process outlined by the framework, and consultation with relevant literature, led…

Research on knowledge representation, machine learning, and knowledge acquisition

NASA Technical Reports Server (NTRS)

Buchanan, Bruce G.

1987-01-01

Research in knowledge representation, machine learning, and knowledge acquisition performed at Knowledge Systems Lab. is summarized. The major goal of the research was to develop flexible, effective methods for representing the qualitative knowledge necessary for solving large problems that require symbolic reasoning as well as numerical computation. The research focused on integrating different representation methods to describe different kinds of knowledge more effectively than any one method can alone. In particular, emphasis was placed on representing and using spatial information about three dimensional objects and constraints on the arrangement of these objects in space. Another major theme is the development of robust machine learning programs that can be integrated with a variety of intelligent systems. To achieve this goal, learning methods were designed, implemented and experimented within several different problem solving environments.

On a categorial aspect of knowledge representation

NASA Astrophysics Data System (ADS)

Tataj, Emanuel; Mulawka, Jan; Nieznański, Edward

Adequate representation of data is crucial for modeling any type of data. To faithfully present and describe the relevant section of the world it is necessary to select the method that can easily be implemented on a computer system which will help in further description allowing reasoning. The main objective of this contribution is to present methods of knowledge representation using categorial approach. Next to identify the main advantages for computer implementation. Categorical aspect of knowledge representation is considered in semantic networks realisation. Such method borrows already known metaphysics properties for data modeling process. The potential topics of further development of categorical semantic networks implementations are also underlined.

An application of object-oriented knowledge representation to engineering expert systems

NASA Technical Reports Server (NTRS)

Logie, D. S.; Kamil, H.; Umaretiya, J. R.

1990-01-01

The paper describes an object-oriented knowledge representation and its application to engineering expert systems. The object-oriented approach promotes efficient handling of the problem data by allowing knowledge to be encapsulated in objects and organized by defining relationships between the objects. An Object Representation Language (ORL) was implemented as a tool for building and manipulating the object base. Rule-based knowledge representation is then used to simulate engineering design reasoning. Using a common object base, very large expert systems can be developed, comprised of small, individually processed, rule sets. The integration of these two schemes makes it easier to develop practical engineering expert systems. The general approach to applying this technology to the domain of the finite element analysis, design, and optimization of aerospace structures is discussed.

The Bobath (NDT) concept in adult neurological rehabilitation: what is the state of the knowledge? A scoping review. Part II: intervention studies perspectives.

PubMed

Vaughan-Graham, Julie; Cott, Cheryl; Wright, F Virginia

2015-01-01

The study's purpose was to describe the range of knowledge pertaining to the Bobath (NDT) concept in adult neurological rehabilitation, synthesizes the findings, identify knowledge gaps and develop empirically based recommendations for future research. A scoping review of research and non-research articles published from 2007 to 2012. Two independent reviewers selected studies based on a systematic procedure. Inclusion criteria for studies were electronically accessible English language literature with Bobath and/or Neurodevelopmental Therapy as the subject heading in the title/keyword/abstract/intervention comparison with respect to adult neurological conditions. Data were abstracted and summarized with respect to study design, theoretical framework, clinical application including population representation, study fidelity, intervention comparison, duration of care, measurement and findings. Of the 33 publications identified 17 were intervention studies (11 RCT's/1 prospective parallel group design/5 N-of-1). One other paper was a systematic review. The intervention studies, primarily RCT designs, have serious methodological concerns particularly related to study/treatment fidelity and measurement resulting in no clear clinical direction. Aspects such as theoretical framework, therapist skill, quality of movement measurement and individualized interventions require careful consideration in the design of Bobath studies. Implications for Rehabilitation Future intervention studies should be based on the current Bobath theoretical framework and key aspects of clinical practice. Study and treatment fidelity issues need to be carefully considered when interpreting the results of existing RCT's evaluating the Bobath concept. N-of-1 randomized, observational, factorial and mixed method study designs should be considered as alternative study options.

A knowledge base of the chemical compounds of intermediary metabolism.

PubMed

Karp, P D

1992-08-01

This paper describes a publicly available knowledge base of the chemical compounds involved in intermediary metabolism. We consider the motivations for constructing a knowledge base of metabolic compounds, the methodology by which it was constructed, and the information that it currently contains. Currently the knowledge base describes 981 compounds, listing for each: synonyms for its name, a systematic name, CAS registry number, chemical formula, molecular weight, chemical structure and two-dimensional display coordinates for the structure. The Compound Knowledge Base (CompoundKB) illustrates several methodological principles that should guide the development of biological knowledge bases. I argue that biological datasets should be made available in multiple representations to increase their accessibility to end users, and I present multiple representations of the CompoundKB (knowledge base, relational data base and ASN. 1 representations). I also analyze the general characteristics of these representations to provide an understanding of their relative advantages and disadvantages. Another principle is that the error rate of biological data bases should be estimated and documented-this analysis is performed for the CompoundKB.

A development framework for distributed artificial intelligence

NASA Technical Reports Server (NTRS)

Adler, Richard M.; Cottman, Bruce H.

1989-01-01

The authors describe distributed artificial intelligence (DAI) applications in which multiple organizations of agents solve multiple domain problems. They then describe work in progress on a DAI system development environment, called SOCIAL, which consists of three primary language-based components. The Knowledge Object Language defines models of knowledge representation and reasoning. The metaCourier language supplies the underlying functionality for interprocess communication and control access across heterogeneous computing environments. The metaAgents language defines models for agent organization coordination, control, and resource management. Application agents and agent organizations will be constructed by combining metaAgents and metaCourier building blocks with task-specific functionality such as diagnostic or planning reasoning. This architecture hides implementation details of communications, control, and integration in distributed processing environments, enabling application developers to concentrate on the design and functionality of the intelligent agents and agent networks themselves.

Characterizing representational learning: A combined simulation and tutorial on perturbation theory

NASA Astrophysics Data System (ADS)

Kohnle, Antje; Passante, Gina

2017-12-01

Analyzing, constructing, and translating between graphical, pictorial, and mathematical representations of physics ideas and reasoning flexibly through them ("representational competence") is a key characteristic of expertise in physics but is a challenge for learners to develop. Interactive computer simulations and University of Washington style tutorials both have affordances to support representational learning. This article describes work to characterize students' spontaneous use of representations before and after working with a combined simulation and tutorial on first-order energy corrections in the context of quantum-mechanical time-independent perturbation theory. Data were collected from two institutions using pre-, mid-, and post-tests to assess short- and long-term gains. A representational competence level framework was adapted to devise level descriptors for the assessment items. The results indicate an increase in the number of representations used by students and the consistency between them following the combined simulation tutorial. The distributions of representational competence levels suggest a shift from perceptual to semantic use of representations based on their underlying meaning. In terms of activity design, this study illustrates the need to support students in making sense of the representations shown in a simulation and in learning to choose the most appropriate representation for a given task. In terms of characterizing representational abilities, this study illustrates the usefulness of a framework focusing on perceptual, syntactic, and semantic use of representations.

Examining the Influences of Epistemic Beliefs and Knowledge Representations on Cognitive Processing and Conceptual Change When Learning Physics

ERIC Educational Resources Information Center

Franco, Gina M.; Muis, Krista R.; Kendeou, Panayiota; Ranellucci, John; Sampasivam, Lavanya; Wang, Xihui

2012-01-01

The purpose of this study was to investigate the role of epistemic beliefs and knowledge representations in cognitive and metacognitive processing when learning about physics concepts through text. Specifically, we manipulated the representation of physics concepts in texts about Newtonian mechanics and explored how these texts interacted with…

Mobile Knowledge, Karma Points and Digital Peers: The Tacit Epistemology and Linguistic Representation of MOOCs

ERIC Educational Resources Information Center

Portmess, Lisa

2013-01-01

Media representations of massive open online courses (MOOCs) such as those offered by Coursera, edX and Udacity reflect tension and ambiguity in their bold promise of democratized education and global knowledge sharing. An approach to MOOCs that emphasizes the tacit epistemology of such representations suggests a richer account of the ambiguities…

Making Connections among Multiple Visual Representations: How Do Sense-Making Skills and Perceptual Fluency Relate to Learning of Chemistry Knowledge?

ERIC Educational Resources Information Center

Rau, Martina A.

2018-01-01

To learn content knowledge in science, technology, engineering, and math domains, students need to make connections among visual representations. This article considers two kinds of connection-making skills: (1) "sense-making skills" that allow students to verbally explain mappings among representations and (2) "perceptual…

Knowledge representation by connection matrices: A method for the on-board implementation of large expert systems

NASA Technical Reports Server (NTRS)

Kellner, A.

1987-01-01

Extremely large knowledge sources and efficient knowledge access characterizing future real-life artificial intelligence applications represent crucial requirements for on-board artificial intelligence systems due to obvious computer time and storage constraints on spacecraft. A type of knowledge representation and corresponding reasoning mechanism is proposed which is particularly suited for the efficient processing of such large knowledge bases in expert systems.

Plants in silico: why, why now and what?--an integrative platform for plant systems biology research.

PubMed

Zhu, Xin-Guang; Lynch, Jonathan P; LeBauer, David S; Millar, Andrew J; Stitt, Mark; Long, Stephen P

2016-05-01

A paradigm shift is needed and timely in moving plant modelling from largely isolated efforts to a connected community endeavour that can take full advantage of advances in computer science and in mechanistic understanding of plant processes. Plants in silico (Psi) envisions a digital representation of layered dynamic modules, linking from gene networks and metabolic pathways through to cellular organization, tissue, organ and whole plant development, together with resource capture and use efficiency in dynamic competitive environments, ultimately allowing a mechanistically rich simulation of the plant or of a community of plants in silico. The concept is to integrate models or modules from different layers of organization spanning from genome to phenome to ecosystem in a modular framework allowing the use of modules of varying mechanistic detail representing the same biological process. Developments in high-performance computing, functional knowledge of plants, the internet and open-source version controlled software make achieving the concept realistic. Open source will enhance collaboration and move towards testing and consensus on quantitative theoretical frameworks. Importantly, Psi provides a quantitative knowledge framework where the implications of a discovery at one level, for example, single gene function or developmental response, can be examined at the whole plant or even crop and natural ecosystem levels. © 2015 The Authors Plant, Cell & Environment Published by John Wiley & Sons Ltd.

Does GEM-encoding clinical practice guidelines improve the quality of knowledge bases? A study with the rule-based formalism.

PubMed

Georg, Georg; Séroussi, Brigitte; Bouaud, Jacques

2003-01-01

The aim of this work was to determine whether the GEM-encoding step could improve the representation of clinical practice guidelines as formalized knowledge bases. We used the 1999 Canadian recommendations for the management of hypertension, chosen as the knowledge source in the ASTI project. We first clarified semantic ambiguities of therapeutic sequences recommended in the guideline by proposing an interpretative framework of therapeutic strategies. Then, after a formalization step to standardize the terms used to characterize clinical situations, we created the GEM-encoded instance of the guideline. We developed a module for the automatic derivation of a rule base, BR-GEM, from the instance. BR-GEM was then compared to the rule base, BR-ASTI, embedded within the critic mode of ASTI, and manually built by two physicians from the same Canadian guideline. As compared to BR-ASTI, BR-GEM is more specific and covers more clinical situations. When evaluated on 10 patient cases, the GEM-based approach led to promising results.

Domain adaptation via transfer component analysis.

PubMed

Pan, Sinno Jialin; Tsang, Ivor W; Kwok, James T; Yang, Qiang

2011-02-01

Domain adaptation allows knowledge from a source domain to be transferred to a different but related target domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we first propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a reproducing kernel Hilbert space using maximum mean miscrepancy. In the subspace spanned by these transfer components, data properties are preserved and data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. Furthermore, in order to uncover the knowledge hidden in the relations between the data labels from the source and target domains, we extend TCA in a semisupervised learning setting, which encodes label information into transfer components learning. We call this extension semisupervised TCA. The main contribution of our work is that we propose a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation. We propose both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce the distance between domain distributions by projecting data onto the learned transfer components. Finally, our approach can handle large datasets and naturally lead to out-of-sample generalization. The effectiveness and efficiency of our approach are verified by experiments on five toy datasets and two real-world applications: cross-domain indoor WiFi localization and cross-domain text classification.

Supervised Learning for Dynamical System Learning.

PubMed

Hefny, Ahmed; Downey, Carlton; Gordon, Geoffrey J

2015-01-01

Recently there has been substantial interest in spectral methods for learning dynamical systems. These methods are popular since they often offer a good tradeoff between computational and statistical efficiency. Unfortunately, they can be difficult to use and extend in practice: e.g., they can make it difficult to incorporate prior information such as sparsity or structure. To address this problem, we present a new view of dynamical system learning: we show how to learn dynamical systems by solving a sequence of ordinary supervised learning problems, thereby allowing users to incorporate prior knowledge via standard techniques such as L 1 regularization. Many existing spectral methods are special cases of this new framework, using linear regression as the supervised learner. We demonstrate the effectiveness of our framework by showing examples where nonlinear regression or lasso let us learn better state representations than plain linear regression does; the correctness of these instances follows directly from our general analysis.

Hierarchical semantic structures for medical NLP.

PubMed

Taira, Ricky K; Arnold, Corey W

2013-01-01

We present a framework for building a medical natural language processing (NLP) system capable of deep understanding of clinical text reports. The framework helps developers understand how various NLP-related efforts and knowledge sources can be integrated. The aspects considered include: 1) computational issues dealing with defining layers of intermediate semantic structures to reduce the dimensionality of the NLP problem; 2) algorithmic issues in which we survey the NLP literature and discuss state-of-the-art procedures used to map between various levels of the hierarchy; and 3) implementation issues to software developers with available resources. The objective of this poster is to educate readers to the various levels of semantic representation (e.g., word level concepts, ontological concepts, logical relations, logical frames, discourse structures, etc.). The poster presents an architecture for which diverse efforts and resources in medical NLP can be integrated in a principled way.

Interoperability between phenotype and anatomy ontologies.

PubMed

Hoehndorf, Robert; Oellrich, Anika; Rebholz-Schuhmann, Dietrich

2010-12-15

Phenotypic information is important for the analysis of the molecular mechanisms underlying disease. A formal ontological representation of phenotypic information can help to identify, interpret and infer phenotypic traits based on experimental findings. The methods that are currently used to represent data and information about phenotypes fail to make the semantics of the phenotypic trait explicit and do not interoperate with ontologies of anatomy and other domains. Therefore, valuable resources for the analysis of phenotype studies remain unconnected and inaccessible to automated analysis and reasoning. We provide a framework to formalize phenotypic descriptions and make their semantics explicit. Based on this formalization, we provide the means to integrate phenotypic descriptions with ontologies of other domains, in particular anatomy and physiology. We demonstrate how our framework leads to the capability to represent disease phenotypes, perform powerful queries that were not possible before and infer additional knowledge. http://bioonto.de/pmwiki.php/Main/PheneOntology.

Autonomous mental development with selective attention, object perception, and knowledge representation

NASA Astrophysics Data System (ADS)

Ban, Sang-Woo; Lee, Minho

2008-04-01

Knowledge-based clustering and autonomous mental development remains a high priority research topic, among which the learning techniques of neural networks are used to achieve optimal performance. In this paper, we present a new framework that can automatically generate a relevance map from sensory data that can represent knowledge regarding objects and infer new knowledge about novel objects. The proposed model is based on understating of the visual what pathway in our brain. A stereo saliency map model can selectively decide salient object areas by additionally considering local symmetry feature. The incremental object perception model makes clusters for the construction of an ontology map in the color and form domains in order to perceive an arbitrary object, which is implemented by the growing fuzzy topology adaptive resonant theory (GFTART) network. Log-polar transformed color and form features for a selected object are used as inputs of the GFTART. The clustered information is relevant to describe specific objects, and the proposed model can automatically infer an unknown object by using the learned information. Experimental results with real data have demonstrated the validity of this approach.

MedRapid--medical community & business intelligence system.

PubMed

Finkeissen, E; Fuchs, H; Jakob, T; Wetter, T

2002-01-01

currently, it takes at least 6 months for researchers to communicate their results. This delay is caused (a) by partial lacks of machine support for both representation as well as communication and (b) by media breaks during the communication process. To make an integrated communication between researchers and practitioners possible, a general structure for medical content representation has been set up. The procedure for data entry and quality management has been generalized and implemented in a web-based authoring system. The MedRapid-system supports the medical experts in entering their knowledge into a database. Here, the level of detail is still below that of current medical guidelines representation. However, the symmetric structure for an area-wide medical knowledge representation is highly retrievable and thus can quickly be communicated into daily routine for the improvement of the treatment quality. In addition, other sources like journal articles and medical guidelines can be references within the MedRapid-system and thus be communicated into daily routine. The fundamental system for the representation of medical reference knowledge (from reference works/books) itself is not sufficient for the friction-less communication amongst medical staff. Rather, the process of (a) representing medical knowledge, (b) refereeing the represented knowledge, (c) communicating the represented knowledge, and (d) retrieving the represented knowledge has to be unified. MedRapid will soon support the whole process on one server system.

Interoception beyond homeostasis: affect, cognition and mental health.

PubMed

Tsakiris, Manos; Critchley, Hugo

2016-11-19

Interoception refers to the sensing of the internal state of one's body. Interoception is distinct from the processing of sensory information concerning external (non-self) stimuli (e.g. vision, hearing, touch and smell) and is the afferent axis to internal (autonomic and hormonal) physiological control. However, the impact of interoception extends beyond homeostatic/allostatic reflexes: it is proposed to be fundamental to motivation, emotion (affective feelings and behaviours), social cognition and self-awareness. This view is supported by a growing body of experimental evidence that links peripheral physiological states to mental processes. Within this framework, the representation of self is constructed from early development through continuous integrative representation of biological data from the body, to form the basis for those aspects of conscious awareness grounded on the subjective sense of being a unique individual. This theme issue of the Philosophical Transactions of the Royal Society B draws together state-of-the-art knowledge concerning theoretical, experimental and clinical facets of interoception with the emphasis on cognitive and affective neuroscience. The multidisciplinary and cross-disciplinary perspectives represented in this theme issue disseminate and entrench knowledge about interoception across the scientific community and provide a reference for the conceptualization and further study of interoception across behavioural sciences. © 2016 The Author(s).

Ontology Design of Influential People Identification Using Centrality

NASA Astrophysics Data System (ADS)

Maulana Awangga, Rolly; Yusril, Muhammad; Setyawan, Helmi

2018-04-01

Identifying influential people as a node in a graph theory commonly calculated by social network analysis. The social network data has the user as node and edge as relation forming a friend relation graph. This research is conducting different meaning of every nodes relation in the social network. Ontology was perfect match science to describe the social network data as conceptual and domain. Ontology gives essential relationship in a social network more than a current graph. Ontology proposed as a standard for knowledge representation for the semantic web by World Wide Web Consortium. The formal data representation use Resource Description Framework (RDF) and Web Ontology Language (OWL) which is strategic for Open Knowledge-Based website data. Ontology used in the semantic description for a relationship in the social network, it is open to developing semantic based relationship ontology by adding and modifying various and different relationship to have influential people as a conclusion. This research proposes a model using OWL and RDF for influential people identification in the social network. The study use degree centrality, between ness centrality, and closeness centrality measurement for data validation. As a conclusion, influential people identification in Facebook can use proposed Ontology model in the Group, Photos, Photo Tag, Friends, Events and Works data.

The M-Factor, a Bilingual Asset for Plurilinguals? Learners' Representations, Discourse Strategies and Third Language Acquisition in Institutional Contexts

ERIC Educational Resources Information Center

Bono, Mariana; Stratilaki, Sofia

2009-01-01

Within the framework of our research on learners' language practices and representations, this contribution explores how their representations about language uses and language learning shape the processes of learning and communication in school settings. More precisely, we will study learners' representations regarding the existence of a…

Representation and presentation of requirements knowledge

NASA Technical Reports Server (NTRS)

Johnson, W. L.; Feather, Martin S.; Harris, David R.

1992-01-01

An approach to representation and presentation of knowledge used in the ARIES, an experimental requirements/specification environment, is described. The approach applies the notion of a representation architecture to the domain of software engineering and incorporates a strong coupling to a transformation system. It is characterized by a single highly expressive underlying representation, interfaced simultaneously to multiple presentations, each with notations of differing degrees of expressivity. This enables analysts to use multiple languages for describing systems and have these descriptions yield a single consistent model of the system.

Influence of the Digital Anatomist Foundational Model on traditional representations of anatomical concepts.

PubMed

Agoncillo, A V; Mejino, J L; Rosse, C

1999-01-01

A principled and logical representation of the structure of the human body has led to conflicts with traditional representations of the same knowledge by anatomy textbooks. The examples which illustrate resolution of these conflicts suggest that stricter requirements must be met for semantic consistency, expressivity and specificity by knowledge sources intended to support inference than by textbooks and term lists. These next-generation resources should influence traditional concept representation, rather than be constrained by convention.

A Framework for Re-thinking Learning in Science from Recent Cognitive Science Perspectives

NASA Astrophysics Data System (ADS)

Tytler, Russell; Prain, Vaughan

2010-10-01

Recent accounts by cognitive scientists of factors affecting cognition imply the need to reconsider current dominant conceptual theories about science learning. These new accounts emphasize the role of context, embodied practices, and narrative-based representation rather than learners' cognitive constructs. In this paper we analyse data from a longitudinal study of primary school children's learning to outline a framework based on these contemporary accounts and to delineate key points of difference from conceptual change perspectives. The findings suggest this framework provides strong theoretical and practical insights into how children learn and the key role of representational negotiation in this learning. We argue that the nature and process of conceptual change can be re-interpreted in terms of the development of students' representational resources.

How to Make a Good Animation: A Grounded Cognition Model of How Visual Representation Design Affects the Construction of Abstract Physics Knowledge

ERIC Educational Resources Information Center

Chen, Zhongzhou; Gladding, Gary

2014-01-01

Visual representations play a critical role in teaching physics. However, since we do not have a satisfactory understanding of how visual perception impacts the construction of abstract knowledge, most visual representations used in instructions are either created based on existing conventions or designed according to the instructor's intuition,…

Knowledge representation and management: transforming textual information into useful knowledge.

PubMed

Rassinoux, A-M

2010-01-01

To summarize current outstanding research in the field of knowledge representation and management. Synopsis of the articles selected for the IMIA Yearbook 2010. Four interesting papers, dealing with structured knowledge, have been selected for the section knowledge representation and management. Combining the newest techniques in computational linguistics and natural language processing with the latest methods in statistical data analysis, machine learning and text mining has proved to be efficient for turning unstructured textual information into meaningful knowledge. Three of the four selected papers for the section knowledge representation and management corroborate this approach and depict various experiments conducted to .extract meaningful knowledge from unstructured free texts such as extracting cancer disease characteristics from pathology reports, or extracting protein-protein interactions from biomedical papers, as well as extracting knowledge for the support of hypothesis generation in molecular biology from the Medline literature. Finally, the last paper addresses the level of formally representing and structuring information within clinical terminologies in order to render such information easily available and shareable among the health informatics community. Delivering common powerful tools able to automatically extract meaningful information from the huge amount of electronically unstructured free texts is an essential step towards promoting sharing and reusability across applications, domains, and institutions thus contributing to building capacities worldwide.

The Representation of Object-Directed Action and Function Knowledge in the Human Brain

PubMed Central

Chen, Quanjing; Garcea, Frank E.; Mahon, Bradford Z.

2016-01-01

The appropriate use of everyday objects requires the integration of action and function knowledge. Previous research suggests that action knowledge is represented in frontoparietal areas while function knowledge is represented in temporal lobe regions. Here we used multivoxel pattern analysis to investigate the representation of object-directed action and function knowledge while participants executed pantomimes of familiar tool actions. A novel approach for decoding object knowledge was used in which classifiers were trained on one pair of objects and then tested on a distinct pair; this permitted a measurement of classification accuracy over and above object-specific information. Region of interest (ROI) analyses showed that object-directed actions could be decoded in tool-preferring regions of both parietal and temporal cortex, while no independently defined tool-preferring ROI showed successful decoding of object function. However, a whole-brain searchlight analysis revealed that while frontoparietal motor and peri-motor regions are engaged in the representation of object-directed actions, medial temporal lobe areas in the left hemisphere are involved in the representation of function knowledge. These results indicate that both action and function knowledge are represented in a topographically coherent manner that is amenable to study with multivariate approaches, and that the left medial temporal cortex represents knowledge of object function. PMID:25595179

Psychology of knowledge representation.

PubMed

Grimm, Lisa R

2014-05-01

Every cognitive enterprise involves some form of knowledge representation. Humans represent information about the external world and internal mental states, like beliefs and desires, and use this information to meet goals (e.g., classification or problem solving). Unfortunately, researchers do not have direct access to mental representations. Instead, cognitive scientists design experiments and implement computational models to develop theories about the mental representations present during task performance. There are several main types of mental representation and corresponding processes that have been posited: spatial, feature, network, and structured. Each type has a particular structure and a set of processes that are capable of accessing and manipulating information within the representation. The structure and processes determine what information can be used during task performance and what information has not been represented at all. As such, the different types of representation are likely used to solve different kinds of tasks. For example, structured representations are more complex and computationally demanding, but are good at representing relational information. Researchers interested in human psychology would benefit from considering how knowledge is represented in their domain of inquiry. For further resources related to this article, please visit the WIREs website. The author has declared no conflicts of interest for this article. © 2014 John Wiley & Sons, Ltd.

Sources of Error in Substance Use Prevalence Surveys

PubMed Central

Johnson, Timothy P.

2014-01-01

Population-based estimates of substance use patterns have been regularly reported now for several decades. Concerns with the quality of the survey methodologies employed to produce those estimates date back almost as far. Those concerns have led to a considerable body of research specifically focused on understanding the nature and consequences of survey-based errors in substance use epidemiology. This paper reviews and summarizes that empirical research by organizing it within a total survey error model framework that considers multiple types of representation and measurement errors. Gaps in our knowledge of error sources in substance use surveys and areas needing future research are also identified. PMID:27437511

Geovisualization to support the exploration of large health and demographic survey data

PubMed Central

Koua, Etien L; Kraak, Menno-Jan

2004-01-01

Background Survey data are increasingly abundant from many international projects and national statistics. They are generally comprehensive and cover local, regional as well as national levels census in many domains including health, demography, human development, and economy. These surveys result in several hundred indicators. Geographical analysis of such large amount of data is often a difficult task and searching for patterns is particularly a difficult challenge. Geovisualization research is increasingly dealing with the exploration of patterns and relationships in such large datasets for understanding underlying geographical processes. One of the attempts has been to use Artificial Neural Networks as a technology especially useful in situations where the numbers are vast and the relationships are often unclear or even hidden. Results We investigate ways to integrate computational analysis based on a Self-Organizing Map neural network, with visual representations of derived structures and patterns in a framework for exploratory visualization to support visual data mining and knowledge discovery. The framework suggests ways to explore the general structure of the dataset in its multidimensional space in order to provide clues for further exploration of correlations and relationships. Conclusion In this paper, the proposed framework is used to explore a demographic and health survey data. Several graphical representations (information spaces) are used to depict the general structure and clustering of the data and get insight about the relationships among the different variables. Detail exploration of correlations and relationships among the attributes is provided. Results of the analysis are also presented in maps and other graphics. PMID:15180898

32 CFR 776.20 - Competence.

Code of Federal Regulations, 2013 CFR

2013-07-01

..., and prompt representation to a client. Competent representation requires the legal knowledge, skill, access to evidence, thoroughness, and expeditious preparation reasonably necessary for representation...

32 CFR 776.20 - Competence.

Code of Federal Regulations, 2012 CFR

2012-07-01

..., and prompt representation to a client. Competent representation requires the legal knowledge, skill, access to evidence, thoroughness, and expeditious preparation reasonably necessary for representation...

32 CFR 776.20 - Competence.

Code of Federal Regulations, 2011 CFR

2011-07-01

..., and prompt representation to a client. Competent representation requires the legal knowledge, skill, access to evidence, thoroughness, and expeditious preparation reasonably necessary for representation...

32 CFR 776.20 - Competence.

Code of Federal Regulations, 2014 CFR

2014-07-01

..., and prompt representation to a client. Competent representation requires the legal knowledge, skill, access to evidence, thoroughness, and expeditious preparation reasonably necessary for representation...

A Methodology for Multiple Rule System Integration and Resolution Within a Singular Knowledge Base

NASA Technical Reports Server (NTRS)

Kautzmann, Frank N., III

1988-01-01

Expert Systems which support knowledge representation by qualitative modeling techniques experience problems, when called upon to support integrated views embodying description and explanation, especially when other factors such as multiple causality, competing rule model resolution, and multiple uses of knowledge representation are included. A series of prototypes are being developed to demonstrate the feasibility of automating the process of systems engineering, design and configuration, and diagnosis and fault management. A study involves not only a generic knowledge representation; it must also support multiple views at varying levels of description and interaction between physical elements, systems, and subsystems. Moreover, it will involve models of description and explanation for each level. This multiple model feature requires the development of control methods between rule systems and heuristics on a meta-level for each expert system involved in an integrated and larger class of expert system. The broadest possible category of interacting expert systems is described along with a general methodology for the knowledge representation and control of mutually exclusive rule systems.

Visual Representation in Mathematics: Special Education Teachers' Knowledge and Emphasis for Instruction

ERIC Educational Resources Information Center

van Garderen, Delinda; Scheuermann, Amy; Poch, Apryl; Murray, Mary M.

2018-01-01

The use of visual representations (VRs) in mathematics is a strongly recommended practice in special education. Although recommended, little is known about special educators' knowledge of and instructional emphasis about VRs. Therefore, in this study, the authors examined special educators' own knowledge of and their instructional emphasis with…

Knowledge representation issues for explaining plans

NASA Technical Reports Server (NTRS)

Prince, Mary Ellen; Johannes, James D.

1988-01-01

Explanations are recognized as an important facet of intelligent behavior. Unfortunately, expert systems are currently limited in their ability to provide useful, intelligent justifications of their results. We are currently investigating the issues involved in providing explanation facilities for expert planning systems. This investigation addresses three issues: knowledge content, knowledge representation, and explanation structure.

What Does Knowledge Look Like? Drawing as a Means of Knowledge Representation and Knowledge Construction

ERIC Educational Resources Information Center

Bowen, Tracey; Evans, M. Max

2015-01-01

The most common tools individuals use to articulate complex and abstract concepts are writing and spoken language, long privileged as primary forms of communication. However, our, explanations of these concepts may be more aptly communicated through visual means, such as drawings. Interpreting and analyzing abstract graphic representations is…

The representation of semantic knowledge in a child with Williams syndrome.

PubMed

Robinson, Sally J; Temple, Christine M

2009-05-01

This study investigated whether there are distinct types of semantic knowledge with distinct representational bases during development. The representation of semantic knowledge in a teenage child (S.T.) with Williams syndrome was explored for the categories of animals, fruit, and vegetables, manipulable objects, and nonmanipulable objects. S.T.'s lexical stores were of a normal size but the volume of "sensory feature" semantic knowledge she generated in oral descriptions was reduced. In visual recognition decisions, S.T. made more false positives to nonitems than did controls. Although overall naming of pictures was unimpaired, S.T. exhibited a category-specific anomia for nonmanipulable objects and impaired naming of visual-feature descriptions of animals. S.T.'s performance was interpreted as reflecting the impaired integration of distinctive features from perceptual input, which may impact upon nonmanipulable objects to a greater extent than the other knowledge categories. Performance was used to inform adult-based models of semantic representation, with category structure proposed to emerge due to differing degrees of dependency upon underlying knowledge types, feature correlations, and the acquisition of information from modality-specific processing modules.

Composing Data Parallel Code for a SPARQL Graph Engine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Castellana, Vito G.; Tumeo, Antonino; Villa, Oreste

Big data analytics process large amount of data to extract knowledge from them. Semantic databases are big data applications that adopt the Resource Description Framework (RDF) to structure metadata through a graph-based representation. The graph based representation provides several benefits, such as the possibility to perform in memory processing with large amounts of parallelism. SPARQL is a language used to perform queries on RDF-structured data through graph matching. In this paper we present a tool that automatically translates SPARQL queries to parallel graph crawling and graph matching operations. The tool also supports complex SPARQL constructs, which requires more than basicmore » graph matching for their implementation. The tool generates parallel code annotated with OpenMP pragmas for x86 Shared-memory Multiprocessors (SMPs). With respect to commercial database systems such as Virtuoso, our approach reduces memory occupation due to join operations and provides higher performance. We show the scaling of the automatically generated graph-matching code on a 48-core SMP.« less

Improving information recognition and performance of recycling chimneys.

PubMed

Durugbo, Christopher

2013-01-01

The aim of this study was to assess and improve how recyclers (individuals carrying out the task of recycling) make use of visual cues to carryout recycling tasks in relation to 'recycling chimneys' (repositories for recycled waste). An initial task analysis was conducted through an activity sampling study and an eye tracking experiment using a mobile eye tracker to capture fixations of recyclers during recycling tasks. Following data collection using the eye tracker, a set of recommendations for improving information representation were then identified using the widely researched skills, rules, knowledge framework, and for a comparative study to assess the performance of improved interfaces for recycling chimneys based on Ecological Interface Design principles. Information representation on recycling chimneys determines how we recycle waste. This study describes an eco-ergonomics-based approach to improve the design of interfaces for recycling chimneys. The results are valuable for improving the performance of waste collection processes in terms of minimising contamination and increasing the quantity of recyclables.

Mind perception of God in Japanese children.

PubMed

Moriguchi, Yusuke; Takahashi, Hideyuki; Nakamata, Tomoko; Todo, Naoya

2018-03-05

There is a theoretical debate regarding whether children represent God with reference to a human. Most previous studies have assessed this issue focusing on knowledge/omniscience in western children. This study used a theoretical framework characterising mental capacities in terms of motivational/emotional (experience) and cognitive (agency) mental capacities and tested whether Japanese children discriminated between God, a human, a baby and an invisible agent according to these capacities. Three- to 6-year-old children were asked about the experience and agency of the agents. The results revealed that children discriminated God from a human in terms of mental capacities including experience and agency in 3-year-old children. On the other hand, 4- to 6-year-old children, but not 3-year-old children, discriminated a human from a baby and an invisible person. The results suggest that the Japanese children's representations of God differed from their representation of a human during preschool years. © 2018 International Union of Psychological Science.

Learning multisensory representations for auditory-visual transfer of sequence category knowledge: a probabilistic language of thought approach.

PubMed

Yildirim, Ilker; Jacobs, Robert A

2015-06-01

If a person is trained to recognize or categorize objects or events using one sensory modality, the person can often recognize or categorize those same (or similar) objects and events via a novel modality. This phenomenon is an instance of cross-modal transfer of knowledge. Here, we study the Multisensory Hypothesis which states that people extract the intrinsic, modality-independent properties of objects and events, and represent these properties in multisensory representations. These representations underlie cross-modal transfer of knowledge. We conducted an experiment evaluating whether people transfer sequence category knowledge across auditory and visual domains. Our experimental data clearly indicate that we do. We also developed a computational model accounting for our experimental results. Consistent with the probabilistic language of thought approach to cognitive modeling, our model formalizes multisensory representations as symbolic "computer programs" and uses Bayesian inference to learn these representations. Because the model demonstrates how the acquisition and use of amodal, multisensory representations can underlie cross-modal transfer of knowledge, and because the model accounts for subjects' experimental performances, our work lends credence to the Multisensory Hypothesis. Overall, our work suggests that people automatically extract and represent objects' and events' intrinsic properties, and use these properties to process and understand the same (and similar) objects and events when they are perceived through novel sensory modalities.

Visual Representations of Microcosm in Textbooks of Chemistry: Constructing a Systemic Network for Their Main Conceptual Framework

ERIC Educational Resources Information Center

Papageorgiou, George; Amariotakis, Vasilios; Spiliotopoulou, Vasiliki

2017-01-01

The main objective of this work is to analyse the visual representations (VRs) of the microcosm depicted in nine Greek secondary chemistry school textbooks of the last three decades in order to construct a systemic network for their main conceptual framework and to evaluate the contribution of each one of the resulting categories to the network.…

37 CFR 11.101 - Competence.

Code of Federal Regulations, 2013 CFR

2013-07-01

... competent representation to a client. Competent representation requires the legal, scientific, and technical knowledge, skill, thoroughness and preparation reasonably necessary for the representation. ... COMMERCE REPRESENTATION OF OTHERS BEFORE THE UNITED STATES PATENT AND TRADEMARK OFFICE USPTO Rules of...

37 CFR 11.101 - Competence.

Code of Federal Regulations, 2014 CFR

2014-07-01

... competent representation to a client. Competent representation requires the legal, scientific, and technical knowledge, skill, thoroughness and preparation reasonably necessary for the representation. ... COMMERCE REPRESENTATION OF OTHERS BEFORE THE UNITED STATES PATENT AND TRADEMARK OFFICE USPTO Rules of...

A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system.

PubMed

Chrysafiadi, Konstantina; Virvou, Maria

2013-12-01

In this paper a knowledge representation approach of an adaptive and/or personalized tutoring system is presented. The domain knowledge should be represented in a more realistic way in order to allow the adaptive and/or personalized tutoring system to deliver the learning material to each individual learner dynamically taking into account her/his learning needs and her/his different learning pace. To succeed this, the domain knowledge representation has to depict the possible increase or decrease of the learner's knowledge. Considering that the domain concepts that constitute the learning material are not independent from each other, the knowledge representation approach has to allow the system to recognize either the domain concepts that are already partly or completely known for a learner, or the domain concepts that s/he has forgotten, taking into account the learner's knowledge level of the related concepts. In other words, the system should be informed about the knowledge dependencies that exist among the domain concepts of the learning material, as well as the strength on impact of each domain concept on others. Fuzzy Cognitive Maps (FCMs) seem to be an ideal way for representing graphically this kind of information. The suggested knowledge representation approach has been implemented in an e-learning adaptive system for teaching computer programming. The particular system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus and was compared with a corresponding system, in which the domain knowledge was represented using the most common used technique of network of concepts. The results of the evaluation were very encouraging.

Shared knowledge or shared affordances? Insights from an ecological dynamics approach to team coordination in sports.

PubMed

Silva, Pedro; Garganta, Júlio; Araújo, Duarte; Davids, Keith; Aguiar, Paulo

2013-09-01

Previous research has proposed that team coordination is based on shared knowledge of the performance context, responsible for linking teammates' mental representations for collective, internalized action solutions. However, this representational approach raises many questions including: how do individual schemata of team members become reformulated together? How much time does it take for this collective cognitive process to occur? How do different cues perceived by different individuals sustain a general shared mental representation? This representational approach is challenged by an ecological dynamics perspective of shared knowledge in team coordination. We argue that the traditional shared knowledge assumption is predicated on 'knowledge about' the environment, which can be used to share knowledge and influence intentions of others prior to competition. Rather, during competitive performance, the control of action by perceiving surrounding informational constraints is expressed in 'knowledge of' the environment. This crucial distinction emphasizes perception of shared affordances (for others and of others) as the main communication channel between team members during team coordination tasks. From this perspective, the emergence of coordinated behaviours in sports teams is based on the formation of interpersonal synergies between players resulting from collective actions predicated on shared affordances.

Associations of Postural Knowledge and Basic Motor Skill with Dyspraxia in Autism: Implication for Abnormalities in Distributed Connectivity and Motor Learning

PubMed Central

Dowell, Lauren R.; Mahone, E. Mark; Mostofsky, Stewart H.

2009-01-01

Children with autism often have difficulty performing skilled movements. Praxis performance requires basic motor skill, knowledge of representations of the movement (mediated by parietal regions), and transcoding of these representations into movement plans (mediated by premotor circuits). The goals of this study were: (a) to determine whether dyspraxia in autism is associated with impaired representational (“postural”) knowledge, and (b) to examine the contributions of postural knowledge and basic motor skill to dyspraxia in autism. Thirty-seven children with autism spectrum disorder (ASD) and 50 typically developing (TD) children, ages 8–13, completed: (a) an examination of basic motor skills, (b) a postural knowledge test assessing praxis discrimination, and (c) a praxis examination. Children with ASD showed worse basic motor skill and postural knowledge than controls. The ASD group continued to show significantly poorer praxis than controls after accounting for age, IQ, basic motor skill, and postural knowledge. Dyspraxia in autism appears to be associated with impaired formation of spatial representations, as well as transcoding and execution. Distributed abnormality across parietal, premotor, and motor circuitry, as well as anomalous connectivity may be implicated. PMID:19702410

Operator assistant systems - An experimental approach using a telerobotics application

NASA Technical Reports Server (NTRS)

Boy, Guy A.; Mathe, Nathalie

1993-01-01

This article presents a knowledge-based system methodology for developing operator assistant (OA) systems in dynamic and interactive environments. This is a problem both of training and design, which is the subject of this article. Design includes both design of the system to be controlled and design of procedures for operating this system. A specific knowledge representation is proposed for representing the corresponding system and operational knowledge. This representation is based on the situation recognition and analytical reasoning paradigm. It tries to make explicit common factors involved in both human and machine intelligence, including perception and reasoning. An OA system based on this representation has been developed for space telerobotics. Simulations have been carried out with astronauts and the resulting protocols have been analyzed. Results show the relevance of the approach and have been used for improving the knowledge representation and the OA architecture.

Capturing and portraying science student teachers' pedagogical content knowledge through CoRe construction

NASA Astrophysics Data System (ADS)

Thongnoppakun, Warangkana; Yuenyong, Chokchai

2018-01-01

Pedagogical content knowledge (PCK) is an essential kind of knowledge that teacher have for teaching particular content to particular students for enhance students' understanding, therefore, teachers with adequate PCK can give content to their students in an understandable way rather than transfer subject matter knowledge to learner. This study explored science student teachers' PCK for teaching science using Content representation base methodology. Research participants were 68 4th year science student teachers from department of General Science, faculty of Education, Phuket Rajabhat University. PCK conceptualization for teaching science by Magnusson et al. (1999) was applied as a theoretical framework in this study. In this study, Content representation (CoRe) by Loughran et al. (2004) was employed as research methodology in the lesson preparation process. In addition, CoRe consisted of eight questions (CoRe prompts) that designed to elicit and portray teacher's PCK for teaching science. Data were collected from science student teachers' CoRes design for teaching a given topic and student grade. Science student teachers asked to create CoRes design for teaching in topic `Motion in one direction' for 7th grade student and further class discussion. Science student teachers mostly created a same group of science concepts according to subunits of school science textbook rather than planned and arranged content to support students' understanding. Furthermore, they described about the effect of student's prior knowledge and learning difficulties such as students' knowledge of Scalar and Vector quantity; and calculating skill. These responses portrayed science student teacher's knowledge of students' understanding of science and their content knowledge. However, they still have inadequate knowledge of instructional strategies and activities for enhance student learning. In summary, CoRes design can represented holistic overviews of science student teachers' PCK related to the teaching of a particular topic and also support them to gain more understanding about how to teach for understanding. Research implications are given for teacher education and educational research to offer a potential way to enhance science student teachers' PCK for teaching science and support their professional learning.

Knowledge inhibition and N400: a study with words that look like common words.

PubMed

Debruille, J B

1998-04-01

In addition to their own representations, low frequency words, such as BRIBE, can covertly activate the representations of higher frequency words they look like (e.g., BRIDE). Hence, look-alike words can activate knowledge that is incompatible with the knowledge corresponding to accurate representations. Comparatively, eccentric words, that is, low frequency words that do not look as much like higher frequency words, are less likely to activate incompatible knowledge. This study focuses on the hypothesis that the N400 component of the event-related potential reflects the inhibition of incompatible knowledge. This hypothesis predicts that look-alike words elicit N400s of greater amplitudes than eccentric words in conditions where incompatible knowledge is inhibited. Results from a single item lexical decision experiment are reported which support the inhibition hypothesis. Copyright 1998 Academic Press.

Revisiting Curriculum Inquiry: The Role of Visual Representations

ERIC Educational Resources Information Center

Eilam, Billie; Ben-Peretz, Miriam

2010-01-01

How do visual representations (VRs) in curriculum materials influence theoretical curriculum frameworks? Suggesting that VRs' integration into curriculum materials affords a different lens for perceiving and understanding the curriculum domain, this study draws on a curricular perspective in relation to multi-representations in texts rather than…

Mental Models in Expert Physics Reasoning.

ERIC Educational Resources Information Center

Roschelle, Jeremy; Greeno, James G.

Proposed is a relational framework for characterizing experienced physicists' representations of physics problem situations and the process of constructing these representations. A representation includes a coherent set of relations among: (1) a mental model of the objects in the situation, along with their relevant properties and relations; (2) a…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hill, David P.; D’Eustachio, Peter; Berardini, Tanya Z.

The concept of a biological pathway, an ordered sequence of molecular transformations, is used to collect and represent molecular knowledge for a broad span of organismal biology. Representations of biomedical pathways typically are rich but idiosyncratic presentations of organized knowledge about individual pathways. Meanwhile, biomedical ontologies and associated annotation files are powerful tools that organize molecular information in a logically rigorous form to support computational analysis. The Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways within its ontological representations. Here we present a methodology for extending and refining the classes inmore » the GO for more comprehensive, consistent and integrated representation of pathways, leveraging knowledge embedded in current pathway representations such as those in the Reactome Knowledgebase and MetaCyc. With carbohydrate metabolic pathways as a use case, we discuss how our representation supports the integration of variant pathway classes into a unified ontological structure that can be used for data comparison and analysis.« less

Origins of Secure Base Script Knowledge and the Developmental Construction of Attachment Representations

PubMed Central

Waters, Theodore E. A.; Ruiz, Sarah K.; Roisman, Glenn I.

2016-01-01

Increasing evidence suggests that attachment representations take at least two forms—a secure base script and an autobiographical narrative of childhood caregiving experiences. This study presents data from the first 26 years of the Minnesota Longitudinal Study of Risk and Adaptation (N = 169), examining the developmental origins of secure base script knowledge in a high-risk sample, and testing alternative models of the developmental sequencing of the construction of attachment representations. Results demonstrated that secure base script knowledge was predicted by observations of maternal sensitivity across childhood and adolescence. Further, findings suggest that the construction of a secure base script supports the development of a coherent autobiographical representation of childhood attachment experiences with primary caregivers by early adulthood. PMID:27302650

Methodological Developments in Geophysical Assimilation Modeling

NASA Astrophysics Data System (ADS)

Christakos, George

2005-06-01

This work presents recent methodological developments in geophysical assimilation research. We revisit the meaning of the term "solution" of a mathematical model representing a geophysical system, and we examine its operational formulations. We argue that an assimilation solution based on epistemic cognition (which assumes that the model describes incomplete knowledge about nature and focuses on conceptual mechanisms of scientific thinking) could lead to more realistic representations of the geophysical situation than a conventional ontologic assimilation solution (which assumes that the model describes nature as is and focuses on form manipulations). Conceptually, the two approaches are fundamentally different. Unlike the reasoning structure of conventional assimilation modeling that is based mainly on ad hoc technical schemes, the epistemic cognition approach is based on teleologic criteria and stochastic adaptation principles. In this way some key ideas are introduced that could open new areas of geophysical assimilation to detailed understanding in an integrated manner. A knowledge synthesis framework can provide the rational means for assimilating a variety of knowledge bases (general and site specific) that are relevant to the geophysical system of interest. Epistemic cognition-based assimilation techniques can produce a realistic representation of the geophysical system, provide a rigorous assessment of the uncertainty sources, and generate informative predictions across space-time. The mathematics of epistemic assimilation involves a powerful and versatile spatiotemporal random field theory that imposes no restriction on the shape of the probability distributions or the form of the predictors (non-Gaussian distributions, multiple-point statistics, and nonlinear models are automatically incorporated) and accounts rigorously for the uncertainty features of the geophysical system. In the epistemic cognition context the assimilation concept may be used to investigate critical issues related to knowledge reliability, such as uncertainty due to model structure error (conceptual uncertainty).

Approaching an Understanding of Omniscience from the Preschool Years to Early Adulthood

ERIC Educational Resources Information Center

Lane, Jonathan D.; Wellman, Henry M.; Evans, E. Margaret

2014-01-01

Individuals in many cultures believe in omniscient (all-knowing) beings, but everyday representations of omniscience have rarely been studied. To understand the nature of such representations requires knowing how they develop. Two studies examined the breadth of knowledge (i.e., types of knowledge) and depth of knowledge (i.e., amount of knowledge…

Preservice Secondary Mathematics Teachers' Development of Mathematical Knowledge for Teaching and Their Use of Knowledge in Their Instruction

ERIC Educational Resources Information Center

Moon, Kyunghee

2013-01-01

This study examined how preservice secondary mathematics teachers developed mathematical knowledge for teaching (MKT) around representations and big ideas through mathematics and mathematics education courses. The importance of big ideas and representations in mathematics has been emphasized in national standards as well as in literature. Yet,…

An Overview of OWL, a Language for Knowledge Representation.

ERIC Educational Resources Information Center

Szolovits, Peter; And Others

This is a description of the motivation and overall organization of the OWL language for knowledge representation. OWL consists of a linguistic memory system (LMS), a memory of concepts in terms of which all English phrases and all knowledge of an application domain are represented; a theory of English grammar which tells how to map English…

[Social and cultural representations in epilepsy awareness].

PubMed

Arborio, Sophie

2015-01-01

Representations relating to epilepsy have evolved over the centuries, but the manifestations of epilepsy awaken archaic images linked to death, violence and disgust. Indeed, the generalised epileptic seizure symbolises a rupture with the surrounding environment, "informs it", through the loss of social codes which it causes. The social and cultural context, as well as medical knowledge, influences the representations of the disease. As a result, popular knowledge is founded on the social and cultural representations of a given era, in a given society. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Concepts and Categories: A Cognitive Neuropsychological Perspective

PubMed Central

Mahon, Bradford Z.; Caramazza, Alfonso

2010-01-01

One of the most provocative and exciting issues in cognitive science is how neural specificity for semantic categories of common objects arises in the functional architecture of the brain. More than two decades of research on the neuropsychological phenomenon of category-specific semantic deficits has generated detailed claims about the organization and representation of conceptual knowledge. More recently, researchers have sought to test hypotheses developed on the basis of neuropsychological evidence with functional imaging. From those two fields, the empirical generalization emerges that object domain and sensory modality jointly constrain the organization of knowledge in the brain. At the same time, research within the embodied cognition framework has highlighted the need to articulate how information is communicated between the sensory and motor systems, and processes that represent and generalize abstract information. Those developments point toward a new approach for understanding category specificity in terms of the coordinated influences of diverse regions and cognitive systems. PMID:18767921

Using texts in science education: cognitive processes and knowledge representation.

PubMed

van den Broek, Paul

2010-04-23

Texts form a powerful tool in teaching concepts and principles in science. How do readers extract information from a text, and what are the limitations in this process? Central to comprehension of and learning from a text is the construction of a coherent mental representation that integrates the textual information and relevant background knowledge. This representation engenders learning if it expands the reader's existing knowledge base or if it corrects misconceptions in this knowledge base. The Landscape Model captures the reading process and the influences of reader characteristics (such as working-memory capacity, reading goal, prior knowledge, and inferential skills) and text characteristics (such as content/structure of presented information, processing demands, and textual cues). The model suggests factors that can optimize--or jeopardize--learning science from text.

Representations in Dynamical Embodied Agents: Re-Analyzing a Minimally Cognitive Model Agent

ERIC Educational Resources Information Center

Mirolli, Marco

2012-01-01

Understanding the role of "representations" in cognitive science is a fundamental problem facing the emerging framework of embodied, situated, dynamical cognition. To make progress, I follow the approach proposed by an influential representational skeptic, Randall Beer: building artificial agents capable of minimally cognitive behaviors and…

Students' Development and Use of Internal Representations When Solving Algebraic Tasks

ERIC Educational Resources Information Center

Cross, Laban J.

2013-01-01

The difficulty in observing, recording, and examining internal representations has been well documented (Goldin & Shteingold, 2001). However, the important role that these internal representations play in the learning and understanding of mathematical concepts has been noted (Yackel, 2000). This study sought to develop a framework for…

Stripping for the Wolf: Rethinking Representations of Gender in Children's Literature

ERIC Educational Resources Information Center

Marshall, Elizabeth

2004-01-01

This essay seeks to broaden theoretical paradigms commonly used in the social sciences to analyze representations of gender, especially girlhood, in children's literature. In particular, this project seeks to add to liberal feminist frameworks for conceptualizing textual representations of gender and sexuality in literacy studies. Liberal…

Towards a multilevel cognitive probabilistic representation of space

NASA Astrophysics Data System (ADS)

Tapus, Adriana; Vasudevan, Shrihari; Siegwart, Roland

2005-03-01

This paper addresses the problem of perception and representation of space for a mobile agent. A probabilistic hierarchical framework is suggested as a solution to this problem. The method proposed is a combination of probabilistic belief with "Object Graph Models" (OGM). The world is viewed from a topological optic, in terms of objects and relationships between them. The hierarchical representation that we propose permits an efficient and reliable modeling of the information that the mobile agent would perceive from its environment. The integration of both navigational and interactional capabilities through efficient representation is also addressed. Experiments on a set of images taken from the real world that validate the approach are reported. This framework draws on the general understanding of human cognition and perception and contributes towards the overall efforts to build cognitive robot companions.

Implementation of workflow engine technology to deliver basic clinical decision support functionality

PubMed Central

2011-01-01

Background Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. Results We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. Conclusions We describe an implementation of a free workflow technology software suite (available at http://code.google.com/p/healthflow) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform. PMID:21477364

[The fragmentation of representational space in schizophrenia].

PubMed

Plagnol, A; Oïta, M; Montreuil, M; Granger, B; Lubart, T

2003-01-01

Existent neurocognitive models of schizophrenia converge towards a core of impairments involving working memory, context processing, action planning, controlled and intentional processing. However, the emergence of this core remains itself difficult to explain and more specific hypotheses do not explain the heterogeneity of schizophrenia. To overcome these limits, we propose a new paradigm based on representational theory from cognitive science. Some recent developments of this theory enable us to describe a subjective universe as a representational space which is displayed from memory. We outline a conceptual framework to construct such a representational space from analogical -representations that can be activated in working memory and are connected to a network of symbolic structures. These connections are notably made through an analytic process of the analogical fragments, which involves the attentional focus. This framework allows us to define rigorously some defense processes in response to traumatic tensions that are expressed on the representational space. The fragmentation of representational space is a consequence of a defensive denial based on an impairment of the analytic process. The fragmentation forms some parasitic areas in memory which are excluded from the main part of the representational space and disturb information processing. The key clinical concepts of paranoid syndromes can be defined in this conceptual framework: mental automatism, delusional intuition, acute destructuration, psychotic dissociation, and autistic withdrawal. We show that these syndromes imply each other, which in return increases the fragmentation of the representational space. Some new concepts emerge naturally in this framework, such as the concept of "suture" which is defined as a link between a parasitic area and the main representational space. Schizophrenia appears as a borderline case of fragmentation of the representational space. This conceptual framework is compatible with numerous etiological factors. Multiple clinical forms can be differentiated in accordance with the persistence of parasitic areas, the degree of fragmentation, and the formation of sutures. We use this approach to account for an empirical study concerning the analysis of analogical representations in schizophrenia. We used the Parallel Visual Information Processing Test (PVIPT) which assesses the analysis of interfering visual information. Subjects were asked to connect several small geometric figures printed on a transparency. The transparency was displayed above four photographs which were the interfering material. Then, subjects completed three tasks concerning the photographs: a recognition task, a recall task, and an affective qualification task. Using a case-by-case study, this test allows us to access the defense processes of the subjects, which is not possible with the usual methods in cognitive psychopathology. Twelve clinically-stable schizophrenic subjects participated in the study which also included a self-assessment of alexithymia by the Toronto Alexithymia Scale. We obtained 2 main results: (a) creation of items in recall or false recognition by 8 subjects, and (b) lack of the usual -negative correlations between the alexithymia score and the recall, recognition and affective qualification scores in the PVIPT. These 2 results contrast with what has been previously observed for alexithymia using the same methodology. The result (a) confirms an interfering activation in schizophrenic memory, which can be interpreted in our framework as indicative of parasitic areas. The creation of items suggests the formation of sutures between the semantic content of photographs and some delusional fragments. The result (b) suggests that the apparent alexithymia in schizophrenia is a defense against interfering activation in parasitic areas. We underline the interest of individual protocols to exhibit the dynamic interplay between an interfering activity in memory and a defensive flattening of affects.

Understanding terminological systems. II: Experience with conceptual and formal representation of structure.

PubMed

de Keizer, N F; Abu-Hanna, A

2000-03-01

This article describes the application of two popular conceptual and formal representation formalisms, as part of a framework for understanding terminological systems. A precise understanding of the structure of a terminological system is essential to assess existing terminological systems, to recognize patterns in various systems and to build new terminological systems. Our experience with the application of this framework to five well-known terminological systems is described.

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal

PubMed Central

Ramkumar, Barathram; Sabarimalai Manikandan, M.

2017-01-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal. PMID:28529758

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal.

PubMed

Satija, Udit; Ramkumar, Barathram; Sabarimalai Manikandan, M

2017-02-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.

Representation in Memory.

ERIC Educational Resources Information Center

Rumelhart, David E.; Norman, Donald A.

This paper reviews work on the representation of knowledge from within psychology and artificial intelligence. The work covers the nature of representation, the distinction between the represented world and the representing world, and significant issues concerned with propositional, analogical, and superpositional representations. Specific topics…

Gender, sexuality and the discursive representation of access and equity in health services literature: implications for LGBT communities

PubMed Central

2011-01-01

Background This article considers how health services access and equity documents represent the problem of access to health services and what the effects of that representation might be for lesbian, gay, bisexual and transgender (LGBT) communities. We conducted a critical discourse analysis on selected access and equity documents using a gender-based diversity framework as determined by two objectives: 1) to identify dominant and counter discourses in health services access and equity literature; and 2) to develop understanding of how particular discourses impact the inclusion, or not, of LGBT communities in health services access and equity frameworks.The analysis was conducted in response to public health and clinical research that has documented barriers to health services access for LGBT communities including institutionalized heterosexism, biphobia, and transphobia, invisibility and lack of health provider knowledge and comfort. The analysis was also conducted as the first step of exploring LGBT access issues in home care services for LGBT populations in Ontario, Canada. Methods A critical discourse analysis of selected health services access and equity documents, using a gender-based diversity framework, was conducted to offer insight into dominant and counter discourses underlying health services access and equity initiatives. Results A continuum of five discourses that characterize the health services access and equity literature were identified including two dominant discourses: 1) multicultural discourse, and 2) diversity discourse; and three counter discourses: 3) social determinants of health (SDOH) discourse; 4) anti-oppression (AOP) discourse; and 5) citizen/social rights discourse. Conclusions The analysis offers a continuum of dominant and counter discourses on health services access and equity as determined from a gender-based diversity perspective. The continuum of discourses offers a framework to identify and redress organizational assumptions about, and ideological commitments to, sexual and gender diversity and health services access and equity. Thus, the continuum of discourses may serve as an important element of a health care organization's access and equity framework for the evaluation of access to good quality care for diverse LGBT populations. More specfically, the analysis offers four important points of consideration in relation to the development of a health services access and equity framework. PMID:21957894

Gender, sexuality and the discursive representation of access and equity in health services literature: implications for LGBT communities.

PubMed

Daley, Andrea E; Macdonnell, Judith A

2011-09-29

This article considers how health services access and equity documents represent the problem of access to health services and what the effects of that representation might be for lesbian, gay, bisexual and transgender (LGBT) communities. We conducted a critical discourse analysis on selected access and equity documents using a gender-based diversity framework as determined by two objectives: 1) to identify dominant and counter discourses in health services access and equity literature; and 2) to develop understanding of how particular discourses impact the inclusion, or not, of LGBT communities in health services access and equity frameworks.The analysis was conducted in response to public health and clinical research that has documented barriers to health services access for LGBT communities including institutionalized heterosexism, biphobia, and transphobia, invisibility and lack of health provider knowledge and comfort. The analysis was also conducted as the first step of exploring LGBT access issues in home care services for LGBT populations in Ontario, Canada. A critical discourse analysis of selected health services access and equity documents, using a gender-based diversity framework, was conducted to offer insight into dominant and counter discourses underlying health services access and equity initiatives. A continuum of five discourses that characterize the health services access and equity literature were identified including two dominant discourses: 1) multicultural discourse, and 2) diversity discourse; and three counter discourses: 3) social determinants of health (SDOH) discourse; 4) anti-oppression (AOP) discourse; and 5) citizen/social rights discourse. The analysis offers a continuum of dominant and counter discourses on health services access and equity as determined from a gender-based diversity perspective. The continuum of discourses offers a framework to identify and redress organizational assumptions about, and ideological commitments to, sexual and gender diversity and health services access and equity. Thus, the continuum of discourses may serve as an important element of a health care organization's access and equity framework for the evaluation of access to good quality care for diverse LGBT populations. More specfically, the analysis offers four important points of consideration in relation to the development of a health services access and equity framework.

Multimodal Literacies in Science: Currency, Coherence and Focus

NASA Astrophysics Data System (ADS)

Klein, Perry D.; Kirkpatrick, Lori C.

2010-01-01

Since the 1990s, researchers have increasingly drawn attention to the multiplicity of representations used in science. This issue of RISE advances this line of research by placing such representations at the centre of science teaching and learning. The authors show that representations do not simply transmit scientific information; they are integral to reasoning about scientific phenomena. This focus on thinking with representations mediates between well-resolved representations and formal reasoning of disciplinary science, and the capacity-limited, perceptually-driven nature of human cognition. The teaching practices described here build on three key principles: Each representation is interpreted through others; natural language is a sign system that is used to interpret a variety of other kinds of representations; and this chain of signs or representations is ultimately grounded in bodily experiences of perception and action. In these papers, the researchers provide examples and analysis of teachers scaffolding students in using representations to construct new knowledge, and in constructing new representations to express and develop their knowledge. The result is a new delineation of the power and the challenges of teaching science with multiple representations.

Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.

PubMed

Shi, Jun; Liu, Xiao; Li, Yan; Zhang, Qi; Li, Yingjie; Ying, Shihui

2015-10-30

Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state-of-the-art unsupervised feature learning method suitable for EEG feature representation. Collaborative representation (CR) is an effective data coding method used as a classifier. Here we use CR as a data representation method to learn features from the EEG signal. A joint collaboration model is established to develop a multi-view learning algorithm, and generate joint CR (JCR) codes to fuse and represent multi-channel EEG signals. A two-stage multi-view learning-based sleep staging framework is then constructed, in which JCR and joint sparse representation (JSR) algorithms first fuse and learning the feature representation from multi-channel EEG signals, respectively. Multi-view JCR and JSR features are then integrated and sleep stages recognized by a multiple kernel extreme learning machine (MK-ELM) algorithm with grid search. The proposed two-stage multi-view learning algorithm achieves superior performance for sleep staging. With a K-means clustering based dictionary, the mean classification accuracy, sensitivity and specificity are 81.10 ± 0.15%, 71.42 ± 0.66% and 94.57 ± 0.07%, respectively; while with the dictionary learned using the submodular optimization method, they are 80.29 ± 0.22%, 71.26 ± 0.78% and 94.38 ± 0.10%, respectively. The two-stage multi-view learning based sleep staging framework outperforms all other classification methods compared in this work, while JCR is superior to JSR. The proposed multi-view learning framework has the potential for sleep staging based on multi-channel or multi-modality polysomnography signals. Copyright © 2015 Elsevier B.V. All rights reserved.

The Representation of Object-Directed Action and Function Knowledge in the Human Brain.

PubMed

Chen, Quanjing; Garcea, Frank E; Mahon, Bradford Z

2016-04-01

The appropriate use of everyday objects requires the integration of action and function knowledge. Previous research suggests that action knowledge is represented in frontoparietal areas while function knowledge is represented in temporal lobe regions. Here we used multivoxel pattern analysis to investigate the representation of object-directed action and function knowledge while participants executed pantomimes of familiar tool actions. A novel approach for decoding object knowledge was used in which classifiers were trained on one pair of objects and then tested on a distinct pair; this permitted a measurement of classification accuracy over and above object-specific information. Region of interest (ROI) analyses showed that object-directed actions could be decoded in tool-preferring regions of both parietal and temporal cortex, while no independently defined tool-preferring ROI showed successful decoding of object function. However, a whole-brain searchlight analysis revealed that while frontoparietal motor and peri-motor regions are engaged in the representation of object-directed actions, medial temporal lobe areas in the left hemisphere are involved in the representation of function knowledge. These results indicate that both action and function knowledge are represented in a topographically coherent manner that is amenable to study with multivariate approaches, and that the left medial temporal cortex represents knowledge of object function. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Theoretical foundations for information representation and constraint specification

NASA Technical Reports Server (NTRS)

Menzel, Christopher P.; Mayer, Richard J.

1991-01-01

Research accomplished at the Knowledge Based Systems Laboratory of the Department of Industrial Engineering at Texas A&M University is described. Outlined here are the theoretical foundations necessary to construct a Neutral Information Representation Scheme (NIRS), which will allow for automated data transfer and translation between model languages, procedural programming languages, database languages, transaction and process languages, and knowledge representation and reasoning control languages for information system specification.

Semantic knowledge fractionations: verbal propositions vs. perceptual input? Evidence from a child with Klinefelter syndrome.

PubMed

Robinson, Sally J; Temple, Christine M

2013-04-01

This paper addresses the relative independence of different types of lexical- and factually-based semantic knowledge in JM, a 9-year-old boy with Klinefelter syndrome (KS). JM was matched to typically developing (TD) controls on the basis of chronological age. Lexical-semantic knowledge was investigated for common noun (CN) and mathematical vocabulary items (MV). Factually-based semantic knowledge was investigated for general and number facts. For CN items, JM's lexical stores were of a normal size but the volume of correct 'sensory feature' semantic knowledge he generated within verbal item descriptions was significantly reduced. He was also significantly impaired at naming item descriptions and pictures, particularly for fruit and vegetables. There was also weak object decision for fruit and vegetables. In contrast, for MV items, JM's lexical stores were elevated, with no significant difference in the amount and type of correct semantic knowledge generated within verbal item descriptions and normal naming. JM's fact retrieval accuracy was normal for all types of factual knowledge. JM's performance indicated a dissociation between the representation of CN and MV vocabulary items during development. JM's preserved semantic knowledge of facts in the face of impaired semantic knowledge of vocabulary also suggests that factually-based semantic knowledge representation is not dependent on normal lexical-semantic knowledge during development. These findings are discussed in relation to the emergence of distinct semantic knowledge representations during development, due to differing degrees of dependency upon the acquisition and representation of semantic knowledge from verbal propositions and perceptual input.

A tuned mesh-generation strategy for image representation based on data-dependent triangulation.

PubMed

Li, Ping; Adams, Michael D

2013-05-01

A mesh-generation framework for image representation based on data-dependent triangulation is proposed. The proposed framework is a modified version of the frameworks of Rippa and Garland and Heckbert that facilitates the development of more effective mesh-generation methods. As the proposed framework has several free parameters, the effects of different choices of these parameters on mesh quality are studied, leading to the recommendation of a particular set of choices for these parameters. A mesh-generation method is then introduced that employs the proposed framework with these best parameter choices. This method is demonstrated to produce meshes of higher quality (both in terms of squared error and subjectively) than those generated by several competing approaches, at a relatively modest computational and memory cost.

Rigor in electronic health record knowledge representation: Lessons learned from a SNOMED CT clinical content encoding exercise.

PubMed

Monsen, Karen A; Finn, Robert S; Fleming, Thea E; Garner, Erin J; LaValla, Amy J; Riemer, Judith G

2016-01-01

Rigor in clinical knowledge representation is necessary foundation for meaningful interoperability, exchange and reuse of electronic health record (EHR) data. It is critical for clinicians to understand principles and implications of using clinical standards for knowledge representation within EHRs. To educate clinicians and students about knowledge representation and to evaluate their success of applying the manual lookups method for assigning Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concept identifiers using formally mapped concepts from the Omaha System interface terminology. Clinicians who were students in a doctoral nursing program conducted 21 lookups for Omaha System terms in publicly available SNOMED CT browsers. Lookups were deemed successful if results matched exactly with the corresponding code from the January 2013 SNOMED CT-Omaha System terminology cross-map. Of the 21 manual lookups attempted, 12 (57.1%) were successful. Errors were due to semantic gaps differences in granularity and synonymy or partial term matching. Achieving rigor in clinical knowledge representation across settings, vendors and health systems is a globally recognized challenge. Cross-maps have potential to improve rigor in SNOMED CT encoding of clinical data. Further research is needed to evaluate outcomes of using of terminology cross-maps to encode clinical terms with SNOMED CT concept identifiers based on interface terminologies.

Dynamic updating of hippocampal object representations reflects new conceptual knowledge

PubMed Central

Mack, Michael L.; Love, Bradley C.; Preston, Alison R.

2016-01-01

Concepts organize the relationship among individual stimuli or events by highlighting shared features. Often, new goals require updating conceptual knowledge to reflect relationships based on different goal-relevant features. Here, our aim is to determine how hippocampal (HPC) object representations are organized and updated to reflect changing conceptual knowledge. Participants learned two classification tasks in which successful learning required attention to different stimulus features, thus providing a means to index how representations of individual stimuli are reorganized according to changing task goals. We used a computational learning model to capture how people attended to goal-relevant features and organized object representations based on those features during learning. Using representational similarity analyses of functional magnetic resonance imaging data, we demonstrate that neural representations in left anterior HPC correspond with model predictions of concept organization. Moreover, we show that during early learning, when concept updating is most consequential, HPC is functionally coupled with prefrontal regions. Based on these findings, we propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal. PMID:27803320

Knowledge-base browsing: an application of hybrid distributed/local connectionist networks

NASA Astrophysics Data System (ADS)

Samad, Tariq; Israel, Peggy

1990-08-01

We describe a knowledge base browser based on a connectionist (or neural network) architecture that employs both distributed and local representations. The distributed representations are used for input and output thereby enabling associative noise-tolerant interaction with the environment. Internally all representations are fully local. This simplifies weight assignment and facilitates network configuration for specific applications. In our browser concepts and relations in a knowledge base are represented using " microfeatures. " The microfeatures can encode semantic attributes structural features contextual information etc. Desired portions of the knowledge base can then be associatively retrieved based on a structured cue. An ordered list of partial matches is presented to the user for selection. Microfeatures can also be used as " bookmarks" they can be placed dynamically at appropriate points in the knowledge base and subsequently used as retrieval cues. A proof-of-concept system has been implemented for an internally developed Honeywell-proprietary knowledge acquisition tool. 1.

Framing of scientific knowledge as a new category of health care research.

PubMed

Salvador-Carulla, Luis; Fernandez, Ana; Madden, Rosamond; Lukersmith, Sue; Colagiuri, Ruth; Torkfar, Ghazal; Sturmberg, Joachim

2014-12-01

The new area of health system research requires a revision of the taxonomy of scientific knowledge that may facilitate a better understanding and representation of complex health phenomena in research discovery, corroboration and implementation. A position paper by an expert group following and iterative approach. 'Scientific evidence' should be differentiated from 'elicited knowledge' of experts and users, and this latter typology should be described beyond the traditional qualitative framework. Within this context 'framing of scientific knowledge' (FSK) is defined as a group of studies of prior expert knowledge specifically aimed at generating formal scientific frames. To be distinguished from other unstructured frames, FSK must be explicit, standardized, based on the available evidence, agreed by a group of experts and subdued to the principles of commensurability, transparency for corroboration and transferability that characterize scientific research. A preliminary typology of scientific framing studies is presented. This typology includes, among others, health declarations, position papers, expert-based clinical guides, conceptual maps, classifications, expert-driven health atlases and expert-driven studies of costs and burden of illness. This grouping of expert-based studies constitutes a different kind of scientific knowledge and should be clearly differentiated from 'evidence' gathered from experimental and observational studies in health system research. © 2014 John Wiley & Sons, Ltd.

Organizing knowledge for tutoring fire loss prevention

NASA Astrophysics Data System (ADS)

Schmoldt, Daniel L.

1989-09-01

The San Bernardino National Forest in southern California has recently developed a systematic approach to wildfire prevention planning. However, a comprehensive document or other mechanism for teaching this process to other prevention personnel does not exist. An intelligent tutorial expert system is being constructed to provide a means for learning the process and to assist in the creation of specific prevention plans. An intelligent tutoring system (ITS) contains two types of knowledge—domain and tutoring. The domain knowledge for wildfire prevention is structured around several foci: (1) individual concepts used in prevention planning; (2) explicitly specified interrelationships between concepts; (3) deductive methods that contain subjective judgment normally unavailable to less-experienced users; (4) analytical models of fire behavior used for identification of hazard areas; (5) how-to guidance needed for performance of planning tasks; and (6) expository information that provides a rationale for planning steps and ideas. Combining analytical, procedure, inferential, conceptual, and expositional knowledge into a tutoring environment provides the student and/or user with a multiple perspective of the subject matter. A concept network provides a unifying framework for structuring and utilizing these diverse forms of prevention planning knowledge. This network structure borrows from and combines semantic networks and frame-based knowledge representations. The flexibility of this organization facilitates an effective synthesis and organization of multiple knowledge forms.

A viewpoint-based case-based reasoning approach utilising an enterprise architecture ontology for experience management

NASA Astrophysics Data System (ADS)

Martin, Andreas; Emmenegger, Sandro; Hinkelmann, Knut; Thönssen, Barbara

2017-04-01

The accessibility of project knowledge obtained from experiences is an important and crucial issue in enterprises. This information need about project knowledge can be different from one person to another depending on the different roles he or she has. Therefore, a new ontology-based case-based reasoning (OBCBR) approach that utilises an enterprise ontology is introduced in this article to improve the accessibility of this project knowledge. Utilising an enterprise ontology improves the case-based reasoning (CBR) system through the systematic inclusion of enterprise-specific knowledge. This enterprise-specific knowledge is captured using the overall structure given by the enterprise ontology named ArchiMEO, which is a partial ontological realisation of the enterprise architecture framework (EAF) ArchiMate. This ontological representation, containing historical cases and specific enterprise domain knowledge, is applied in a new OBCBR approach. To support the different information needs of different stakeholders, this OBCBR approach has been built in such a way that different views, viewpoints, concerns and stakeholders can be considered. This is realised using a case viewpoint model derived from the ISO/IEC/IEEE 42010 standard. The introduced approach was implemented as a demonstrator and evaluated using an application case that has been elicited from a business partner in the Swiss research project.

The Representation of Self and Person Knowledge in the Medial Prefrontal Cortex

PubMed Central

Haxby, James V.; Heatherton, Todd F.

2012-01-01

Nearly forty years ago, social psychologists began applying the information processing framework of cognitive psychology to the question of how humans understand and represent knowledge about themselves and others. This approach gave rise to the immensely successful field of social cognition and fundamentally changed the way in which social psychological phenomena are studied. More recently, social scientists of many stripes have turned to the methods of cognitive neuroscience to understand the neural basis of social cognition. A pervasive finding from this research is that social knowledge, be it about one's self or of others, is represented in the medial prefrontal cortex. This review focuses on the social cognitive neuroscience of self and person knowledge in the medial prefrontal cortex. We begin with a brief historical overview of social cognition, followed by a review of recent and influential research on the brain basis of self and person knowledge. In the latter half of this review we discuss the role of familiarity and similarity in person perception and of spontaneous processes in self and other referential cognition. Throughout, we discuss the myriad ways in which the social cognitive neuroscience approach has provided new insights into the nature and structure of self and person knowledge. PMID:22712038

The Effect of Concept Mapping with Different Levels of Generativity and Learners' Self-Regulated Learning Skills on Knowledge Acquisition and Representation

ERIC Educational Resources Information Center

Lim, Kyu Yon

2008-01-01

The purpose of this study was to investigate the effectiveness of concept mapping strategies with different levels of generativity in terms of knowledge acquisition and knowledge representation. Also, it examined whether or not learners' self-regulated learning (SRL) skills influenced the effectiveness of concept mapping strategies with different…

Theorizing "Difficult Knowledge" in the Aftermath of the "Affective Turn": Implications for Curriculum and Pedagogy in Handling Traumatic Representations

ERIC Educational Resources Information Center

Zembylas, Michalinos

2014-01-01

This essay draws on the concept of "difficult knowledge" to think with some of the interventions and arguments of affect theory and discusses the implications for curriculum and pedagogy in handling traumatic representations. The author makes an argument that affect theory enables the theorization of difficult knowledge as an…

Knowledge Representation and Self-Regulatory Experiences of Expert and Novice Certified Athletic Trainers in College and University Settings

ERIC Educational Resources Information Center

Gardin, Fredrick Anthony

2009-01-01

The purpose of this study was to describe how male, collegiate, certified athletic trainers (AT's) represent knowledge during 5 injury evaluation scenarios. A second purpose of the study was to identify what self-regulatory behaviors participants engaged in to improve or maintain their skills. Knowledge representation was studied as cue selection…

Depth of Teachers' Knowledge: Frameworks for Teachers' Knowledge of Mathematics

ERIC Educational Resources Information Center

Holmes, Vicki-Lynn

2012-01-01

This article describes seven teacher knowledge frameworks and relates these frameworks to the teaching and assessment of elementary teacher's mathematics knowledge. The frameworks classify teachers' knowledge and provide a vocabulary and common language through which knowledge can be discussed and assessed. These frameworks are categorized into…

Structural Representations in Knowledge Acquisition.

ERIC Educational Resources Information Center

Gonzalvo, Pilar; And Others

1994-01-01

Multidimensional scaling (MDS) and Pathfinder techniques for assessing changes in the structural representation of a knowledge domain were studied with relatedness ratings collected from 72 Spanish college students. Comparison of student and expert similarity measures indicate that MDS and graph theoretic approaches are valid techniques. (SLD)

Acquisition, representation and rule generation for procedural knowledge

NASA Technical Reports Server (NTRS)

Ortiz, Chris; Saito, Tim; Mithal, Sachin; Loftin, R. Bowen

1991-01-01

Current research into the design and continuing development of a system for the acquisition of procedural knowledge, its representation in useful forms, and proposed methods for automated C Language Integrated Production System (CLIPS) rule generation is discussed. The Task Analysis and Rule Generation Tool (TARGET) is intended to permit experts, individually or collectively, to visually describe and refine procedural tasks. The system is designed to represent the acquired knowledge in the form of graphical objects with the capacity for generating production rules in CLIPS. The generated rules can then be integrated into applications such as NASA's Intelligent Computer Aided Training (ICAT) architecture. Also described are proposed methods for use in translating the graphical and intermediate knowledge representations into CLIPS rules.

Lexical is as lexical does: computational approaches to lexical representation

PubMed Central

Woollams, Anna M.

2015-01-01

In much of neuroimaging and neuropsychology, regions of the brain have been associated with ‘lexical representation’, with little consideration as to what this cognitive construct actually denotes. Within current computational models of word recognition, there are a number of different approaches to the representation of lexical knowledge. Structural lexical representations, found in original theories of word recognition, have been instantiated in modern localist models. However, such a representational scheme lacks neural plausibility in terms of economy and flexibility. Connectionist models have therefore adopted distributed representations of form and meaning. Semantic representations in connectionist models necessarily encode lexical knowledge. Yet when equipped with recurrent connections, connectionist models can also develop attractors for familiar forms that function as lexical representations. Current behavioural, neuropsychological and neuroimaging evidence shows a clear role for semantic information, but also suggests some modality- and task-specific lexical representations. A variety of connectionist architectures could implement these distributed functional representations, and further experimental and simulation work is required to discriminate between these alternatives. Future conceptualisations of lexical representations will therefore emerge from a synergy between modelling and neuroscience. PMID:25893204

38 CFR 14.632 - Standards of conduct for persons providing representation before the Department

Code of Federal Regulations, 2013 CFR

2013-07-01

... the knowledge, skill, thoroughness, and preparation necessary for the representation. This includes... persons providing representation before the Department 14.632 Section 14.632 Pensions, Bonuses, and... Representation of Department of Veterans Affairs Claimants; Recognition of Organizations, Accredited...

38 CFR 14.632 - Standards of conduct for persons providing representation before the Department

Code of Federal Regulations, 2011 CFR

2011-07-01

... the knowledge, skill, thoroughness, and preparation necessary for the representation. This includes... persons providing representation before the Department 14.632 Section 14.632 Pensions, Bonuses, and... Representation of Department of Veterans Affairs Claimants; Recognition of Organizations, Accredited...

38 CFR 14.632 - Standards of conduct for persons providing representation before the Department

Code of Federal Regulations, 2014 CFR

2014-07-01

... the knowledge, skill, thoroughness, and preparation necessary for the representation. This includes... persons providing representation before the Department 14.632 Section 14.632 Pensions, Bonuses, and... Representation of Department of Veterans Affairs Claimants; Recognition of Organizations, Accredited...

38 CFR 14.632 - Standards of conduct for persons providing representation before the Department

Code of Federal Regulations, 2010 CFR

2010-07-01

... the knowledge, skill, thoroughness, and preparation necessary for the representation. This includes... persons providing representation before the Department 14.632 Section 14.632 Pensions, Bonuses, and... Representation of Department of Veterans Affairs Claimants; Recognition of Organizations, Accredited...

38 CFR 14.632 - Standards of conduct for persons providing representation before the Department

Code of Federal Regulations, 2012 CFR

2012-07-01

... the knowledge, skill, thoroughness, and preparation necessary for the representation. This includes... persons providing representation before the Department 14.632 Section 14.632 Pensions, Bonuses, and... Representation of Department of Veterans Affairs Claimants; Recognition of Organizations, Accredited...

GALEN: a third generation terminology tool to support a multipurpose national coding system for surgical procedures.

PubMed

Trombert-Paviot, B; Rodrigues, J M; Rogers, J E; Baud, R; van der Haring, E; Rassinoux, A M; Abrial, V; Clavel, L; Idir, H

2000-09-01

Generalised architecture for languages, encyclopedia and nomenclatures in medicine (GALEN) has developed a new generation of terminology tools based on a language independent model describing the semantics and allowing computer processing and multiple reuses as well as natural language understanding systems applications to facilitate the sharing and maintaining of consistent medical knowledge. During the European Union 4 Th. framework program project GALEN-IN-USE and later on within two contracts with the national health authorities we applied the modelling and the tools to the development of a new multipurpose coding system for surgical procedures named CCAM in a minority language country, France. On one hand, we contributed to a language independent knowledge repository and multilingual semantic dictionaries for multicultural Europe. On the other hand, we support the traditional process for creating a new coding system in medicine which is very much labour consuming by artificial intelligence tools using a medically oriented recursive ontology and natural language processing. We used an integrated software named CLAW (for classification workbench) to process French professional medical language rubrics produced by the national colleges of surgeons domain experts into intermediate dissections and to the Grail reference ontology model representation. From this language independent concept model representation, on one hand, we generate with the LNAT natural language generator controlled French natural language to support the finalization of the linguistic labels (first generation) in relation with the meanings of the conceptual system structure. On the other hand, the Claw classification manager proves to be very powerful to retrieve the initial domain experts rubrics list with different categories of concepts (second generation) within a semantic structured representation (third generation) bridge to the electronic patient record detailed terminology.

An Ontology for Representing Geoscience Theories and Related Knowledge

NASA Astrophysics Data System (ADS)

Brodaric, B.

2009-12-01

Online scientific research, or e-science, is increasingly reliant on machine-readable representations of scientific data and knowledge. At present, much of the knowledge is represented in ontologies, which typically contain geoscience categories such as ‘water body’, ‘aquifer’, ‘granite’, ‘temperature’, ‘density’, ‘Co2’. While extremely useful for many e-science activities, such categorical representations constitute only a fragment of geoscience knowledge. Also needed are online representations of elements such as geoscience theories, to enable geoscientists to pose and evaluate hypotheses online. To address this need, the Science Knowledge Infrastructure ontology (SKIo) specializes the DOLCE foundational ontology with basic science knowledge primitives such as theory, model, observation, and prediction. Discussed will be SKIo as well as its implementation in the geosciences, including case studies from marine science, environmental science, and geologic mapping. These case studies demonstrate SKIo’s ability to represent a wide spectrum of geoscience knowledge types, to help fuel next generation e-science.

A framework for the computer-aided planning and optimisation of manufacturing processes for components with functional graded properties

NASA Astrophysics Data System (ADS)

Biermann, D.; Gausemeier, J.; Heim, H.-P.; Hess, S.; Petersen, M.; Ries, A.; Wagner, T.

2014-05-01

In this contribution a framework for the computer-aided planning and optimisation of functional graded components is presented. The framework is divided into three modules - the "Component Description", the "Expert System" for the synthetisation of several process chains and the "Modelling and Process Chain Optimisation". The Component Description module enhances a standard computer-aided design (CAD) model by a voxel-based representation of the graded properties. The Expert System synthesises process steps stored in the knowledge base to generate several alternative process chains. Each process chain is capable of producing components according to the enhanced CAD model and usually consists of a sequence of heating-, cooling-, and forming processes. The dependencies between the component and the applied manufacturing processes as well as between the processes themselves need to be considered. The Expert System utilises an ontology for that purpose. The ontology represents all dependencies in a structured way and connects the information of the knowledge base via relations. The third module performs the evaluation of the generated process chains. To accomplish this, the parameters of each process are optimised with respect to the component specification, whereby the result of the best parameterisation is used as representative value. Finally, the process chain which is capable of manufacturing a functionally graded component in an optimal way regarding to the property distributions of the component description is presented by means of a dedicated specification technique.

Rubber airplane: Constraint-based component-modeling for knowledge representation in computer-aided conceptual design

NASA Technical Reports Server (NTRS)

Kolb, Mark A.

1990-01-01

Viewgraphs on Rubber Airplane: Constraint-based Component-Modeling for Knowledge Representation in Computer Aided Conceptual Design are presented. Topics covered include: computer aided design; object oriented programming; airfoil design; surveillance aircraft; commercial aircraft; aircraft design; and launch vehicles.

Translation between representation languages

NASA Technical Reports Server (NTRS)

Vanbaalen, Jeffrey

1994-01-01

A capability for translating between representation languages is critical for effective knowledge base reuse. A translation technology for knowledge representation languages based on the use of an interlingua for communicating knowledge is described. The interlingua-based translation process consists of three major steps: translation from the source language into a subset of the interlingua, translation between subsets of the interlingua, and translation from a subset of the interlingua into the target language. The first translation step into the interlingua can typically be specified in the form of a grammar that describes how each top-level form in the source language translates into the interlingua. In cases where the source language does not have a declarative semantics, such a grammar is also a specification of a declarative semantics for the language. A methodology for building translators that is currently under development is described. A 'translator shell' based on this methodology is also under development. The shell has been used to build translators for multiple representation languages and those translators have successfully translated nontrivial knowledge bases.

Modeling biochemical pathways in the gene ontology

DOE PAGES

Hill, David P.; D’Eustachio, Peter; Berardini, Tanya Z.; ...

2016-09-01

The concept of a biological pathway, an ordered sequence of molecular transformations, is used to collect and represent molecular knowledge for a broad span of organismal biology. Representations of biomedical pathways typically are rich but idiosyncratic presentations of organized knowledge about individual pathways. Meanwhile, biomedical ontologies and associated annotation files are powerful tools that organize molecular information in a logically rigorous form to support computational analysis. The Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways within its ontological representations. Here we present a methodology for extending and refining the classes inmore » the GO for more comprehensive, consistent and integrated representation of pathways, leveraging knowledge embedded in current pathway representations such as those in the Reactome Knowledgebase and MetaCyc. With carbohydrate metabolic pathways as a use case, we discuss how our representation supports the integration of variant pathway classes into a unified ontological structure that can be used for data comparison and analysis.« less

Representation of Biomedical Expertise in Ontologies: a Case Study about Knowledge Acquisition on HTLV viruses and their clinical manifestations.

PubMed

Cardoso Coelho, Kátia; Barcellos Almeida, Maurício

2015-01-01

In this paper, we introduce a set of methodological steps for knowledge acquisition applied to the organization of biomedical information through ontologies. Those steps are tested in a real case involving Human T Cell Lymphotropic Virus (HTLV), which causes myriad infectious diseases. We hope to contribute to providing suitable knowledge representation of scientific domains.

Knowledge representation for fuzzy inference aided medical image interpretation.

PubMed

Gal, Norbert; Stoicu-Tivadar, Vasile

2012-01-01

Knowledge defines how an automated system transforms data into information. This paper suggests a representation method of medical imaging knowledge using fuzzy inference systems coded in XML files. The imaging knowledge incorporates features of the investigated objects in linguistic form and inference rules that can transform the linguistic data into information about a possible diagnosis. A fuzzy inference system is used to model the vagueness of the linguistic medical imaging terms. XML files are used to facilitate easy manipulation and deployment of the knowledge into the imaging software. Preliminary results are presented.

On the acquisition and representation of procedural knowledge

NASA Technical Reports Server (NTRS)

Saito, T.; Ortiz, C.; Loftin, R. B.

1992-01-01

Historically knowledge acquisition has proven to be one of the greatest barriers to the development of intelligent systems. Current practice generally requires lengthy interactions between the expert whose knowledge is to be captured and the knowledge engineer whose responsibility is to acquire and represent knowledge in a useful form. Although much research has been devoted to the development of methodologies and computer software to aid in the capture and representation of some of some types of knowledge, little attention has been devoted to procedural knowledge. NASA personnel frequently perform tasks that are primarily procedural in nature. Previous work is reviewed in the field of knowledge acquisition and then focus on knowledge acquisition for procedural tasks with special attention devoted to the Navy's VISTA tool. The design and development is described of a system for the acquisition and representation of procedural knowledge-TARGET (Task Analysis and Rule Generation Tool). TARGET is intended as a tool that permits experts to visually describe procedural tasks and as a common medium for knowledge refinement by the expert and knowledge engineer. The system is designed to represent the acquired knowledge in the form of production rules. Systems such as TARGET have the potential to profoundly reduce the time, difficulties, and costs of developing knowledge-based systems for the performance of procedural tasks.

An Unified Multiscale Framework for Planar, Surface, and Curve Skeletonization.

PubMed

Jalba, Andrei C; Sobiecki, Andre; Telea, Alexandru C

2016-01-01

Computing skeletons of 2D shapes, and medial surface and curve skeletons of 3D shapes, is a challenging task. In particular, there is no unified framework that detects all types of skeletons using a single model, and also produces a multiscale representation which allows to progressively simplify, or regularize, all skeleton types. In this paper, we present such a framework. We model skeleton detection and regularization by a conservative mass transport process from a shape's boundary to its surface skeleton, next to its curve skeleton, and finally to the shape center. The resulting density field can be thresholded to obtain a multiscale representation of progressively simplified surface, or curve, skeletons. We detail a numerical implementation of our framework which is demonstrably stable and has high computational efficiency. We demonstrate our framework on several complex 2D and 3D shapes.

Knowledge Representation and Management. From Ontology to Annotation. Findings from the Yearbook 2015 Section on Knowledge Representation and Management.

PubMed

Charlet, J; Darmoni, S J

2015-08-13

To summarize the best papers in the field of Knowledge Representation and Management (KRM). A comprehensive review of medical informatics literature was performed to select some of the most interesting papers of KRM published in 2014. Four articles were selected, two focused on annotation and information retrieval using an ontology. The two others focused mainly on ontologies, one dealing with the usage of a temporal ontology in order to analyze the content of narrative document, one describing a methodology for building multilingual ontologies. Semantic models began to show their efficiency, coupled with annotation tools.

Sentiments analysis at conceptual level making use of the Narrative Knowledge Representation Language.

PubMed

Zarri, Gian Piero

2014-10-01

This paper illustrates some of the knowledge representation structures and inference procedures proper to a high-level, fully implemented conceptual language, NKRL (Narrative Knowledge Representation Language). The aim is to show how these tools can be used to deal, in a sentiment analysis/opinion mining context, with some common types of human (and non-human) "behaviors". These behaviors correspond, in particular, to the concrete, mutual relationships among human and non-human characters that can be expressed under the form of non-fictional and real-time "narratives" (i.e., as logically and temporally structured sequences of "elementary events"). Copyright © 2014 Elsevier Ltd. All rights reserved.

Spatial navigation by congenitally blind individuals.

PubMed

Schinazi, Victor R; Thrash, Tyler; Chebat, Daniel-Robert

2016-01-01

Spatial navigation in the absence of vision has been investigated from a variety of perspectives and disciplines. These different approaches have progressed our understanding of spatial knowledge acquisition by blind individuals, including their abilities, strategies, and corresponding mental representations. In this review, we propose a framework for investigating differences in spatial knowledge acquisition by blind and sighted people consisting of three longitudinal models (i.e., convergent, cumulative, and persistent). Recent advances in neuroscience and technological devices have provided novel insights into the different neural mechanisms underlying spatial navigation by blind and sighted people and the potential for functional reorganization. Despite these advances, there is still a lack of consensus regarding the extent to which locomotion and wayfinding depend on amodal spatial representations. This challenge largely stems from methodological limitations such as heterogeneity in the blind population and terminological ambiguity related to the concept of cognitive maps. Coupled with an over-reliance on potential technological solutions, the field has diffused into theoretical and applied branches that do not always communicate. Here, we review research on navigation by congenitally blind individuals with an emphasis on behavioral and neuroscientific evidence, as well as the potential of technological assistance. Throughout the article, we emphasize the need to disentangle strategy choice and performance when discussing the navigation abilities of the blind population. For further resources related to this article, please visit the WIREs website. © 2015 The Authors. WIREs Cognitive Science published by Wiley Periodicals, Inc.

Origins of Secure Base Script Knowledge and the Developmental Construction of Attachment Representations.

PubMed

Waters, Theodore E A; Ruiz, Sarah K; Roisman, Glenn I

2017-01-01

Increasing evidence suggests that attachment representations take at least two forms: a secure base script and an autobiographical narrative of childhood caregiving experiences. This study presents data from the first 26 years of the Minnesota Longitudinal Study of Risk and Adaptation (N = 169), examining the developmental origins of secure base script knowledge in a high-risk sample and testing alternative models of the developmental sequencing of the construction of attachment representations. Results demonstrated that secure base script knowledge was predicted by observations of maternal sensitivity across childhood and adolescence. Furthermore, findings suggest that the construction of a secure base script supports the development of a coherent autobiographical representation of childhood attachment experiences with primary caregivers by early adulthood. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

Evaluation of an Intelligent Tutoring System in Pathology: Effects of External Representation on Performance Gains, Metacognition, and Acceptance

PubMed Central

Crowley, Rebecca S.; Legowski, Elizabeth; Medvedeva, Olga; Tseytlin, Eugene; Roh, Ellen; Jukic, Drazen

2007-01-01

Objective Determine effects of computer-based tutoring on diagnostic performance gains, meta-cognition, and acceptance using two different problem representations. Describe impact of tutoring on spectrum of diagnostic skills required for task performance. Identify key features of student-tutor interaction contributing to learning gains. Design Prospective, between-subjects study, controlled for participant level of training. Resident physicians in two academic pathology programs spent four hours using one of two interfaces which differed mainly in external problem representation. The case-focused representation provided an open-learning environment in which students were free to explore evidence-hypothesis relationships within a case, but could not visualize the entire diagnostic space. The knowledge-focused representation provided an interactive representation of the entire diagnostic space, which more tightly constrained student actions. Measurements Metrics included results of pretest, post-test and retention-test for multiple choice and case diagnosis tests, ratios of performance to student reported certainty, results of participant survey, learning curves, and interaction behaviors during tutoring. Results Students had highly significant learning gains after one tutoring session. Learning was retained at one week. There were no differences between the two interfaces in learning gains on post-test or retention test. Only students in the knowledge-focused interface exhibited significant metacognitive gains from pretest to post-test and pretest to retention test. Students rated the knowledge-focused interface significantly higher than the case-focused interface. Conclusions Cognitive tutoring is associated with improved diagnostic performance in a complex medical domain. The effect is retained at one-week post-training. Knowledge-focused external problem representation shows an advantage over case-focused representation for metacognitive effects and user acceptance. PMID:17213494

Evaluation of an intelligent tutoring system in pathology: effects of external representation on performance gains, metacognition, and acceptance.

PubMed

Crowley, Rebecca S; Legowski, Elizabeth; Medvedeva, Olga; Tseytlin, Eugene; Roh, Ellen; Jukic, Drazen

2007-01-01

Determine effects of computer-based tutoring on diagnostic performance gains, meta-cognition, and acceptance using two different problem representations. Describe impact of tutoring on spectrum of diagnostic skills required for task performance. Identify key features of student-tutor interaction contributing to learning gains. Prospective, between-subjects study, controlled for participant level of training. Resident physicians in two academic pathology programs spent four hours using one of two interfaces which differed mainly in external problem representation. The case-focused representation provided an open-learning environment in which students were free to explore evidence-hypothesis relationships within a case, but could not visualize the entire diagnostic space. The knowledge-focused representation provided an interactive representation of the entire diagnostic space, which more tightly constrained student actions. Metrics included results of pretest, post-test and retention-test for multiple choice and case diagnosis tests, ratios of performance to student reported certainty, results of participant survey, learning curves, and interaction behaviors during tutoring. Students had highly significant learning gains after one tutoring session. Learning was retained at one week. There were no differences between the two interfaces in learning gains on post-test or retention test. Only students in the knowledge-focused interface exhibited significant metacognitive gains from pretest to post-test and pretest to retention test. Students rated the knowledge-focused interface significantly higher than the case-focused interface. Cognitive tutoring is associated with improved diagnostic performance in a complex medical domain. The effect is retained at one-week post-training. Knowledge-focused external problem representation shows an advantage over case-focused representation for metacognitive effects and user acceptance.

Human white matter and knowledge representation

PubMed Central

2018-01-01

Understanding how knowledge is represented in the human brain is a fundamental challenge in neuroscience. To date, most of the work on this topic has focused on knowledge representation in cortical areas and debated whether knowledge is represented in a distributed or localized fashion. Fang and colleagues provide evidence that brain connections and the white matter supporting such connections might play a significant role. The work opens new avenues of investigation, breaking through disciplinary boundaries across network neuroscience, computational neuroscience, cognitive science, and classical lesion studies. PMID:29698391

Human white matter and knowledge representation.

PubMed

Pestilli, Franco

2018-04-01

Understanding how knowledge is represented in the human brain is a fundamental challenge in neuroscience. To date, most of the work on this topic has focused on knowledge representation in cortical areas and debated whether knowledge is represented in a distributed or localized fashion. Fang and colleagues provide evidence that brain connections and the white matter supporting such connections might play a significant role. The work opens new avenues of investigation, breaking through disciplinary boundaries across network neuroscience, computational neuroscience, cognitive science, and classical lesion studies.

48 CFR 2052.209-71 - Contractor organizational conflicts of interest (representation).

Code of Federal Regulations, 2010 CFR

2010-10-01

... conflicts of interest (representation). 2052.209-71 Section 2052.209-71 Federal Acquisition Regulations... of Provisions and Clauses 2052.209-71 Contractor organizational conflicts of interest (representation... Organizational Conflicts of Interest Representation (OCT 1999) I represent to the best of my knowledge and belief...

Public Higher Education Performance Accountability Framework Report: Goal--Access and Affordability. Measure: Percentage of Racial Representation in Systems of Higher Education Compared to Racial Representation in the State. Commission Report 07-20

ERIC Educational Resources Information Center

California Postsecondary Education Commission, 2007

2007-01-01

Despite segmental efforts to increase diversity in higher education, African American and Latino students are not achieving levels of representation in California public universities that are equivalent to their levels of representation in the overall State population. Using data for the years 1997 through 2006, the California Postsecondary…

The Semantic eScience Framework

NASA Astrophysics Data System (ADS)

McGuinness, Deborah; Fox, Peter; Hendler, James

2010-05-01

The goal of this effort is to design and implement a configurable and extensible semantic eScience framework (SESF). Configuration requires research into accommodating different levels of semantic expressivity and user requirements from use cases. Extensibility is being achieved in a modular approach to the semantic encodings (i.e. ontologies) performed in community settings, i.e. an ontology framework into which specific applications all the way up to communities can extend the semantics for their needs.We report on how we are accommodating the rapid advances in semantic technologies and tools and the sustainable software path for the future (certain) technical advances. In addition to a generalization of the current data science interface, we will present plans for an upper-level interface suitable for use by clearinghouses, and/or educational portals, digital libraries, and other disciplines.SESF builds upon previous work in the Virtual Solar-Terrestrial Observatory. The VSTO utilizes leading edge knowledge representation, query and reasoning techniques to support knowledge-enhanced search, data access, integration, and manipulation. It encodes term meanings and their inter-relationships in ontologies anduses these ontologies and associated inference engines to semantically enable the data services. The Semantically-Enabled Science Data Integration (SESDI) project implemented data integration capabilities among three sub-disciplines; solar radiation, volcanic outgassing and atmospheric structure using extensions to existingmodular ontolgies and used the VSTO data framework, while adding smart faceted search and semantic data registrationtools. The Semantic Provenance Capture in Data Ingest Systems (SPCDIS) has added explanation provenance capabilities to an observational data ingest pipeline for images of the Sun providing a set of tools to answer diverseend user questions such as ``Why does this image look bad?. http://tw.rpi.edu/portal/SESF

Integrating machine learning techniques into robust data enrichment approach and its application to gene expression data.

PubMed

Erdoğdu, Utku; Tan, Mehmet; Alhajj, Reda; Polat, Faruk; Rokne, Jon; Demetrick, Douglas

2013-01-01

The availability of enough samples for effective analysis and knowledge discovery has been a challenge in the research community, especially in the area of gene expression data analysis. Thus, the approaches being developed for data analysis have mostly suffered from the lack of enough data to train and test the constructed models. We argue that the process of sample generation could be successfully automated by employing some sophisticated machine learning techniques. An automated sample generation framework could successfully complement the actual sample generation from real cases. This argument is validated in this paper by describing a framework that integrates multiple models (perspectives) for sample generation. We illustrate its applicability for producing new gene expression data samples, a highly demanding area that has not received attention. The three perspectives employed in the process are based on models that are not closely related. The independence eliminates the bias of having the produced approach covering only certain characteristics of the domain and leading to samples skewed towards one direction. The first model is based on the Probabilistic Boolean Network (PBN) representation of the gene regulatory network underlying the given gene expression data. The second model integrates Hierarchical Markov Model (HIMM) and the third model employs a genetic algorithm in the process. Each model learns as much as possible characteristics of the domain being analysed and tries to incorporate the learned characteristics in generating new samples. In other words, the models base their analysis on domain knowledge implicitly present in the data itself. The developed framework has been extensively tested by checking how the new samples complement the original samples. The produced results are very promising in showing the effectiveness, usefulness and applicability of the proposed multi-model framework.

The Semantic eScience Framework

NASA Astrophysics Data System (ADS)

Fox, P. A.; McGuinness, D. L.

2009-12-01

The goal of this effort is to design and implement a configurable and extensible semantic eScience framework (SESF). Configuration requires research into accommodating different levels of semantic expressivity and user requirements from use cases. Extensibility is being achieved in a modular approach to the semantic encodings (i.e. ontologies) performed in community settings, i.e. an ontology framework into which specific applications all the way up to communities can extend the semantics for their needs.We report on how we are accommodating the rapid advances in semantic technologies and tools and the sustainable software path for the future (certain) technical advances. In addition to a generalization of the current data science interface, we will present plans for an upper-level interface suitable for use by clearinghouses, and/or educational portals, digital libraries, and other disciplines.SESF builds upon previous work in the Virtual Solar-Terrestrial Observatory. The VSTO utilizes leading edge knowledge representation, query and reasoning techniques to support knowledge-enhanced search, data access, integration, and manipulation. It encodes term meanings and their inter-relationships in ontologies anduses these ontologies and associated inference engines to semantically enable the data services. The Semantically-Enabled Science Data Integration (SESDI) project implemented data integration capabilities among three sub-disciplines; solar radiation, volcanic outgassing and atmospheric structure using extensions to existingmodular ontolgies and used the VSTO data framework, while adding smart faceted search and semantic data registrationtools. The Semantic Provenance Capture in Data Ingest Systems (SPCDIS) has added explanation provenance capabilities to an observational data ingest pipeline for images of the Sun providing a set of tools to answer diverseend user questions such as ``Why does this image look bad?.

Towards "Inverse" Character Tables? A One-Step Method for Decomposing Reducible Representations

ERIC Educational Resources Information Center

Piquemal, J.-Y.; Losno, R.; Ancian, B.

2009-01-01

In the framework of group theory, a new procedure is described for a one-step automated reduction of reducible representations. The matrix inversion tool, provided by standard spreadsheet software, is applied to the central part of the character table that contains the characters of the irreducible representation. This method is not restricted to…

Towards sustainable infrastructure management: knowledge-based service-oriented computing framework for visual analytics

NASA Astrophysics Data System (ADS)

Vatcha, Rashna; Lee, Seok-Won; Murty, Ajeet; Tolone, William; Wang, Xiaoyu; Dou, Wenwen; Chang, Remco; Ribarsky, William; Liu, Wanqiu; Chen, Shen-en; Hauser, Edd

2009-05-01

Infrastructure management (and its associated processes) is complex to understand, perform and thus, hard to make efficient and effective informed decisions. The management involves a multi-faceted operation that requires the most robust data fusion, visualization and decision making. In order to protect and build sustainable critical assets, we present our on-going multi-disciplinary large-scale project that establishes the Integrated Remote Sensing and Visualization (IRSV) system with a focus on supporting bridge structure inspection and management. This project involves specific expertise from civil engineers, computer scientists, geographers, and real-world practitioners from industry, local and federal government agencies. IRSV is being designed to accommodate the essential needs from the following aspects: 1) Better understanding and enforcement of complex inspection process that can bridge the gap between evidence gathering and decision making through the implementation of ontological knowledge engineering system; 2) Aggregation, representation and fusion of complex multi-layered heterogeneous data (i.e. infrared imaging, aerial photos and ground-mounted LIDAR etc.) with domain application knowledge to support machine understandable recommendation system; 3) Robust visualization techniques with large-scale analytical and interactive visualizations that support users' decision making; and 4) Integration of these needs through the flexible Service-oriented Architecture (SOA) framework to compose and provide services on-demand. IRSV is expected to serve as a management and data visualization tool for construction deliverable assurance and infrastructure monitoring both periodically (annually, monthly, even daily if needed) as well as after extreme events.

God-mother-baby: what children think they know.

PubMed

Kiessling, Florian; Perner, Josef

2014-01-01

This study tested one hundred and nine 3- to 6-year-old children on a knowledge-ignorance task about knowledge in humans (mother, baby) and God. In their responses, participants not reliably grasping that seeing leads to knowing in humans (pre-representational) were significantly influenced by own knowledge and marginally by question format. Moreover, knowledge was attributed significantly more often to mother than baby and explained by agent-based characteristics. Of participants mastering the task for humans (representational), God was largely conceived as ignorant "man in the sky" by younger and increasingly as "supernatural agent in the sky" by older children. Evidence for egocentrism and for anthropomorphizing God lends support to an anthropomorphism hypothesis. First-time evidence for an agent-based conception of others' knowledge in pre-representational children is presented. © 2013 The Authors. Child Development © 2013 Society for Research in Child Development, Inc.

Does GEM-Encoding Clinical Practice Guidelines Improve the Quality of Knowledge Bases? A Study with the Rule-Based Formalism

PubMed Central

Georg, Gersende; Séroussi, Brigitte; Bouaud, Jacques

2003-01-01

The aim of this work was to determine whether the GEM-encoding step could improve the representation of clinical practice guidelines as formalized knowledge bases. We used the 1999 Canadian recommendations for the management of hypertension, chosen as the knowledge source in the ASTI project. We first clarified semantic ambiguities of therapeutic sequences recommended in the guideline by proposing an interpretative framework of therapeutic strategies. Then, after a formalization step to standardize the terms used to characterize clinical situations, we created the GEM-encoded instance of the guideline. We developed a module for the automatic derivation of a rule base, BR-GEM, from the instance. BR-GEM was then compared to the rule base, BR-ASTI, embedded within the critic mode of ASTI, and manually built by two physicians from the same Canadian guideline. As compared to BR-ASTI, BR-GEM is more specific and covers more clinical situations. When evaluated on 10 patient cases, the GEM-based approach led to promising results. PMID:14728173

An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring.

PubMed

Alirezaie, Marjan; Kiselev, Andrey; Längkvist, Martin; Klügl, Franziska; Loutfi, Amy

2017-11-05

This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment-central Stockholm-in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as "find all regions close to schools and far from the flooded area". The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.

An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring

PubMed Central

Alirezaie, Marjan; Klügl, Franziska; Loutfi, Amy

2017-01-01

This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints. PMID:29113073

Interaction with Machine Improvisation

NASA Astrophysics Data System (ADS)

Assayag, Gerard; Bloch, George; Cont, Arshia; Dubnov, Shlomo

We describe two multi-agent architectures for an improvisation oriented musician-machine interaction systems that learn in real time from human performers. The improvisation kernel is based on sequence modeling and statistical learning. We present two frameworks of interaction with this kernel. In the first, the stylistic interaction is guided by a human operator in front of an interactive computer environment. In the second framework, the stylistic interaction is delegated to machine intelligence and therefore, knowledge propagation and decision are taken care of by the computer alone. The first framework involves a hybrid architecture using two popular composition/performance environments, Max and OpenMusic, that are put to work and communicate together, each one handling the process at a different time/memory scale. The second framework shares the same representational schemes with the first but uses an Active Learning architecture based on collaborative, competitive and memory-based learning to handle stylistic interactions. Both systems are capable of processing real-time audio/video as well as MIDI. After discussing the general cognitive background of improvisation practices, the statistical modelling tools and the concurrent agent architecture are presented. Then, an Active Learning scheme is described and considered in terms of using different improvisation regimes for improvisation planning. Finally, we provide more details about the different system implementations and describe several performances with the system.

Some Problems and Proposals for Knowledge Representation.

DTIC Science & Technology

1984-01-01

BROTHER(BiI, AI ) and FATHER( AI ,John) According to Woods, these both denote the fact that Bill is the uncle of John. However, we now must have two...34knowledge representation language being developed at the Berkeley Artificial Inteligience Research Project. KODIAK is an attempt to redress the above

Knowledge Representation in a Physics Tutor. COINS Technical Report 86-37.

ERIC Educational Resources Information Center

Murray, Tom; Woolf, Beverly

This paper is based on the idea that designing a knowledge representation for an intelligent physics computer tutoring system depends, in part, on the target behavior anticipated from the student. In addition, the document distinguishes between qualitative and quantitative competence in physics. These competencies are illustrated through questions…

Descriptive Analysis of the Graphic Representations of Science Textbooks

ERIC Educational Resources Information Center

Khine, Myint Swe; Liu, Yang

2017-01-01

Textbooks are primary teaching aids, sources from which students obtain knowledge of science domain. Due to this fact, curriculum developers in the field emphasize the crucial role of analysing the contents of science textbooks in improving science education. Scientific domain knowledge relies on graphical representations for the manifestation of…

Enhancing Conceptual Knowledge of Energy in Biology with Incorrect Representations

ERIC Educational Resources Information Center

Wernecke, Ulrike; Schütte, Kerstin; Schwanewedel, Julia; Harms, Ute

2018-01-01

Energy is an important concept in all natural sciences, and a challenging one for school science education. Students' conceptual knowledge of energy is often low, and they entertain misconceptions. Educational research in science and mathematics suggests that learning through depictive representations and learning from errors, based on the theory…

Software GOLUCA: Knowledge Representation in Mental Calculation

ERIC Educational Resources Information Center

Casas-Garcia, Luis M.; Luengo-Gonzalez, Ricardo; Godinho-Lopes, Vitor

2011-01-01

We present a new software, called Goluca (Godinho, Luengo, and Casas, 2007), based on the technique of Pathfinder Associative Networks (Schvaneveldt, 1989), which produces graphical representations of the cognitive structure of individuals in a given field knowledge. In this case, we studied the strategies used by teachers and its relationship…

Semantics vs. World Knowledge in Prefrontal Cortex

ERIC Educational Resources Information Center

Pylkkanen, Liina; Oliveri, Bridget; Smart, Andrew J.

2009-01-01

Humans have knowledge about the properties of their native language at various levels of representation; sound, structure, and meaning computation constitute the core components of any linguistic theory. Although the brain sciences have engaged with representational theories of sound and syntactic structure, the study of the neural bases of…

Systematic Representation of Knowledge of Ecology: Concepts and Relationships.

ERIC Educational Resources Information Center

Garb, Yaakov; And Others

This study describes efforts to apply principles of systematic knowledge representation (concept mapping and computer-based semantic networking techniques) to the domain of ecology. A set of 24 relationships and modifiers is presented that seem sufficient for describing all ecological relationships discussed in an introductory course. Many of…

An individual differences approach to semantic cognition: Divergent effects of age on representation, retrieval and selection.

PubMed

Hoffman, Paul

2018-05-25

Semantic cognition refers to the appropriate use of acquired knowledge about the world. This requires representation of knowledge as well as control processes which ensure that currently-relevant aspects of knowledge are retrieved and selected. Although these abilities can be impaired selectively following brain damage, the relationship between them in healthy individuals is unclear. It is also commonly assumed that semantic cognition is preserved in later life, because older people have greater reserves of knowledge. However, this claim overlooks the possibility of decline in semantic control processes. Here, semantic cognition was assessed in 100 young and older adults. Despite having a broader knowledge base, older people showed specific impairments in semantic control, performing more poorly than young people when selecting among competing semantic representations. Conversely, they showed preserved controlled retrieval of less salient information from the semantic store. Breadth of semantic knowledge was positively correlated with controlled retrieval but was unrelated to semantic selection ability, which was instead correlated with non-semantic executive function. These findings indicate that three distinct elements contribute to semantic cognition: semantic representations that accumulate throughout the lifespan, processes for controlled retrieval of less salient semantic information, which appear age-invariant, and mechanisms for selecting task-relevant aspects of semantic knowledge, which decline with age and may relate more closely to domain-general executive control.

Making Connections: Elementary Teachers' Construction of Division Word Problems and Representations

ERIC Educational Resources Information Center

Timmerman, Maria A.

2014-01-01

If teachers make few connections among multiple representations of division, supporting students in using representations to develop operation sense demanded by national standards will not occur. Studies have investigated how prospective and practicing teachers use representations to develop knowledge of fraction division. However, few studies…

Playing Linear Number Board Games Improves Children's Mathematical Knowledge

ERIC Educational Resources Information Center

Siegler, Robert S.; Ramani, Geetha

2009-01-01

The present study focused on two main goals. One was to test the "representational mapping hypothesis": The greater the transparency of the mapping between physical materials and desired internal representations, the greater the learning of the desired internal representation. The implication of the representational mapping hypothesis in the…

linkedISA: semantic representation of ISA-Tab experimental metadata.

PubMed

González-Beltrán, Alejandra; Maguire, Eamonn; Sansone, Susanna-Assunta; Rocca-Serra, Philippe

2014-01-01

Reporting and sharing experimental metadata- such as the experimental design, characteristics of the samples, and procedures applied, along with the analysis results, in a standardised manner ensures that datasets are comprehensible and, in principle, reproducible, comparable and reusable. Furthermore, sharing datasets in formats designed for consumption by humans and machines will also maximize their use. The Investigation/Study/Assay (ISA) open source metadata tracking framework facilitates standards-compliant collection, curation, visualization, storage and sharing of datasets, leveraging on other platforms to enable analysis and publication. The ISA software suite includes several components used in increasingly diverse set of life science and biomedical domains; it is underpinned by a general-purpose format, ISA-Tab, and conversions exist into formats required by public repositories. While ISA-Tab works well mainly as a human readable format, we have also implemented a linked data approach to semantically define the ISA-Tab syntax. We present a semantic web representation of the ISA-Tab syntax that complements ISA-Tab's syntactic interoperability with semantic interoperability. We introduce the linkedISA conversion tool from ISA-Tab to the Resource Description Framework (RDF), supporting mappings from the ISA syntax to multiple community-defined, open ontologies and capitalising on user-provided ontology annotations in the experimental metadata. We describe insights of the implementation and how annotations can be expanded driven by the metadata. We applied the conversion tool as part of Bio-GraphIIn, a web-based application supporting integration of the semantically-rich experimental descriptions. Designed in a user-friendly manner, the Bio-GraphIIn interface hides most of the complexities to the users, exposing a familiar tabular view of the experimental description to allow seamless interaction with the RDF representation, and visualising descriptors to drive the query over the semantic representation of the experimental design. In addition, we defined queries over the linkedISA RDF representation and demonstrated its use over the linkedISA conversion of datasets from Nature' Scientific Data online publication. Our linked data approach has allowed us to: 1) make the ISA-Tab semantics explicit and machine-processable, 2) exploit the existing ontology-based annotations in the ISA-Tab experimental descriptions, 3) augment the ISA-Tab syntax with new descriptive elements, 4) visualise and query elements related to the experimental design. Reasoning over ISA-Tab metadata and associated data will facilitate data integration and knowledge discovery.

Neuroconstructivism

ERIC Educational Resources Information Center

Westermann, Gert; Mareschal, Denis; Johnson, Mark H.; Sirois, Sylvain; Spratling, Michael W.; Thomas, Michael S. C.

2007-01-01

Neuroconstructivism is a theoretical framework focusing on the construction of representations in the developing brain. Cognitive development is explained as emerging from the experience-dependent development of neural structures supporting mental representations. Neural development occurs in the context of multiple interacting constraints acting…

Operator algebra as an application of logarithmic representation of infinitesimal generators

NASA Astrophysics Data System (ADS)

Iwata, Yoritaka

2018-02-01

The operator algebra is introduced based on the framework of logarithmic representation of infinitesimal generators. In conclusion a set of generally-unbounded infinitesimal generators is characterized as a module over the Banach algebra.

Activity inference for Ambient Intelligence through handling artifacts in a healthcare environment.

PubMed

Martínez-Pérez, Francisco E; González-Fraga, Jose Ángel; Cuevas-Tello, Juan C; Rodríguez, Marcela D

2012-01-01

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user.

Activity Inference for Ambient Intelligence Through Handling Artifacts in a Healthcare Environment

PubMed Central

Martínez-Pérez, Francisco E.; González-Fraga, Jose Ángel; Cuevas-Tello, Juan C.; Rodríguez, Marcela D.

2012-01-01

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user. PMID:22368512

An embedded system for face classification in infrared video using sparse representation

NASA Astrophysics Data System (ADS)

Saavedra M., Antonio; Pezoa, Jorge E.; Zarkesh-Ha, Payman; Figueroa, Miguel

2017-09-01

We propose a platform for robust face recognition in Infrared (IR) images using Compressive Sensing (CS). In line with CS theory, the classification problem is solved using a sparse representation framework, where test images are modeled by means of a linear combination of the training set. Because the training set constitutes an over-complete dictionary, we identify new images by finding their sparsest representation based on the training set, using standard l1-minimization algorithms. Unlike conventional face-recognition algorithms, we feature extraction is performed using random projections with a precomputed binary matrix, as proposed in the CS literature. This random sampling reduces the effects of noise and occlusions such as facial hair, eyeglasses, and disguises, which are notoriously challenging in IR images. Thus, the performance of our framework is robust to these noise and occlusion factors, achieving an average accuracy of approximately 90% when the UCHThermalFace database is used for training and testing purposes. We implemented our framework on a high-performance embedded digital system, where the computation of the sparse representation of IR images was performed by a dedicated hardware using a deeply pipelined architecture on an Field-Programmable Gate Array (FPGA).

Representational Approach: A Conceptual Framework to Guide Patient Education Research and Practice.

PubMed

Arida, Janet A; Sherwood, Paula R; Flannery, Marie; Donovan, Heidi S

2016-11-01

Illness representations are cognitive structures that individuals rely on to understand and explain their illnesses and associated symptoms. The Representational Approach (RA) to patient education offers a theoretically based, clinically useful model that can support oncology nurses to develop a shared understanding of patients' illness representations to collaboratively develop highly personalized plans for symptom management and other important self-management behaviors. This article discusses theoretical underpinnings, practical applications, challenges, and future directions for incorporating illness representations and the RA in clinical and research endeavors.

Complex networks as a unified framework for descriptive analysis and predictive modeling in climate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steinhaeuser, Karsten J K; Chawla, Nitesh; Ganguly, Auroop R

The analysis of climate data has relied heavily on hypothesis-driven statistical methods, while projections of future climate are based primarily on physics-based computational models. However, in recent years a wealth of new datasets has become available. Therefore, we take a more data-centric approach and propose a unified framework for studying climate, with an aim towards characterizing observed phenomena as well as discovering new knowledge in the climate domain. Specifically, we posit that complex networks are well-suited for both descriptive analysis and predictive modeling tasks. We show that the structural properties of climate networks have useful interpretation within the domain. Further,more » we extract clusters from these networks and demonstrate their predictive power as climate indices. Our experimental results establish that the network clusters are statistically significantly better predictors than clusters derived using a more traditional clustering approach. Using complex networks as data representation thus enables the unique opportunity for descriptive and predictive modeling to inform each other.« less

Contextual Variability in Personality From Significant–Other Knowledge and Relational Selves

PubMed Central

Andersen, Susan M.; Tuskeviciute, Rugile; Przybylinski, Elizabeth; Ahn, Janet N.; Xu, Joy H.

2016-01-01

We argue that the self is intrinsically embedded in an interpersonal context such that it varies in IF–THEN terms, as the relational self. We have demonstrated that representations of the significant other and the relationship with that other are automatically activated by situational cues and that this activation affects both experienced and expressed aspects of the self and personality. Here, we expand on developments of the IF–THEN cognitive-affective framework of personality system (Mischel and Shoda, 1995), by extending it to the domain of interpersonal relationships at the dyadic level (Andersen and Chen, 2002). Going beyond Mischel’s early research (Mischel, 1968), our framework combines social cognition and learning theory with a learning-based psychodynamic approach, which provides the basis for extensive research on the social-cognitive process of transference and the relational self as it arises in everyday social interactions (Andersen and Cole, 1990), evidence from which contributes to a modern conceptualization of personality that emphasizes the centrality of the situation. PMID:26779051

Modular Knowledge Representation and Reasoning in the Semantic Web

NASA Astrophysics Data System (ADS)

Serafini, Luciano; Homola, Martin

Construction of modular ontologies by combining different modules is becoming a necessity in ontology engineering in order to cope with the increasing complexity of the ontologies and the domains they represent. The modular ontology approach takes inspiration from software engineering, where modularization is a widely acknowledged feature. Distributed reasoning is the other side of the coin of modular ontologies: given an ontology comprising of a set of modules, it is desired to perform reasoning by combination of multiple reasoning processes performed locally on each of the modules. In the last ten years, a number of approaches for combining logics has been developed in order to formalize modular ontologies. In this chapter, we survey and compare the main formalisms for modular ontologies and distributed reasoning in the Semantic Web. We select four formalisms build on formal logical grounds of Description Logics: Distributed Description Logics, ℰ-connections, Package-based Description Logics and Integrated Distributed Description Logics. We concentrate on expressivity and distinctive modeling features of each framework. We also discuss reasoning capabilities of each framework.

Technical note: The Linked Paleo Data framework - a common tongue for paleoclimatology

NASA Astrophysics Data System (ADS)

McKay, Nicholas P.; Emile-Geay, Julien

2016-04-01

Paleoclimatology is a highly collaborative scientific endeavor, increasingly reliant on online databases for data sharing. Yet there is currently no universal way to describe, store and share paleoclimate data: in other words, no standard. Data standards are often regarded by scientists as mere technicalities, though they underlie much scientific and technological innovation, as well as facilitating collaborations between research groups. In this article, we propose a preliminary data standard for paleoclimate data, general enough to accommodate all the archive and measurement types encountered in a large international collaboration (PAGES 2k). We also introduce a vehicle for such structured data (Linked Paleo Data, or LiPD), leveraging recent advances in knowledge representation (Linked Open Data).The LiPD framework enables quick querying and extraction, and we expect that it will facilitate the writing of open-source community codes to access, analyze, model and visualize paleoclimate observations. We welcome community feedback on this standard, and encourage paleoclimatologists to experiment with the format for their own purposes.

Contextual Variability in Personality From Significant-Other Knowledge and Relational Selves.

PubMed

Andersen, Susan M; Tuskeviciute, Rugile; Przybylinski, Elizabeth; Ahn, Janet N; Xu, Joy H

2015-01-01

We argue that the self is intrinsically embedded in an interpersonal context such that it varies in IF-THEN terms, as the relational self. We have demonstrated that representations of the significant other and the relationship with that other are automatically activated by situational cues and that this activation affects both experienced and expressed aspects of the self and personality. Here, we expand on developments of the IF-THEN cognitive-affective framework of personality system (Mischel and Shoda, 1995), by extending it to the domain of interpersonal relationships at the dyadic level (Andersen and Chen, 2002). Going beyond Mischel's early research (Mischel, 1968), our framework combines social cognition and learning theory with a learning-based psychodynamic approach, which provides the basis for extensive research on the social-cognitive process of transference and the relational self as it arises in everyday social interactions (Andersen and Cole, 1990), evidence from which contributes to a modern conceptualization of personality that emphasizes the centrality of the situation.

Integration of object-oriented knowledge representation with the CLIPS rule based system

NASA Technical Reports Server (NTRS)

Logie, David S.; Kamil, Hasan

1990-01-01

The paper describes a portion of the work aimed at developing an integrated, knowledge based environment for the development of engineering-oriented applications. An Object Representation Language (ORL) was implemented in C++ which is used to build and modify an object-oriented knowledge base. The ORL was designed in such a way so as to be easily integrated with other representation schemes that could effectively reason with the object base. Specifically, the integration of the ORL with the rule based system C Language Production Systems (CLIPS), developed at the NASA Johnson Space Center, will be discussed. The object-oriented knowledge representation provides a natural means of representing problem data as a collection of related objects. Objects are comprised of descriptive properties and interrelationships. The object-oriented model promotes efficient handling of the problem data by allowing knowledge to be encapsulated in objects. Data is inherited through an object network via the relationship links. Together, the two schemes complement each other in that the object-oriented approach efficiently handles problem data while the rule based knowledge is used to simulate the reasoning process. Alone, the object based knowledge is little more than an object-oriented data storage scheme; however, the CLIPS inference engine adds the mechanism to directly and automatically reason with that knowledge. In this hybrid scheme, the expert system dynamically queries for data and can modify the object base with complete access to all the functionality of the ORL from rules.

From Data to Knowledge through Concept-oriented Terminologies

PubMed Central

Cimino, James J.

2000-01-01

Knowledge representation involves enumeration of conceptual symbols and arrangement of these symbols into some meaningful structure. Medical knowledge representation has traditionally focused more on the structure than the symbols. Several significant efforts are under way, at local, national, and international levels, to address the representation of the symbols though the creation of high-quality terminologies that are themselves knowledge based. This paper reviews these efforts, including the Medical Entities Dictionary (MED) in use at Columbia University and the New York Presbyterian Hospital. A decade's experience with the MED is summarized to serve as a proof-of-concept that knowledge-based terminologies can support the use of coded patient data for a variety of knowledge-based activities, including the improved understanding of patient data, the access of information sources relevant to specific patient care problems, the application of expert systems directly to the care of patients, and the discovery of new medical knowledge. The terminological knowledge in the MED has also been used successfully to support clinical application development and maintenance, including that of the MED itself. On the basis of this experience, current efforts to create standard knowledge-based terminologies appear to be justified. PMID:10833166

From data to knowledge through concept-oriented terminologies: experience with the Medical Entities Dictionary.

PubMed

Cimino, J J

2000-01-01

Knowledge representation involves enumeration of conceptual symbols and arrangement of these symbols into some meaningful structure. Medical knowledge representation has traditionally focused more on the structure than the symbols. Several significant efforts are under way, at local, national, and international levels, to address the representation of the symbols though the creation of high-quality terminologies that are themselves knowledge based. This paper reviews these efforts, including the Medical Entities Dictionary (MED) in use at Columbia University and the New York Presbyterian Hospital. A decade's experience with the MED is summarized to serve as a proof-of-concept that knowledge-based terminologies can support the use of coded patient data for a variety of knowledge-based activities, including the improved understanding of patient data, the access of information sources relevant to specific patient care problems, the application of expert systems directly to the care of patients, and the discovery of new medical knowledge. The terminological knowledge in the MED has also been used successfully to support clinical application development and maintenance, including that of the MED itself. On the basis of this experience, current efforts to create standard knowledge-based terminologies appear to be justified.

Inter-level Scaffolding and Sequences of Representational Activities in Teaching a Chemical System with Graphical Simulations

NASA Astrophysics Data System (ADS)

Li, Na; Black, John B.

2016-10-01

Chemistry knowledge can be represented at macro-, micro- and symbolic levels, and learning a chemistry topic requires students to engage in multiple representational activities. This study focused on scaffolding for inter-level connection-making in learning chemistry knowledge with graphical simulations. We also tested whether different sequences of representational activities produced different student learning outcomes in learning a chemistry topic. A sample of 129 seventh graders participated in this study. In a simulation-based environment, participants completed three representational activities to learn several ideal gas law concepts. We conducted a 2 × 3 factorial design experiment. We compared two scaffolding conditions: (1) the inter- level scaffolding condition in which participants received inter-level questions and experienced the dynamic link function in the simulation-based environment and (2) the intra- level scaffolding condition in which participants received intra-level questions and did not experience the dynamic link function. We also compared three different sequences of representational activities: macro-symbolic-micro, micro-symbolic-macro and symbolic-micro-macro. For the scaffolding variable, we found that the inter- level scaffolding condition produced significantly better performance in both knowledge comprehension and application, compared to the intra- level scaffolding condition. For the sequence variable, we found that the macro-symbolic-micro sequence produced significantly better knowledge comprehension performance than the other two sequences; however, it did not benefit knowledge application performance. There was a trend that the treatment group who experienced inter- level scaffolding and the micro-symbolic-macro sequence achieved the best knowledge application performance.

Knowledge representation in space flight operations

NASA Technical Reports Server (NTRS)

Busse, Carl

1989-01-01

In space flight operations rapid understanding of the state of the space vehicle is essential. Representation of knowledge depicting space vehicle status in a dynamic environment presents a difficult challenge. The NASA Jet Propulsion Laboratory has pursued areas of technology associated with the advancement of spacecraft operations environment. This has led to the development of several advanced mission systems which incorporate enhanced graphics capabilities. These systems include: (1) Spacecraft Health Automated Reasoning Prototype (SHARP); (2) Spacecraft Monitoring Environment (SME); (3) Electrical Power Data Monitor (EPDM); (4) Generic Payload Operations Control Center (GPOCC); and (5) Telemetry System Monitor Prototype (TSM). Knowledge representation in these systems provides a direct representation of the intrinsic images associated with the instrument and satellite telemetry and telecommunications systems. The man-machine interface includes easily interpreted contextual graphic displays. These interactive video displays contain multiple display screens with pop-up windows and intelligent, high resolution graphics linked through context and mouse-sensitive icons and text.

Secure Base Narrative Representations and Intimate Partner Violence: A Dyadic Perspective

PubMed Central

Karakurt, Gunnur; Silver, Kristin E.; Keiley, Margaret K.

2015-01-01

This study aimed to understand the relationship between secure base phenomena and dating violence among couples. Within a relationship, a secure base can be defined as a balancing act of proximity-seeking and exploration at various times and contexts with the assurance of a caregiver’s availability and responsiveness in emotionally distressing situations. Participants were 87 heterosexual couples. The Actor-Partner Interdependence Model was used to examine the relationship between each partner’s scores on secure base representational knowledge and intimate partner violence. Findings demonstrated that women’s secure base representational knowledge had a significant direct negative effect on the victimization of both men and women, while men’s secure base representational knowledge did not have any significant partner or actor effects. Therefore, findings suggest that women with insecure attachments may be more vulnerable to being both the victims and the perpetrators of PMID:27445432

The Digital electronic Guideline Library (DeGeL): a hybrid framework for representation and use of clinical guidelines.

PubMed

Shahar, Yuval; Young, Ohad; Shalom, Erez; Mayaffit, Alon; Moskovitch, Robert; Hessing, Alon; Galperin, Maya

2004-01-01

We propose to present a poster (and potentially also a demonstration of the implemented system) summarizing the current state of our work on a hybrid, multiple-format representation of clinical guidelines that facilitates conversion of guidelines from free text to a formal representation. We describe a distributed Web-based architecture (DeGeL) and a set of tools using the hybrid representation. The tools enable performing tasks such as guideline specification, semantic markup, search, retrieval, visualization, eligibility determination, runtime application and retrospective quality assessment. The representation includes four parallel formats: Free text (one or more original sources); semistructured text (labeled by the target guideline-ontology semantic labels); semiformal text (which includes some control specification); and a formal, machine-executable representation. The specification, indexing, search, retrieval, and browsing tools are essentially independent of the ontology chosen for guideline representation, but editing the semi-formal and formal formats requires ontology-specific tools, which we have developed in the case of the Asbru guideline-specification language. The four formats support increasingly sophisticated computational tasks. The hybrid guidelines are stored in a Web-based library. All tools, such as for runtime guideline application or retrospective quality assessment, are designed to operate on all representations. We demonstrate the hybrid framework by providing examples from the semantic markup and search tools.

OpenTox predictive toxicology framework: toxicological ontology and semantic media wiki-based OpenToxipedia

PubMed Central

2012-01-01

Background The OpenTox Framework, developed by the partners in the OpenTox project (http://www.opentox.org), aims at providing a unified access to toxicity data, predictive models and validation procedures. Interoperability of resources is achieved using a common information model, based on the OpenTox ontologies, describing predictive algorithms, models and toxicity data. As toxicological data may come from different, heterogeneous sources, a deployed ontology, unifying the terminology and the resources, is critical for the rational and reliable organization of the data, and its automatic processing. Results The following related ontologies have been developed for OpenTox: a) Toxicological ontology – listing the toxicological endpoints; b) Organs system and Effects ontology – addressing organs, targets/examinations and effects observed in in vivo studies; c) ToxML ontology – representing semi-automatic conversion of the ToxML schema; d) OpenTox ontology– representation of OpenTox framework components: chemical compounds, datasets, types of algorithms, models and validation web services; e) ToxLink–ToxCast assays ontology and f) OpenToxipedia community knowledge resource on toxicology terminology. OpenTox components are made available through standardized REST web services, where every compound, data set, and predictive method has a unique resolvable address (URI), used to retrieve its Resource Description Framework (RDF) representation, or to initiate the associated calculations and generate new RDF-based resources. The services support the integration of toxicity and chemical data from various sources, the generation and validation of computer models for toxic effects, seamless integration of new algorithms and scientifically sound validation routines and provide a flexible framework, which allows building arbitrary number of applications, tailored to solving different problems by end users (e.g. toxicologists). Availability The OpenTox toxicological ontology projects may be accessed via the OpenTox ontology development page http://www.opentox.org/dev/ontology; the OpenTox ontology is available as OWL at http://opentox.org/api/1 1/opentox.owl, the ToxML - OWL conversion utility is an open source resource available at http://ambit.svn.sourceforge.net/viewvc/ambit/branches/toxml-utils/ PMID:22541598

OpenTox predictive toxicology framework: toxicological ontology and semantic media wiki-based OpenToxipedia.

PubMed

Tcheremenskaia, Olga; Benigni, Romualdo; Nikolova, Ivelina; Jeliazkova, Nina; Escher, Sylvia E; Batke, Monika; Baier, Thomas; Poroikov, Vladimir; Lagunin, Alexey; Rautenberg, Micha; Hardy, Barry

2012-04-24

The OpenTox Framework, developed by the partners in the OpenTox project (http://www.opentox.org), aims at providing a unified access to toxicity data, predictive models and validation procedures. Interoperability of resources is achieved using a common information model, based on the OpenTox ontologies, describing predictive algorithms, models and toxicity data. As toxicological data may come from different, heterogeneous sources, a deployed ontology, unifying the terminology and the resources, is critical for the rational and reliable organization of the data, and its automatic processing. The following related ontologies have been developed for OpenTox: a) Toxicological ontology - listing the toxicological endpoints; b) Organs system and Effects ontology - addressing organs, targets/examinations and effects observed in in vivo studies; c) ToxML ontology - representing semi-automatic conversion of the ToxML schema; d) OpenTox ontology- representation of OpenTox framework components: chemical compounds, datasets, types of algorithms, models and validation web services; e) ToxLink-ToxCast assays ontology and f) OpenToxipedia community knowledge resource on toxicology terminology.OpenTox components are made available through standardized REST web services, where every compound, data set, and predictive method has a unique resolvable address (URI), used to retrieve its Resource Description Framework (RDF) representation, or to initiate the associated calculations and generate new RDF-based resources.The services support the integration of toxicity and chemical data from various sources, the generation and validation of computer models for toxic effects, seamless integration of new algorithms and scientifically sound validation routines and provide a flexible framework, which allows building arbitrary number of applications, tailored to solving different problems by end users (e.g. toxicologists). The OpenTox toxicological ontology projects may be accessed via the OpenTox ontology development page http://www.opentox.org/dev/ontology; the OpenTox ontology is available as OWL at http://opentox.org/api/1 1/opentox.owl, the ToxML - OWL conversion utility is an open source resource available at http://ambit.svn.sourceforge.net/viewvc/ambit/branches/toxml-utils/

Matrix product representation of the stationary state of the open zero range process

NASA Astrophysics Data System (ADS)

Bertin, Eric; Vanicat, Matthieu

2018-06-01

Many one-dimensional lattice particle models with open boundaries, like the paradigmatic asymmetric simple exclusion process (ASEP), have their stationary states represented in the form of a matrix product, with matrices that do not explicitly depend on the lattice site. In contrast, the stationary state of the open 1D zero-range process (ZRP) takes an inhomogeneous factorized form, with site-dependent probability weights. We show that in spite of the absence of correlations, the stationary state of the open ZRP can also be represented in a matrix product form, where the matrices are site-independent, non-commuting and determined from algebraic relations resulting from the master equation. We recover the known distribution of the open ZRP in two different ways: first, using an explicit representation of the matrices and boundary vectors; second, from the sole knowledge of the algebraic relations satisfied by these matrices and vectors. Finally, an interpretation of the relation between the matrix product form and the inhomogeneous factorized form is proposed within the framework of hidden Markov chains.

[Comparative study of needs of transplanted patients or those waiting for an organ transplantation and the nurses' attitudes of these needs].

PubMed

Baert, C; Cocula, N; Delran, J; Faubel, E; Foucaud, C; Martins, V

2000-12-01

Literature has shown that information, education and support had a beneficial effect on how the patients and their family lived through the transplant process. In our daily practice, we are permanently confronted with requests for information and psychological adjustments from our patients. Do the needs of this population meet the representations of the care-takers? Our theoretical framework is based on the theories of Maslow and Callista Roy, on the concept of social representations according to Moscovici and on the steps of the transplant process. To carry out this survey, we used a questionnaire which we gave to the patients at the different phases of the graft and to the nurses of the services involved in the transplant. There was a similarity of the results between the two populations, despite some differences for certain items. The development of a programme for information and for education will enable an improvement of the care quality thanks to the adaptation of knowledge to the needs of the transplanted patients.

A critical analysis of the Australian Defence Force policy on maternal health care.

PubMed

Montalban, Maureen

2017-08-01

To critically analyse the Australian Defence Force (ADF) policy on maternal health care: Health Directive No 235 - Management of pregnant members in the Australian Defence Force. Bacchi's 'What's the problem represented to be' framework was used to analyse Health Directive No 235. This paper critically examines the representation of pregnancy and birth, the resulting effects and considers alternate representations. The ADF's policy on maternal healthcare considers pregnancy as a health issue that requires specialist intervention and care, also known as the medicalisation of birth. Current research emphasises women-centred care; a model of care not contained in the ADF policy. The problematisation of pregnancy in the ADF restricts women's choices regarding their maternal healthcare provider. This is contrary to the healthcare rights of Australians and likely contributes to health inequalities of ADF women. Implications for public health: A research gap regarding ADF women's knowledge and wishes regarding their maternal health care has been identified. Future research can inform any alterations to the ADF policy on maternal healthcare. © 2017 The Authors.

Probabilistic Graphical Model Representation in Phylogenetics

PubMed Central

Höhna, Sebastian; Heath, Tracy A.; Boussau, Bastien; Landis, Michael J.; Ronquist, Fredrik; Huelsenbeck, John P.

2014-01-01

Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (i) reproducibility of an analysis, (ii) model development, and (iii) software design. Moreover, a unified, clear and understandable framework for model representation lowers the barrier for beginners and nonspecialists to grasp complex phylogenetic models, including their assumptions and parameter/variable dependencies. Graphical modeling is a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea is to break complex models into conditionally independent distributions. The strength lies in the comprehensibility, flexibility, and adaptability of this formalism, and the large body of computational work based on it. Graphical models are well-suited to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference. Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and introduce modules to simplify the representation of standard components in large and complex models. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using, for example, Metropolis–Hastings or Gibbs sampling of the posterior distribution. [Computation; graphical models; inference; modularization; statistical phylogenetics; tree plate.] PMID:24951559

Computational neuroanatomy: ontology-based representation of neural components and connectivity.

PubMed

Rubin, Daniel L; Talos, Ion-Florin; Halle, Michael; Musen, Mark A; Kikinis, Ron

2009-02-05

A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning. We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems. We built a prototype model of the motor system that integrates descriptive anatomic and qualitative functional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, our approach provides an explicit representation of abnormal neural connectivity in disease states, such as common movement disorders. The ontology-based representation encodes both structural and functional aspects of neuroanatomy. The ontology-based models can be evaluated computationally, enabling development of automated computer reasoning applications. Neuroanatomical knowledge can be represented in machine-accessible format using ontologies. Computational neuroanatomical approaches such as described in this work could become a key tool in translational informatics, leading to decision support applications that inform and guide surgical planning and personalized care for neurological disease in the future.

Understanding Deep Representations Learned in Modeling Users Likes.

PubMed

Guntuku, Sharath Chandra; Zhou, Joey Tianyi; Roy, Sujoy; Lin, Weisi; Tsang, Ivor W

2016-08-01

Automatically understanding and discriminating different users' liking for an image is a challenging problem. This is because the relationship between image features (even semantic ones extracted by existing tools, viz., faces, objects, and so on) and users' likes is non-linear, influenced by several subtle factors. This paper presents a deep bi-modal knowledge representation of images based on their visual content and associated tags (text). A mapping step between the different levels of visual and textual representations allows for the transfer of semantic knowledge between the two modalities. Feature selection is applied before learning deep representation to identify the important features for a user to like an image. The proposed representation is shown to be effective in discriminating users based on images they like and also in recommending images that a given user likes, outperforming the state-of-the-art feature representations by  ∼ 15 %-20%. Beyond this test-set performance, an attempt is made to qualitatively understand the representations learned by the deep architecture used to model user likes.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine.

PubMed

Maier, Dieter; Kalus, Wenzel; Wolff, Martin; Kalko, Susana G; Roca, Josep; Marin de Mas, Igor; Turan, Nil; Cascante, Marta; Falciani, Francesco; Hernandez, Miguel; Villà-Freixa, Jordi; Losko, Sascha

2011-03-05

To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype-phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene--disease and gene--compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

PubMed Central

2011-01-01

Background To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development. PMID:21375767

What can biochemistry students learn about protein translation? Using variation theory to explore the space of learning created by some common external representations

NASA Astrophysics Data System (ADS)

Bussey, Thomas J.

Biochemistry education relies heavily on students' ability to visualize abstract cellular and molecular processes, mechanisms, and components. As such, biochemistry educators often turn to external representations to provide tangible, working models from which students' internal representations (mental models) can be constructed, evaluated, and revised. However, prior research has shown that, while potentially beneficial, external representations can also lead to alternative student conceptions. Considering the breadth of biochemical phenomena, protein translation has been identified as an essential biochemical process and can subsequently be considered a fundamental concept for biochemistry students to learn. External representations of translation range from static diagrams to dynamic animations, from simplistic, stylized illustrations to more complex, realistic presentations. In order to explore the potential for student learning about protein translation from some common external representations of translation, I used variation theory. Variation theory offers a theoretical framework from which to explore what is intended for students to learn, what is possible for students to learn, and what students actually learn about an object of learning, e.g., protein translation. The goals of this project were threefold. First, I wanted to identify instructors' intentions for student learning about protein translation. From a phenomenographic analysis of instructor interviews, I was able to determine the critical features instructors felt their students should be learning. Second, I wanted to determine which features of protein translation were possible for students to learn from some common external representations of the process. From a variation analysis of the three representations shown to students, I was able to describe the possible combinations of features enacted by the sequential viewing of pairs of representations. Third, I wanted to identify what students actually learned about protein translation by viewing these external representations. From a phenomenographic analysis of student interviews, I was able to describe changes between students prior lived object of learning and their post lived object of learning. Based on the findings from this project, I can conclude that variation can be used to cue students to notice particular features of an external representation. Additionally, students' prior knowledge and, potentially, the intended objects of learning from previous instructors can also affect what students can learn from a representation. Finally, further study is needed to identify the extent to which mode and level of abstraction of an external representation affect student learning outcomes.

An Analysis of Different Representations for Vectors and Planes in R[superscript 3]: Learning Challenges

ERIC Educational Resources Information Center

Sandoval, Ivonne; Possani, Edgar

2016-01-01

The purpose of this paper is to present an analysis of the difficulties faced by students when working with different representations of vectors, planes and their intersections in R[superscript 3]. Duval's theoretical framework on semiotic representations is used to design a set of evaluating activities, and later to analyze student work. The…

Dynamic speech representations in the human temporal lobe.

PubMed

Leonard, Matthew K; Chang, Edward F

2014-09-01

Speech perception requires rapid integration of acoustic input with context-dependent knowledge. Recent methodological advances have allowed researchers to identify underlying information representations in primary and secondary auditory cortex and to examine how context modulates these representations. We review recent studies that focus on contextual modulations of neural activity in the superior temporal gyrus (STG), a major hub for spectrotemporal encoding. Recent findings suggest a highly interactive flow of information processing through the auditory ventral stream, including influences of higher-level linguistic and metalinguistic knowledge, even within individual areas. Such mechanisms may give rise to more abstract representations, such as those for words. We discuss the importance of characterizing representations of context-dependent and dynamic patterns of neural activity in the approach to speech perception research. Copyright © 2014 Elsevier Ltd. All rights reserved.

Improving the learning of clinical reasoning through computer-based cognitive representation.

PubMed

Wu, Bian; Wang, Minhong; Johnson, Janice M; Grotzer, Tina A

2014-01-01

Objective Clinical reasoning is usually taught using a problem-solving approach, which is widely adopted in medical education. However, learning through problem solving is difficult as a result of the contextualization and dynamic aspects of actual problems. Moreover, knowledge acquired from problem-solving practice tends to be inert and fragmented. This study proposed a computer-based cognitive representation approach that externalizes and facilitates the complex processes in learning clinical reasoning. The approach is operationalized in a computer-based cognitive representation tool that involves argument mapping to externalize the problem-solving process and concept mapping to reveal the knowledge constructed from the problems. Methods Twenty-nine Year 3 or higher students from a medical school in east China participated in the study. Participants used the proposed approach implemented in an e-learning system to complete four learning cases in 4 weeks on an individual basis. For each case, students interacted with the problem to capture critical data, generate and justify hypotheses, make a diagnosis, recall relevant knowledge, and update their conceptual understanding of the problem domain. Meanwhile, students used the computer-based cognitive representation tool to articulate and represent the key elements and their interactions in the learning process. Results A significant improvement was found in students' learning products from the beginning to the end of the study, consistent with students' report of close-to-moderate progress in developing problem-solving and knowledge-construction abilities. No significant differences were found between the pretest and posttest scores with the 4-week period. The cognitive representation approach was found to provide more formative assessment. Conclusions The computer-based cognitive representation approach improved the learning of clinical reasoning in both problem solving and knowledge construction.

Improving the learning of clinical reasoning through computer-based cognitive representation

PubMed Central

Wu, Bian; Wang, Minhong; Johnson, Janice M.; Grotzer, Tina A.

2014-01-01

Objective Clinical reasoning is usually taught using a problem-solving approach, which is widely adopted in medical education. However, learning through problem solving is difficult as a result of the contextualization and dynamic aspects of actual problems. Moreover, knowledge acquired from problem-solving practice tends to be inert and fragmented. This study proposed a computer-based cognitive representation approach that externalizes and facilitates the complex processes in learning clinical reasoning. The approach is operationalized in a computer-based cognitive representation tool that involves argument mapping to externalize the problem-solving process and concept mapping to reveal the knowledge constructed from the problems. Methods Twenty-nine Year 3 or higher students from a medical school in east China participated in the study. Participants used the proposed approach implemented in an e-learning system to complete four learning cases in 4 weeks on an individual basis. For each case, students interacted with the problem to capture critical data, generate and justify hypotheses, make a diagnosis, recall relevant knowledge, and update their conceptual understanding of the problem domain. Meanwhile, students used the computer-based cognitive representation tool to articulate and represent the key elements and their interactions in the learning process. Results A significant improvement was found in students’ learning products from the beginning to the end of the study, consistent with students’ report of close-to-moderate progress in developing problem-solving and knowledge-construction abilities. No significant differences were found between the pretest and posttest scores with the 4-week period. The cognitive representation approach was found to provide more formative assessment. Conclusions The computer-based cognitive representation approach improved the learning of clinical reasoning in both problem solving and knowledge construction. PMID:25518871

Improving the learning of clinical reasoning through computer-based cognitive representation.

PubMed

Wu, Bian; Wang, Minhong; Johnson, Janice M; Grotzer, Tina A

2014-01-01

Clinical reasoning is usually taught using a problem-solving approach, which is widely adopted in medical education. However, learning through problem solving is difficult as a result of the contextualization and dynamic aspects of actual problems. Moreover, knowledge acquired from problem-solving practice tends to be inert and fragmented. This study proposed a computer-based cognitive representation approach that externalizes and facilitates the complex processes in learning clinical reasoning. The approach is operationalized in a computer-based cognitive representation tool that involves argument mapping to externalize the problem-solving process and concept mapping to reveal the knowledge constructed from the problems. Twenty-nine Year 3 or higher students from a medical school in east China participated in the study. Participants used the proposed approach implemented in an e-learning system to complete four learning cases in 4 weeks on an individual basis. For each case, students interacted with the problem to capture critical data, generate and justify hypotheses, make a diagnosis, recall relevant knowledge, and update their conceptual understanding of the problem domain. Meanwhile, students used the computer-based cognitive representation tool to articulate and represent the key elements and their interactions in the learning process. A significant improvement was found in students' learning products from the beginning to the end of the study, consistent with students' report of close-to-moderate progress in developing problem-solving and knowledge-construction abilities. No significant differences were found between the pretest and posttest scores with the 4-week period. The cognitive representation approach was found to provide more formative assessment. The computer-based cognitive representation approach improved the learning of clinical reasoning in both problem solving and knowledge construction.

The effect of training methodology on knowledge representation in categorization.

PubMed

Hélie, Sébastien; Shamloo, Farzin; Ell, Shawn W

2017-01-01

Category representations can be broadly classified as containing within-category information or between-category information. Although such representational differences can have a profound impact on decision-making, relatively little is known about the factors contributing to the development and generalizability of different types of category representations. These issues are addressed by investigating the impact of training methodology and category structures using a traditional empirical approach as well as the novel adaptation of computational modeling techniques from the machine learning literature. Experiment 1 focused on rule-based (RB) category structures thought to promote between-category representations. Participants learned two sets of two categories during training and were subsequently tested on a novel categorization problem using the training categories. Classification training resulted in a bias toward between-category representations whereas concept training resulted in a bias toward within-category representations. Experiment 2 focused on information-integration (II) category structures thought to promote within-category representations. With II structures, there was a bias toward within-category representations regardless of training methodology. Furthermore, in both experiments, computational modeling suggests that only within-category representations could support generalization during the test phase. These data suggest that within-category representations may be dominant and more robust for supporting the reconfiguration of current knowledge to support generalization.

The effect of training methodology on knowledge representation in categorization

PubMed Central

Shamloo, Farzin; Ell, Shawn W.

2017-01-01

Category representations can be broadly classified as containing within–category information or between–category information. Although such representational differences can have a profound impact on decision–making, relatively little is known about the factors contributing to the development and generalizability of different types of category representations. These issues are addressed by investigating the impact of training methodology and category structures using a traditional empirical approach as well as the novel adaptation of computational modeling techniques from the machine learning literature. Experiment 1 focused on rule–based (RB) category structures thought to promote between–category representations. Participants learned two sets of two categories during training and were subsequently tested on a novel categorization problem using the training categories. Classification training resulted in a bias toward between–category representations whereas concept training resulted in a bias toward within–category representations. Experiment 2 focused on information-integration (II) category structures thought to promote within–category representations. With II structures, there was a bias toward within–category representations regardless of training methodology. Furthermore, in both experiments, computational modeling suggests that only within–category representations could support generalization during the test phase. These data suggest that within–category representations may be dominant and more robust for supporting the reconfiguration of current knowledge to support generalization. PMID:28846732

29 CFR 2570.34 - Information to be included in every exemption application.

Code of Federal Regulations, 2010 CFR

2010-07-01

... knowledge and belief, the representations made in such statement are true and correct. (c) An application... with the matters discussed in this application and, to the best of my knowledge and belief, the representations made in this application are true and correct. (ii) This declaration must be dated and signed by...

On the Roles of External Knowledge Representations in Assessment Design

ERIC Educational Resources Information Center

Mislevy, Robert J.; Behrens, John T.; Bennett, Randy E.; Demark, Sarah F.; Frezzo, Dennis C.; Levy, Roy; Robinson, Daniel H.; Rutstein, Daisy Wise; Shute, Valerie J.; Stanley, Ken; Winters, Fielding I.

2010-01-01

People use external knowledge representations (KRs) to identify, depict, transform, store, share, and archive information. Learning how to work with KRs is central to be-coming proficient in virtually every discipline. As such, KRs play central roles in curriculum, instruction, and assessment. We describe five key roles of KRs in assessment: (1)…

"BioONT": Improving Knowledge Organization and Representation in the Domain of Biometric Authentication

ERIC Educational Resources Information Center

Buerle, Stephen

2017-01-01

This dissertation explores some of the fundamental challenges facing the information assurance community as it relates to knowledge categorization, organization and representation within the field of information security and more specifically within the domain of biometric authentication. A primary objective of this research is the development of…

The Influence of Textbooks on Teachers' Knowledge of Chemical Bonding Representations Relative to Students' Difficulties Understanding

ERIC Educational Resources Information Center

Bergqvist, Anna; Chang Rundgren, Shu-Nu

2017-01-01

Background: Textbooks are integral tools for teachers' lessons. Several researchers observed that school teachers rely heavily on textbooks as informational sources when planning lessons. Moreover, textbooks are an important resource for developing students' knowledge as they contain various representations that influence students' learning.…

On the Roles of External Knowledge Representations in Assessment Design. CSE Report 722

ERIC Educational Resources Information Center

Mislevy, Robert J.; Behrens, John T.; Bennett, Randy E.; Demark, Sarah F.; Frezzo, Dennis C.; Levy, Roy; Robinson, Daniel H.; Rutstein, Daisy Wise; Shute, Valerie J.; Stanley, Ken; Winters, Fielding I.

2007-01-01

People use external knowledge representations (EKRs) to identify, depict, transform, store, share, and archive information. Learning how to work with EKRs is central to becoming proficient in virtually every discipline. As such, EKRs play central roles in curriculum, instruction, and assessment. Five key roles of EKRs in educational assessment are…

Disciplinary Representation on Institutional Websites: Changing Knowledge, Changing Power?

ERIC Educational Resources Information Center

O'Connor, Kate; Yates, Lyn

2014-01-01

This paper analyses shifts in the representation of history and physics as named organisational units on Australian university websites over the last 15 years in the context of broader questions about the production of knowledge in contemporary times. It derives from a broader project concerned with disciplinarity, changing university contexts and…

Inter-Level Scaffolding and Sequences of Representational Activities in Teaching a Chemical System with Graphical Simulations

ERIC Educational Resources Information Center

Li, Na; Black, John B.

2016-01-01

Chemistry knowledge can be represented at macro-, micro- and symbolic levels, and learning a chemistry topic requires students to engage in multiple representational activities. This study focused on scaffolding for inter-level connection-making in learning chemistry knowledge with graphical simulations. We also tested whether different sequences…

Decision support system for nursing management control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ernst, C.J.

A knowledge representation approach for expert systems supporting decision processes in business is proposed. A description of a knowledge representation schema using a logic programming metalanguage is described, then the role of such a schema in a management expert system is demonstrated through the problem of nursing management control in hospitals. 18 references.

Developing Explanations and Developing Understanding: Students Explain the Phases of the Moon Using Visual Representations

ERIC Educational Resources Information Center

Parnafes, Orit

2012-01-01

This article presents a theoretical model of the process by which students construct and elaborate explanations of scientific phenomena using visual representations. The model describes progress in the underlying conceptual processes in students' explanations as a reorganization of fine-grained knowledge elements based on the Knowledge in Pieces…

Emerging Standards for Medical Logic

PubMed Central

Clayton, Paul D.; Hripcsak, George; Pryor, T. Allan

1990-01-01

Sharing medical logic has traditionally occurred in the form of lectures, conversations, books and journals. As knowledge based computer systems have demonstrated their utility in the health care arena, individuals have pondered the best way to transfer knowledge in a computer based representation (1). A simple representation which allows the knowledge to be shared can be constructed when the knowledge base is modular. Within this representation, units have been named Medical Logic Modules (MLM's) and a syntax has emerged which would allow multiple users to create, criticize, and share those types of medical logic which can be represented in this format. In this paper we talk about why standards exist and why they emerge in some areas and not in others. The appropriateness of using the proposed standards for medical logic modules is then examined against this broader context.

Students' Understanding of External Representations of the Potassium Ion Channel Protein Part II: Structure-Function Relationships and Fragmented Knowledge

ERIC Educational Resources Information Center

Harle, Marissa; Towns, Marcy H.

2012-01-01

Research that has focused on external representations in biochemistry has uncovered student difficulties in comprehending and interpreting external representations. This study focuses on students' understanding of three external representations (ribbon diagram, wireframe, and hydrophobic/hydrophilic) of the potassium ion channel protein. Analysis…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pin, F.G.

Outdoor sensor-based operation of autonomous robots has revealed to be an extremely challenging problem, mainly because of the difficulties encountered when attempting to represent the many uncertainties which are always present in the real world. These uncertainties are primarily due to sensor imprecisions and unpredictability of the environment, i.e., lack of full knowledge of the environment characteristics and dynamics. Two basic principles, or philosophies, and their associated methodologies are proposed in an attempt to remedy some of these difficulties. The first principle is based on the concept of ``minimal model`` for accomplishing given tasks and proposes to utilize only themore » minimum level of information and precision necessary to accomplish elemental functions of complex tasks. This approach diverges completely from the direction taken by most artificial vision studies which conventionally call for crisp and detailed analysis of every available component in the perception data. The paper will first review the basic concepts of this approach and will discuss its pragmatic feasibility when embodied in a behaviorist framework. The second principle which is proposed deals with implicit representation of uncertainties using Fuzzy Set Theory-based approximations and approximate reasoning, rather than explicit (crisp) representation through calculation and conventional propagation techniques. A framework which merges these principles and approaches is presented, and its application to the problem of sensor-based outdoor navigation of a mobile robot is discussed. Results of navigation experiments with a real car in actual outdoor environments are also discussed to illustrate the feasibility of the overall concept.« less

Surveying Medieval Archaeology: a New Form for Harris Paradigm Linking Photogrammetry and Temporal Relations

NASA Astrophysics Data System (ADS)

Drap, P.; Papini, O.; Pruno, E.; Nucciotti, M.; Vannini, G.

2017-02-01

The paper presents some reflexions concerning an interdisciplinary project between Medieval Archaeologists from the University of Florence (Italy) and ICT researchers from CNRS LSIS of Marseille (France), aiming towards a connection between 3D spatial representation and archaeological knowledge. It is well known that Laser Scanner, Photogrammetry and Computer Vision are very attractive tools for archaeologists, although the integration of representation of space and representation of archaeological time has not yet found a methodological standard of reference. We try to develop an integrated system for archaeological 3D survey and all other types of archaeological data and knowledge through integrating observable (material) and non-graphic (interpretive) data. Survey plays a central role, since it is both a metric representation of the archaeological site and, to a wider extent, an interpretation of it (being also a common basis for communication between the 2 teams). More specifically 3D survey is crucial, allowing archaeologists to connect actual spatial assets to the stratigraphic formation processes (i.e. to the archaeological time) and to translate spatial observations into historical interpretation of the site. We propose a common formalism for describing photogrammetrical survey and archaeological knowledge stemming from ontologies: Indeed, ontologies are fully used to model and store 3D data and archaeological knowledge. Xe equip this formalism with a qualitative representation of time. Stratigraphic analyses (both of excavated deposits and of upstanding structures) are closely related to E. C. Harris theory of "Stratigraphic Unit" ("US" from now on). Every US is connected to the others by geometric, topological and, eventually, temporal links, and are recorded by the 3D photogrammetric survey. However, the limitations of the Harris Matrix approach lead to use another representation formalism for stratigraphic relationships, namely Qualitative Constraints Networks (QCN) successfully used in the domain of knowledge representation and reasoning in artificial intelligence for representing temporal relations.

Prediction and Informative Risk Factor Selection of Bone Diseases.

PubMed

Li, Hui; Li, Xiaoyi; Ramanathan, Murali; Zhang, Aidong

2015-01-01

With the booming of healthcare industry and the overwhelming amount of electronic health records (EHRs) shared by healthcare institutions and practitioners, we take advantage of EHR data to develop an effective disease risk management model that not only models the progression of the disease, but also predicts the risk of the disease for early disease control or prevention. Existing models for answering these questions usually fall into two categories: the expert knowledge based model or the handcrafted feature set based model. To fully utilize the whole EHR data, we will build a framework to construct an integrated representation of features from all available risk factors in the EHR data and use these integrated features to effectively predict osteoporosis and bone fractures. We will also develop a framework for informative risk factor selection of bone diseases. A pair of models for two contrast cohorts (e.g., diseased patients versus non-diseased patients) will be established to discriminate their characteristics and find the most informative risk factors. Several empirical results on a real bone disease data set show that the proposed framework can successfully predict bone diseases and select informative risk factors that are beneficial and useful to guide clinical decisions.

Implementation of a frame-based representation in CLIPS

NASA Technical Reports Server (NTRS)

Assal, Hisham; Myers, Leonard

1990-01-01

Knowledge representation is one of the major concerns in expert systems. The representation of domain-specific knowledge should agree with the nature of the domain entities and their use in the real world. For example, architectural applications deal with objects and entities such as spaces, walls, and windows. A natural way of representing these architectural entities is provided by frames. This research explores the potential of using the expert system shell CLIPS, developed by NASA, to implement a frame-based representation that can accommodate architectural knowledge. These frames are similar but quite different from the 'template' construct in version 4.3 of CLIPS. Templates support only the grouping of related information and the assignment of default values to template fields. In addition to these features frames provide other capabilities including definition of classes, inheritance between classes and subclasses, relation of objects of different classes with 'has-a', association of methods (demons) of different types (standard and user-defined) to fields (slots), and creation of new fields at run-time. This frame-based representation is implemented completely in CLIPS. No change to the source code is necessary.

Increasing situation awareness of the CBRNE robot operators

NASA Astrophysics Data System (ADS)

Jasiobedzki, Piotr; Ng, Ho-Kong; Bondy, Michel; McDiarmid, Carl H.

2010-04-01

Situational awareness of CBRN robot operators is quite limited, as they rely on images and measurements from on-board detectors. This paper describes a novel framework that enables a uniform and intuitive access to live and recent data via 2D and 3D representations of visited sites. These representations are created automatically and augmented with images, models and CBRNE measurements. This framework has been developed for CBRNE Crime Scene Modeler (C2SM), a mobile CBRNE mapping system. The system creates representations (2D floor plans and 3D photorealistic models) of the visited sites, which are then automatically augmented with CBRNE detector measurements. The data stored in a database is accessed using a variety of user interfaces providing different perspectives and increasing operators' situational awareness.

A unified framework for managing provenance information in translational research

PubMed Central

2011-01-01

Background A critical aspect of the NIH Translational Research roadmap, which seeks to accelerate the delivery of "bench-side" discoveries to patient's "bedside," is the management of the provenance metadata that keeps track of the origin and history of data resources as they traverse the path from the bench to the bedside and back. A comprehensive provenance framework is essential for researchers to verify the quality of data, reproduce scientific results published in peer-reviewed literature, validate scientific process, and associate trust value with data and results. Traditional approaches to provenance management have focused on only partial sections of the translational research life cycle and they do not incorporate "domain semantics", which is essential to support domain-specific querying and analysis by scientists. Results We identify a common set of challenges in managing provenance information across the pre-publication and post-publication phases of data in the translational research lifecycle. We define the semantic provenance framework (SPF), underpinned by the Provenir upper-level provenance ontology, to address these challenges in the four stages of provenance metadata: (a) Provenance collection - during data generation (b) Provenance representation - to support interoperability, reasoning, and incorporate domain semantics (c) Provenance storage and propagation - to allow efficient storage and seamless propagation of provenance as the data is transferred across applications (d) Provenance query - to support queries with increasing complexity over large data size and also support knowledge discovery applications We apply the SPF to two exemplar translational research projects, namely the Semantic Problem Solving Environment for Trypanosoma cruzi (T.cruzi SPSE) and the Biomedical Knowledge Repository (BKR) project, to demonstrate its effectiveness. Conclusions The SPF provides a unified framework to effectively manage provenance of translational research data during pre and post-publication phases. This framework is underpinned by an upper-level provenance ontology called Provenir that is extended to create domain-specific provenance ontologies to facilitate provenance interoperability, seamless propagation of provenance, automated querying, and analysis. PMID:22126369

The representation of self and person knowledge in the medial prefrontal cortex.

PubMed

Wagner, Dylan D; Haxby, James V; Heatherton, Todd F

2012-07-01

Nearly 40 years ago, social psychologists began applying the information processing framework of cognitive psychology to the question of how humans understand and represent knowledge about themselves and others. This approach gave rise to the immensely successful field of social cognition and fundamentally changed the way in which social psychological phenomena are studied. More recently, social scientists of many stripes have turned to the methods of cognitive neuroscience to understand the neural basis of social cognition. A pervasive finding from this research is that social knowledge, be it about one's self or of others, is represented in the medial prefrontal cortex (MPFC). This review focuses on the social cognitive neuroscience of self and person knowledge in the MPFC. We begin with a brief historical overview of social cognition, followed by a review of recent and influential research on the brain basis of self and person knowledge. In the latter half of this review, we discuss the role of familiarity and similarity in person perception and of spontaneous processes in self and other-referential cognition. Throughout, we discuss the myriad ways in which the social cognitive neuroscience approach has provided new insights into the nature and structure of self and person knowledge. WIREs Cogn Sci 2012, 3:451-470. doi: 10.1002/wcs.1183 This article is categorized under: Neuroscience > Cognition. Copyright © 2012 John Wiley & Sons, Ltd.

Identifying knowledge activism in worker health and safety representation: A cluster analysis.

PubMed

Hall, Alan; Oudyk, John; King, Andrew; Naqvi, Syed; Lewchuk, Wayne

2016-01-01

Although worker representation in OHS has been widely recognized as contributing to health and safety improvements at work, few studies have examined the role that worker representatives play in this process. Using a large quantitative sample, this paper seeks to confirm findings from an earlier exploratory qualitative study that worker representatives can be differentiated by the knowledge intensive tactics and strategies that they use to achieve changes in their workplace. Just under 900 worker health and safety representatives in Ontario completed surveys which asked them to report on the amount of time they devoted to different types of representation activities (i.e., technical activities such as inspections and report writing vs. political activities such as mobilizing workers to build support), the kinds of conditions or hazards they tried to address through their representation (e.g., housekeeping vs. modifications in ventilation systems), and their reported success in making positive improvements. A cluster analysis was used to determine whether the worker representatives could be distinguished in terms of the relative time devoted to different activities and the clusters were then compared with reference to types of intervention efforts and outcomes. The cluster analysis identified three distinct groupings of representatives with significant differences in reported types of interventions and in their level of reported impact. Two of the clusters were consistent with the findings in the exploratory study, identified as knowledge activism for greater emphasis on knowledge based political activity and technical-legal representation for greater emphasis on formalized technical oriented procedures and legal regulations. Knowledge activists were more likely to take on challenging interventions and they reported more impact across the full range of interventions. This paper provides further support for the concepts of knowledge activism and technical-legal representation when differentiating the strategic orientations and impact of worker health and safety representatives, with important implications for education, political support and recruitment. © 2015 Wiley Periodicals, Inc.

Data governance for health care providers.

PubMed

Andronis, Katerina; Moysey, Kevin

2013-01-01

Data governance is characterised from broader definitions of governance. These characteristics are then mapped to a framework that provides a practical representation of the concepts. This representation is further developed with operating models and roles. Several information related scenarios covering both clinical and non-clinical domains are considered in information terms and then related back to the data governance framework. This assists the reader in understanding how data governance would help address the issues or achieve a better outcome. These elements together enable the reader to gain an understanding of the data governance framework and how it applies in practice. Finally, some practical advice is offered for establishing and operating data governance as well as approaches for justifying the investment.

Robust and efficient anomaly detection using heterogeneous representations

NASA Astrophysics Data System (ADS)

Hu, Xing; Hu, Shiqiang; Xie, Jinhua; Zheng, Shiyou

2015-05-01

Various approaches have been proposed for video anomaly detection. Yet these approaches typically suffer from one or more limitations: they often characterize the pattern using its internal information, but ignore its external relationship which is important for local anomaly detection. Moreover, the high-dimensionality and the lack of robustness of pattern representation may lead to problems, including overfitting, increased computational cost and memory requirements, and high false alarm rate. We propose a video anomaly detection framework which relies on a heterogeneous representation to account for both the pattern's internal information and external relationship. The internal information is characterized by slow features learned by slow feature analysis from low-level representations, and the external relationship is characterized by the spatial contextual distances. The heterogeneous representation is compact, robust, efficient, and discriminative for anomaly detection. Moreover, both the pattern's internal information and external relationship can be taken into account in the proposed framework. Extensive experiments demonstrate the robustness and efficiency of our approach by comparison with the state-of-the-art approaches on the widely used benchmark datasets.

Participation without representation? Senior opinion, legislative behavior, and federal health reform.

PubMed

Bradley, Katharine W V; Chen, Jowei

2014-04-01

Why do legislators sometimes engage in behavior that deviates from the expressed policy preferences of constituents who participate in politics at high rates? We examine this puzzle in the context of Democratic legislators' representation of their senior citizen constituents on the Patient Protection and Affordable Care Act of 2010 (ACA). We find that legislators' roll-call votes on the ACA did not reflect the stated preferences of their respective senior constituents; by contrast, these roll-call votes did reflect the preferences of nonsenior adults. We draw upon a theoretical framework developed by Mansbridge to explain this apparent nonresponsiveness to seniors on the ACA. This framework distinguishes between promissory representation, whereby legislators merely respond to constituents' preferences, and anticipatory representation, whereby legislators respond to constituents' underlying policy interests, even when such interests conflict with expressed preferences. By considering the Medicare provisions in the ACA and analyzing Democratic legislators' floor speeches on health reform, we provide preliminary evidence that members of Congress engaged in anticipatory representation of their senior constituents by attempting to educate seniors about how the ACA serves their policy interests.

[Social representations on HIV/AIDS among adolescentes: implications for nursing care].

PubMed

Thiengo, Maria Aparecida; de Oliveira, Denize Cristina; Rodrigues, Benedita Maria Rêgo Deusdará

2005-03-01

With the objective of discussing the implications of the social representations of HIV/AIDS for the interpersonal relations and the practices for protection among adolescents, 15 semidirective interviews were carried out with adolescents, both with and without HIV, assisted at a Hospital School in Rio de Janeiro. The software ALCESTE 4.5 was used for the data analysis. It was observed that the social representation of AIDS is structured around cognitions connected to prevention, revealing a contradiction between the knowledge and the practices reported by the group. It is suggested that the nursing practices should be directed towards the reduction of the distance between practices, representations and scientific knowledge.

Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks.

PubMed

Tran, Son N; d'Avila Garcez, Artur S

2018-02-01

Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whether it can improve learning performance when it is available, and to the extraction of knowledge from trained deep networks, and whether it can offer a better understanding of the representations learned by such networks. To this end, we use a simple symbolic language-a set of logical rules that we call confidence rules-and show that it is suitable for the representation of quantitative reasoning in deep networks. We show by knowledge extraction that confidence rules can offer a low-cost representation for layerwise networks (or restricted Boltzmann machines). We also show that layerwise extraction can produce an improvement in the accuracy of deep belief networks. Furthermore, the proposed symbolic characterization of deep networks provides a novel method for the insertion of prior knowledge and training of deep networks. With the use of this method, a deep neural-symbolic system is proposed and evaluated, with the experimental results indicating that modularity through the use of confidence rules and knowledge insertion can be beneficial to network performance.

Der Aufbau mentaler Modelle durch bildliche Darstellungen: Eine experimentalle Studie uber die Bedeutung der Merkmalsdimensionen Elaboriertheit und Strukturierheit im Sachunterricht der Grundschule (The Development of Mental Processes through Graphic Representation with Diverging Degrees of Elaboration and Structurization: An Experimental Study Carried Out in Elementary Science Instruction in Primary School).

ERIC Educational Resources Information Center

Martschinke, Sabine

1996-01-01

Examines types of graphical representation as to their suitability for knowledge acquisition in primary grades. Uses the concept of mental models to clarify the relationship between external presentation and internal representation of knowledge. Finds that students who learned with highly elaborated and highly structured pictures displayed the…

Context-rich semantic framework for effective data-to-decisions in coalition networks

NASA Astrophysics Data System (ADS)

Grueneberg, Keith; de Mel, Geeth; Braines, Dave; Wang, Xiping; Calo, Seraphin; Pham, Tien

2013-05-01

In a coalition context, data fusion involves combining of soft (e.g., field reports, intelligence reports) and hard (e.g., acoustic, imagery) sensory data such that the resulting output is better than what it would have been if the data are taken individually. However, due to the lack of explicit semantics attached with such data, it is difficult to automatically disseminate and put the right contextual data in the hands of the decision makers. In order to understand the data, explicit meaning needs to be added by means of categorizing and/or classifying the data in relationship to each other from base reference sources. In this paper, we present a semantic framework that provides automated mechanisms to expose real-time raw data effectively by presenting appropriate information needed for a given situation so that an informed decision could be made effectively. The system utilizes controlled natural language capabilities provided by the ITA (International Technology Alliance) Controlled English (CE) toolkit to provide a human-friendly semantic representation of messages so that the messages can be directly processed in human/machine hybrid environments. The Real-time Semantic Enrichment (RTSE) service adds relevant contextual information to raw data streams from domain knowledge bases using declarative rules. The rules define how the added semantics and context information are derived and stored in a semantic knowledge base. The software framework exposes contextual information from a variety of hard and soft data sources in a fast, reliable manner so that an informed decision can be made using semantic queries in intelligent software systems.

Classifying Black Hole States with Machine Learning

NASA Astrophysics Data System (ADS)

Huppenkothen, Daniela

2018-01-01

Galactic black hole binaries are known to go through different states with apparent signatures in both X-ray light curves and spectra, leading to important implications for accretion physics as well as our knowledge of General Relativity. Existing frameworks of classification are usually based on human interpretation of low-dimensional representations of the data, and generally only apply to fairly small data sets. Machine learning, in contrast, allows for rapid classification of large, high-dimensional data sets. In this talk, I will report on advances made in classification of states observed in Black Hole X-ray Binaries, focusing on the two sources GRS 1915+105 and Cygnus X-1, and show both the successes and limitations of using machine learning to derive physical constraints on these systems.

Agent-based models in translational systems biology

PubMed Central

An, Gary; Mi, Qi; Dutta-Moscato, Joyeeta; Vodovotz, Yoram

2013-01-01

Effective translational methodologies for knowledge representation are needed in order to make strides against the constellation of diseases that affect the world today. These diseases are defined by their mechanistic complexity, redundancy, and nonlinearity. Translational systems biology aims to harness the power of computational simulation to streamline drug/device design, simulate clinical trials, and eventually to predict the effects of drugs on individuals. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggests that this modeling framework is well suited for translational systems biology. This review describes agent-based modeling and gives examples of its translational applications in the context of acute inflammation and wound healing. PMID:20835989

A model for diagnosing and explaining multiple disorders.

PubMed

Jamieson, P W

1991-08-01

The ability to diagnose multiple interacting disorders and explain them in a coherent causal framework has only partially been achieved in medical expert systems. This paper proposes a causal model for diagnosing and explaining multiple disorders whose key elements are: physician-directed hypotheses generation, object-oriented knowledge representation, and novel explanation heuristics. The heuristics modify and link the explanations to make the physician aware of diagnostic complexities. A computer program incorporating the model currently is in use for diagnosing peripheral nerve and muscle disorders. The program successfully diagnoses and explains interactions between diseases in terms of underlying pathophysiologic concepts. The model offers a new architecture for medical domains where reasoning from first principles is difficult but explanation of disease interactions is crucial for the system's operation.

A Framework for the Design of Effective Graphics for Scientific Visualization

NASA Technical Reports Server (NTRS)

Miceli, Kristina D.

1992-01-01

This proposal presents a visualization framework, based on a data model, that supports the production of effective graphics for scientific visualization. Visual representations are effective only if they augment comprehension of the increasing amounts of data being generated by modern computer simulations. These representations are created by taking into account the goals and capabilities of the scientist, the type of data to be displayed, and software and hardware considerations. This framework is embodied in an assistant-based visualization system to guide the scientist in the visualization process. This will improve the quality of the visualizations and decrease the time the scientist is required to spend in generating the visualizations. I intend to prove that such a framework will create a more productive environment for tile analysis and interpretation of large, complex data sets.

Structure-Specific Statistical Mapping of White Matter Tracts

PubMed Central

Yushkevich, Paul A.; Zhang, Hui; Simon, Tony; Gee, James C.

2008-01-01

We present a new model-based framework for the statistical analysis of diffusion imaging data associated with specific white matter tracts. The framework takes advantage of the fact that several of the major white matter tracts are thin sheet-like structures that can be effectively modeled by medial representations. The approach involves segmenting major tracts and fitting them with deformable geometric medial models. The medial representation makes it possible to average and combine tensor-based features along directions locally perpendicular to the tracts, thus reducing data dimensionality and accounting for errors in normalization. The framework enables the analysis of individual white matter structures, and provides a range of possibilities for computing statistics and visualizing differences between cohorts. The framework is demonstrated in a study of white matter differences in pediatric chromosome 22q11.2 deletion syndrome. PMID:18407524

New frontiers for intelligent content-based retrieval

NASA Astrophysics Data System (ADS)

Benitez, Ana B.; Smith, John R.

2001-01-01

In this paper, we examine emerging frontiers in the evolution of content-based retrieval systems that rely on an intelligent infrastructure. Here, we refer to intelligence as the capabilities of the systems to build and maintain situational or world models, utilize dynamic knowledge representation, exploit context, and leverage advanced reasoning and learning capabilities. We argue that these elements are essential to producing effective systems for retrieving audio-visual content at semantic levels matching those of human perception and cognition. In this paper, we review relevant research on the understanding of human intelligence and construction of intelligent system in the fields of cognitive psychology, artificial intelligence, semiotics, and computer vision. We also discus how some of the principal ideas form these fields lead to new opportunities and capabilities for content-based retrieval systems. Finally, we describe some of our efforts in these directions. In particular, we present MediaNet, a multimedia knowledge presentation framework, and some MPEG-7 description tools that facilitate and enable intelligent content-based retrieval.

New frontiers for intelligent content-based retrieval

NASA Astrophysics Data System (ADS)

Benitez, Ana B.; Smith, John R.

2000-12-01

In this paper, we examine emerging frontiers in the evolution of content-based retrieval systems that rely on an intelligent infrastructure. Here, we refer to intelligence as the capabilities of the systems to build and maintain situational or world models, utilize dynamic knowledge representation, exploit context, and leverage advanced reasoning and learning capabilities. We argue that these elements are essential to producing effective systems for retrieving audio-visual content at semantic levels matching those of human perception and cognition. In this paper, we review relevant research on the understanding of human intelligence and construction of intelligent system in the fields of cognitive psychology, artificial intelligence, semiotics, and computer vision. We also discus how some of the principal ideas form these fields lead to new opportunities and capabilities for content-based retrieval systems. Finally, we describe some of our efforts in these directions. In particular, we present MediaNet, a multimedia knowledge presentation framework, and some MPEG-7 description tools that facilitate and enable intelligent content-based retrieval.

Volume and Value of Big Healthcare Data.

PubMed

Dinov, Ivo D

Modern scientific inquiries require significant data-driven evidence and trans-disciplinary expertise to extract valuable information and gain actionable knowledge about natural processes. Effective evidence-based decisions require collection, processing and interpretation of vast amounts of complex data. The Moore's and Kryder's laws of exponential increase of computational power and information storage, respectively, dictate the need rapid trans-disciplinary advances, technological innovation and effective mechanisms for managing and interrogating Big Healthcare Data. In this article, we review important aspects of Big Data analytics and discuss important questions like: What are the challenges and opportunities associated with this biomedical, social, and healthcare data avalanche? Are there innovative statistical computing strategies to represent, model, analyze and interpret Big heterogeneous data? We present the foundation of a new compressive big data analytics (CBDA) framework for representation, modeling and inference of large, complex and heterogeneous datasets. Finally, we consider specific directions likely to impact the process of extracting information from Big healthcare data, translating that information to knowledge, and deriving appropriate actions.

Ontology-driven education: Teaching anatomy with intelligent 3D games on the web

NASA Astrophysics Data System (ADS)

Nilsen, Trond

Human anatomy is a challenging and intimidating subject whose understanding is essential to good medical practice, taught primarily using a combination of lectures and the dissection of human cadavers. Lectures are cheap and scalable, but do a poor job of teaching spatial understanding, whereas dissection lets students experience the body's interior first-hand, but is expensive, cannot be repeated, and is often imperfect. Educational games and online learning activities have the potential to supplement these teaching methods in a cheap and relatively effective way, but they are difficult for educators to customize for particular curricula and lack the tutoring support that human instructors provide. I present an approach to the creation of learning activities for anatomy called ontology-driven education, in which the Foundational Model of Anatomy, an ontological representation of knowledge about anatomy, is leveraged to generate educational content, model student knowledge, and support learning activities and games in a configurable web-based educational framework for anatomy.

Volume and Value of Big Healthcare Data

PubMed Central

Dinov, Ivo D.

2016-01-01

Modern scientific inquiries require significant data-driven evidence and trans-disciplinary expertise to extract valuable information and gain actionable knowledge about natural processes. Effective evidence-based decisions require collection, processing and interpretation of vast amounts of complex data. The Moore's and Kryder's laws of exponential increase of computational power and information storage, respectively, dictate the need rapid trans-disciplinary advances, technological innovation and effective mechanisms for managing and interrogating Big Healthcare Data. In this article, we review important aspects of Big Data analytics and discuss important questions like: What are the challenges and opportunities associated with this biomedical, social, and healthcare data avalanche? Are there innovative statistical computing strategies to represent, model, analyze and interpret Big heterogeneous data? We present the foundation of a new compressive big data analytics (CBDA) framework for representation, modeling and inference of large, complex and heterogeneous datasets. Finally, we consider specific directions likely to impact the process of extracting information from Big healthcare data, translating that information to knowledge, and deriving appropriate actions. PMID:26998309

Enhancing students’ mathematical representation and selfefficacy through situation-based learning assisted by geometer’s sketchpad program

NASA Astrophysics Data System (ADS)

Sowanto; Kusumah, Y. S.

2018-05-01

This research was conducted based on the problem of a lack of students’ mathematical representation ability as well as self-efficacy in accomplishing mathematical tasks. To overcome this problem, this research used situation-based learning (SBL) assisted by geometer’s sketchpad program (GSP). This research investigated students’ improvement of mathematical representation ability who were taught under situation-based learning (SBL) assisted by geometer’s sketchpad program (GSP) and regular method that viewed from the whole students’ prior knowledge (high, average, and low level). In addition, this research investigated the difference of students’ self-efficacy after learning was given. This research belongs to quasi experiment research using non-equivalent control group design with purposive sampling. The result of this research showed that students’ enhancement in their mathematical representation ability taught under SBL assisted by GSP was better than the regular method. Also, there was no interaction between learning methods and students prior knowledge in student’ enhancement of mathematical representation ability. There was significant difference of students’ enhancement of mathematical representation ability taught under SBL assisted by GSP viewed from students’ prior knowledge. Furthermore, there was no significant difference in terms of self-efficacy between those who were taught by SBL assisted by GSP with the regular method.

In defense of abstract conceptual representations.

PubMed

Binder, Jeffrey R

2016-08-01

An extensive program of research in the past 2 decades has focused on the role of modal sensory, motor, and affective brain systems in storing and retrieving concept knowledge. This focus has led in some circles to an underestimation of the need for more abstract, supramodal conceptual representations in semantic cognition. Evidence for supramodal processing comes from neuroimaging work documenting a large, well-defined cortical network that responds to meaningful stimuli regardless of modal content. The nodes in this network correspond to high-level "convergence zones" that receive broadly crossmodal input and presumably process crossmodal conjunctions. It is proposed that highly conjunctive representations are needed for several critical functions, including capturing conceptual similarity structure, enabling thematic associative relationships independent of conceptual similarity, and providing efficient "chunking" of concept representations for a range of higher order tasks that require concepts to be configured as situations. These hypothesized functions account for a wide range of neuroimaging results showing modulation of the supramodal convergence zone network by associative strength, lexicality, familiarity, imageability, frequency, and semantic compositionality. The evidence supports a hierarchical model of knowledge representation in which modal systems provide a mechanism for concept acquisition and serve to ground individual concepts in external reality, whereas broadly conjunctive, supramodal representations play an equally important role in concept association and situation knowledge.

COM3/369: Knowledge-based Information Systems: A new approach for the representation and retrieval of medical information

PubMed Central

Mann, G; Birkmann, C; Schmidt, T; Schaeffler, V

1999-01-01

Introduction Present solutions for the representation and retrieval of medical information from online sources are not very satisfying. Either the retrieval process lacks of precision and completeness the representation does not support the update and maintenance of the represented information. Most efforts are currently put into improving the combination of search engines and HTML based documents. However, due to the current shortcomings of methods for natural language understanding there are clear limitations to this approach. Furthermore, this approach does not solve the maintenance problem. At least medical information exceeding a certain complexity seems to afford approaches that rely on structured knowledge representation and corresponding retrieval mechanisms. Methods Knowledge-based information systems are based on the following fundamental ideas. The representation of information is based on ontologies that define the structure of the domain's concepts and their relations. Views on domain models are defined and represented as retrieval schemata. Retrieval schemata can be interpreted as canonical query types focussing on specific aspects of the provided information (e.g. diagnosis or therapy centred views). Based on these retrieval schemata it can be decided which parts of the information in the domain model must be represented explicitly and formalised to support the retrieval process. As representation language propositional logic is used. All other information can be represented in a structured but informal way using text, images etc. Layout schemata are used to assign layout information to retrieved domain concepts. Depending on the target environment HTML or XML can be used. Results Based on this approach two knowledge-based information systems have been developed. The 'Ophthalmologic Knowledge-based Information System for Diabetic Retinopathy' (OKIS-DR) provides information on diagnoses, findings, examinations, guidelines, and reference images related to diabetic retinopathy. OKIS-DR uses combinations of findings to specify the information that must be retrieved. The second system focuses on nutrition related allergies and intolerances. Information on allergies and intolerances of a patient are used to retrieve general information on the specified combination of allergies and intolerances. As a special feature the system generates tables showing food types and products that are tolerated or not tolerated by patients. Evaluation by external experts and user groups showed that the described approach of knowledge-based information systems increases the precision and completeness of knowledge retrieval. Due to the structured and non-redundant representation of information the maintenance and update of the information can be simplified. Both systems are available as WWW based online knowledge bases and CD-ROMs (cf. http://mta.gsf.de topic: products).

Object segmentation and recovery via neural oscillators implementing the similarity and prior knowledge gestalt rules.

PubMed

Ursino, Mauro; Magosso, Elisa; La Cara, Giuseppe-Emiliano; Cuppini, Cristiano

2006-09-01

Object recognition requires the solution of the binding and segmentation problems, i.e., grouping different features to achieve a coherent representation. Synchronization of neural activity in the gamma-band, associated with gestalt perception, has often been proposed as a putative mechanism to solve these problems, not only as to low-level processing, but also in higher cortical functions. In the present work, a network of Wilson-Cowan oscillators is used to segment simultaneous objects, and recover an object from partial or corrupted information, by implementing two gestalt rules: similarity and prior knowledge. The network consists of H different areas, each devoted to representation of a particular feature of the object, according to a topological organization. The similarity law is realized via lateral intra-area connections, arranged as a "Mexican-hat". Prior knowledge is realized via inter-area connections, which link properties belonging to a previously memorized object. A global inhibitor allows segmentation of several objects avoiding interference. Simulation results, performed using three simultaneous input objects, show that the network is able to detect an object even in difficult conditions (i.e., when some features are absent or shifted with respect to the original one). Moreover, the trade-off between sensitivity (capacity to detect true positives) and specificity (capacity to reject false positives) can be controlled acting on the extension of lateral synapses (i.e., on the level of accepted similarity). Finally, the network can also deal with correlated objects, i.e., objects which have some common features. Simulations performed using a different number of objects (2, 3, 4 or 5) suggest that the network is able to segment and recall up to four objects, but the oscillation frequency must increase, the lower the number of objects simultaneously present. The model, although quite simpler compared with neurophysiology, may represent a theoretical framework for the analysis of the relationships between object representation, memory, learning, and gamma-band activity. In particular, it extends previous studies on autoassociative memory since it exploits not only oscillatory dynamics, but also a topological organization of features.

Identification of threats using linguistics-based knowledge extraction.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chew, Peter A.

One of the challenges increasingly facing intelligence analysts, along with professionals in many other fields, is the vast amount of data which needs to be reviewed and converted into meaningful information, and ultimately into rational, wise decisions by policy makers. The advent of the world wide web (WWW) has magnified this challenge. A key hypothesis which has guided us is that threats come from ideas (or ideology), and ideas are almost always put into writing before the threats materialize. While in the past the 'writing' might have taken the form of pamphlets or books, today's medium of choice is themore » WWW, precisely because it is a decentralized, flexible, and low-cost method of reaching a wide audience. However, a factor which complicates matters for the analyst is that material published on the WWW may be in any of a large number of languages. In 'Identification of Threats Using Linguistics-Based Knowledge Extraction', we have sought to use Latent Semantic Analysis (LSA) and other similar text analysis techniques to map documents from the WWW, in whatever language they were originally written, to a common language-independent vector-based representation. This then opens up a number of possibilities. First, similar documents can be found across language boundaries. Secondly, a set of documents in multiple languages can be visualized in a graphical representation. These alone offer potentially useful tools and capabilities to the intelligence analyst whose knowledge of foreign languages may be limited. Finally, we can test the over-arching hypothesis--that ideology, and more specifically ideology which represents a threat, can be detected solely from the words which express the ideology--by using the vector-based representation of documents to predict additional features (such as the ideology) within a framework based on supervised learning. In this report, we present the results of a three-year project of the same name. We believe these results clearly demonstrate the general feasibility of an approach such as that outlined above. Nevertheless, there are obstacles which must still be overcome, relating primarily to how 'ideology' should be defined. We discuss these and point to possible solutions.« less

Dialect Variation and Phonological Knowledge: Phonological Representations and Metalinguistic Awareness among Beginning Readers who Speak Nonmainstream American English

ERIC Educational Resources Information Center

Terry, Nicole Patton

2014-01-01

Children's spoken nonmainstream American English (NMAE) dialect use and their knowledge about phonological representations of word pronunciations were assessed in a sample of 105 children in kindergarten through second grade. Children were given expressive and receptive tasks with dialect-sensitive stimuli. Students who produced many NMAE…

Meta-Representation in an Algebra I Classroom

ERIC Educational Resources Information Center

Izsak, Andrew; Caglayan, Gunhan; Olive, John

2009-01-01

We describe how 1 Algebra I teacher and her 8th-grade students used meta-representational knowledge when generating and evaluating equations to solve word problems. Analyzing data from a sequence of 4 lessons, we found that the teacher and her students used criteria for evaluating equations, in addition to other types of knowledge (e.g., different…

Exploring the Progression in Preservice Chemistry Teachers' Pedagogical Content Knowledge Representations: The Case of "Behavior of Gases"

ERIC Educational Resources Information Center

Adadan, Emine; Oner, Diler

2014-01-01

This multiple case study investigated how two preservice chemistry teachers' pedagogical content knowledge (PCK) representations of behavior of gases progressed in the context of a semester-long chemistry teaching methods course. The change in the participants' PCK components was interpreted with respect to the theoretical PCK learning…

Relative Expertise in an Everyday Reasoning Task: Epistemic Understanding, Problem Representation, and Reasoning Competence

ERIC Educational Resources Information Center

Weinstock, Michael

2009-01-01

Experts in cognitive domains differ from non-experts in how they represent problems and knowledge, and in their epistemic understandings of tasks in their domain of expertise. This study investigates whether task-specific epistemic understanding also underlies the representation of knowledge on an everyday reasoning task on which the competent…

An Investigation of Pattern Problems Posed by Middle School Mathematics Preservice Teachers Using Multiple Representation

ERIC Educational Resources Information Center

Yilmaz, Yasemin; Durmus, Soner; Yaman, Hakan

2018-01-01

This study investigated the pattern problems posed by middle school mathematics preservice teachers using multiple representations to determine both their pattern knowledge levels and their abilities to transfer this knowledge to students. The design of the study is the survey method, one of the quantitative research methods. The study group was…

Representations of the Nature of Scientific Knowledge in Turkish Biology Textbooks

ERIC Educational Resources Information Center

Irez, Serhat

2016-01-01

Considering the impact of textbooks on learning, this study set out to assess representations of the nature of scientific knowledge in Turkish 9th grade biology textbooks. To this end, the ten most commonly used 9th grade biology textbooks were analyzed. A qualitative research approach was utilized and the textbooks were analyzed using…

Advantages of Thesaurus Representation Using the Simple Knowledge Organization System (SKOS) Compared with Proposed Alternatives

ERIC Educational Resources Information Center

Pastor-Sanchez, Juan-Antonio; Martinez Mendez, Francisco Javier; Rodriguez-Munoz, Jose Vicente

2009-01-01

Introduction: This paper presents an analysis of the Simple Knowledge Organization System (SKOS) compared with other alternatives for thesaurus representation in the Semantic Web. Method: Based on functional and structural changes of thesauri, provides an overview of the current context in which lexical paradigm is abandoned in favour of the…

Design by Analogy: Achieving More Patentable Ideas from One Creative Design

NASA Astrophysics Data System (ADS)

Jia, Li-Zhen; Wu, Chun-Long; Zhu, Xue-Hong; Tan, Run-Hua

2018-12-01

A patent is a kind of technical document to protect intellectual property for individuals or enterprises. Patentable idea generation is a crucial step for patent application and analogy is confirmed to be an effective technique to inspire creative ideas. Analogy-based design usually starts from representation of an analogy source and is followed by the retrieval of appropriate analogs, mapping of design knowledge and adaptation of target solution. To diffuse one core idea into other new contexts and achieve more patentable ideas, this paper mainly centered on the first two stages of analogy-based design and proposed a patentable ideation framework. The analogical information of the source system, including source design problems and solution, was mined comprehensively through International Patent Classification analysis and represented in the form of function, behavior and structure. Three heuristics were suggested for searching the set of candidate target systems with a similar design problem, where the source design could be transferred. To systematize the process of source representation, analogs retrieval, idea transfer, and solution generation, an ideation model was put forward. Finally, the bladeless fan was selected as a source design to illustrate the application of this work. The design output shows that the representation and heuristics are beneficial, and this systematic ideation method can help the engineer or designer enhance creativity and discover more patentable opportunities.

24 CFR 4001.116 - Representations and prohibitions.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 5 2012-04-01 2012-04-01 false Representations and prohibitions... Eligibility Requirements and Underwriting Procedures § 4001.116 Representations and prohibitions. (a... actual knowledge furnished material information known to be false for the purpose of obtaining the...

24 CFR 4001.116 - Representations and prohibitions.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 5 2010-04-01 2010-04-01 false Representations and prohibitions... Eligibility Requirements and Underwriting Procedures § 4001.116 Representations and prohibitions. (a... actual knowledge furnished material information known to be false for the purpose of obtaining the...

24 CFR 4001.116 - Representations and prohibitions.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 5 2011-04-01 2011-04-01 false Representations and prohibitions... Eligibility Requirements and Underwriting Procedures § 4001.116 Representations and prohibitions. (a... actual knowledge furnished material information known to be false for the purpose of obtaining the...

Predictive representations can link model-based reinforcement learning to model-free mechanisms.

PubMed

Russek, Evan M; Momennejad, Ida; Botvinick, Matthew M; Gershman, Samuel J; Daw, Nathaniel D

2017-09-01

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation.

Predictive representations can link model-based reinforcement learning to model-free mechanisms

PubMed Central

Botvinick, Matthew M.

2017-01-01

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation. PMID:28945743

Hierarchical Representation Learning for Kinship Verification.

PubMed

Kohli, Naman; Vatsa, Mayank; Singh, Richa; Noore, Afzel; Majumdar, Angshul

2017-01-01

Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this paper, first, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate kinship-cues. The visual stimuli presented to the participants determine their ability to recognize kin relationship using the whole face as well as specific facial regions. The effect of participant gender and age and kin-relation pair of the stimulus is analyzed using quantitative measures such as accuracy, discriminability index d' , and perceptual information entropy. Utilizing the information obtained from the human study, a hierarchical kinship verification via representation learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner. We propose a novel approach for feature representation termed as filtered contractive deep belief networks (fcDBN). The proposed feature representation encodes relational information present in images using filters and contractive regularization penalty. A compact representation of facial images of kin is extracted as an output from the learned model and a multi-layer neural network is utilized to verify the kin accurately. A new WVU kinship database is created, which consists of multiple images per subject to facilitate kinship verification. The results show that the proposed deep learning framework (KVRL-fcDBN) yields the state-of-the-art kinship verification accuracy on the WVU kinship database and on four existing benchmark data sets. Furthermore, kinship information is used as a soft biometric modality to boost the performance of face verification via product of likelihood ratio and support vector machine based approaches. Using the proposed KVRL-fcDBN framework, an improvement of over 20% is observed in the performance of face verification.

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hasan, S. M. Shamimul; Fox, Edward A.; Bisset, Keith

Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. Asmore » a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK—a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks—aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed—the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. As a result, we show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.« less

Erognomic education on housework for women with upper limb repetitive strain injury (RSI): a conceptual representation of therapists' clinical reasoning.

PubMed

Cheung, Therma W C; Clemson, Lindy; O' Loughlin, Kate; Shuttleworth, Russell

2017-09-18

Ergonomic education in housework that aims to facilitate behavior change is important for women with upper limb repetitive strain injury. Therapists usually conduct such programs based on implicit reasoning. Making this reasoning explicit is important in contributing to the profession's knowledge. To construct a conceptual representation of how occupational therapists make clinical decisions for such program. Based on a constructivist-grounded theory methodology, data were collected through in-depth interviewing with 14 occupational therapists from a major hospital in Singapore. Interviews were audiotaped and transcribed. Data was analyzed with line by line, focused and axial coding with constant data comparison throughout data collection. Therapists made clinical decisions based on their perceptions of their clients' behavior change in three stages: (i) listen; (ii) try; and (iii) persevere, bearing significant similarities to the transtheoretical theory of change. The study also showed that therapists may not have considered the full range of meanings that their clients attach to housework when interacting with them, a gap that needs to be addressed. The present study indicates the importance of therapists' understanding of the meanings that their clients attach to housework. Further research needs to address how to achieve this in a time-pressured clinical environment. Implications for Rehabilitation This study used qualitative research to demonstrate the process of translating therapists' tacit knowledge into an explicit form. It elucidates the following major implications for practice when therapists conduct ergonomic education to facilitate behavior change in housework for female homemakers with upper limb RSI:The conceptual framework of clinical reasoning constructed from the results can be used to increase therapists' awareness of how they make clinical decisions during an intervention. This framework can also be used for training new therapists. It is important for therapists to actively listen to their clients. Active listening will enable the therapists to understand and consider the personal meanings that these women attach to housework in order to facilitate a behavior change. Client-therapist interactions to facilitate clients' willingness to change should become a major focus in such a program. Similar research should be conducted in other clinical areas to develop explicit clinical reasoning frameworks to facilitate learning of novice therapists and reflection of experienced therapists to address any gap in their clinical reasoning.

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

DOE PAGES

Hasan, S. M. Shamimul; Fox, Edward A.; Bisset, Keith; ...

2017-11-06

Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. Asmore » a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK—a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks—aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed—the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. As a result, we show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.« less

Getting Mental Models and Computer Models to Cooperate

NASA Technical Reports Server (NTRS)

Sheridan, T. B.; Roseborough, J.; Charney, L.; Mendel, M.

1984-01-01

A qualitative theory of supervisory control is outlined wherein the mental models of one or more human operators are related to the knowledge representations within automatic controllers (observers, estimators) and operator decision aids (expert systems, advice-givers). Methods of quantifying knowledge and the calibration of one knowledge representation to another (human, computer, or objective truth) are discussed. Ongoing experiments in the use of decision aids for exploring one's own objective function or exploring system constraints and control strategies are described.

Towards Ontology as Knowledge Representation for Intellectual Capital Measurement

NASA Astrophysics Data System (ADS)

Zadjabbari, B.; Wongthongtham, P.; Dillon, T. S.

For many years, physical asset indicators were the main evidence of an organization’s successful performance. However, the situation has changed after information technology revolution in the knowledge-based economy. Since 1980’s business performance has not been limited only to physical assets instead intellectual capital are increasingly playing a major role in business performance. In this paper, we utilize ontology as a tool for knowledge representation in the domain of intellectual capital measurement. The ontology classifies ways of intangible capital measurement.

Mainstream web standards now support science data too

NASA Astrophysics Data System (ADS)

Richard, S. M.; Cox, S. J. D.; Janowicz, K.; Fox, P. A.

2017-12-01

The science community has developed many models and ontologies for representation of scientific data and knowledge. In some cases these have been built as part of coordinated frameworks. For example, the biomedical communities OBO Foundry federates applications covering various aspects of life sciences, which are united through reference to a common foundational ontology (BFO). The SWEET ontology, originally developed at NASA and now governed through ESIP, is a single large unified ontology for earth and environmental sciences. On a smaller scale, GeoSciML provides a UML and corresponding XML representation of geological mapping and observation data. Some of the key concepts related to scientific data and observations have recently been incorporated into domain-neutral mainstream ontologies developed by the World Wide Web consortium through their Spatial Data on the Web working group (SDWWG). OWL-Time has been enhanced to support temporal reference systems needed for science, and has been deployed in a linked data representation of the International Chronostratigraphic Chart. The Semantic Sensor Network ontology has been extended to cover samples and sampling, including relationships between samples. Gridded data and time-series is supported by applications of the statistical data-cube ontology (QB) for earth observations (the EO-QB profile) and spatio-temporal data (QB4ST). These standard ontologies and encodings can be used directly for science data, or can provide a bridge to specialized domain ontologies. There are a number of advantages in alignment with the W3C standards. The W3C vocabularies use discipline-neutral language and thus support cross-disciplinary applications directly without complex mappings. The W3C vocabularies are already aligned with the core ontologies that are the building blocks of the semantic web. The W3C vocabularies are each tightly scoped thus encouraging good practices in the combination of complementary small ontologies. The W3C vocabularies are hosted on well known, reliable infrastructure. The W3C SDWWG outputs are being selectively adopted by the general schema.org discovery framework.

Temporal abstraction and temporal Bayesian networks in clinical domains: a survey.

PubMed

Orphanou, Kalia; Stassopoulou, Athena; Keravnou, Elpida

2014-03-01

Temporal abstraction (TA) of clinical data aims to abstract and interpret clinical data into meaningful higher-level interval concepts. Abstracted concepts are used for diagnostic, prediction and therapy planning purposes. On the other hand, temporal Bayesian networks (TBNs) are temporal extensions of the known probabilistic graphical models, Bayesian networks. TBNs can represent temporal relationships between events and their state changes, or the evolution of a process, through time. This paper offers a survey on techniques/methods from these two areas that were used independently in many clinical domains (e.g. diabetes, hepatitis, cancer) for various clinical tasks (e.g. diagnosis, prognosis). A main objective of this survey, in addition to presenting the key aspects of TA and TBNs, is to point out important benefits from a potential integration of TA and TBNs in medical domains and tasks. The motivation for integrating these two areas is their complementary function: TA provides clinicians with high level views of data while TBNs serve as a knowledge representation and reasoning tool under uncertainty, which is inherent in all clinical tasks. Key publications from these two areas of relevance to clinical systems, mainly circumscribed to the latest two decades, are reviewed and classified. TA techniques are compared on the basis of: (a) knowledge acquisition and representation for deriving TA concepts and (b) methodology for deriving basic and complex temporal abstractions. TBNs are compared on the basis of: (a) representation of time, (b) knowledge representation and acquisition, (c) inference methods and the computational demands of the network, and (d) their applications in medicine. The survey performs an extensive comparative analysis to illustrate the separate merits and limitations of various TA and TBN techniques used in clinical systems with the purpose of anticipating potential gains through an integration of the two techniques, thus leading to a unified methodology for clinical systems. The surveyed contributions are evaluated using frameworks of respective key features. In addition, for the evaluation of TBN methods, a unifying clinical domain (diabetes) is used. The main conclusion transpiring from this review is that techniques/methods from these two areas, that so far are being largely used independently of each other in clinical domains, could be effectively integrated in the context of medical decision-support systems. The anticipated key benefits of the perceived integration are: (a) during problem solving, the reasoning can be directed at different levels of temporal and/or conceptual abstractions since the nodes of the TBNs can be complex entities, temporally and structurally and (b) during model building, knowledge generated in the form of basic and/or complex abstractions, can be deployed in a TBN. Copyright © 2014 Elsevier B.V. All rights reserved.

Knowledge-Based Object Detection in Laser Scanning Point Clouds

NASA Astrophysics Data System (ADS)

Boochs, F.; Karmacharya, A.; Marbs, A.

2012-07-01

Object identification and object processing in 3D point clouds have always posed challenges in terms of effectiveness and efficiency. In practice, this process is highly dependent on human interpretation of the scene represented by the point cloud data, as well as the set of modeling tools available for use. Such modeling algorithms are data-driven and concentrate on specific features of the objects, being accessible to numerical models. We present an approach that brings the human expert knowledge about the scene, the objects inside, and their representation by the data and the behavior of algorithms to the machine. This "understanding" enables the machine to assist human interpretation of the scene inside the point cloud. Furthermore, it allows the machine to understand possibilities and limitations of algorithms and to take this into account within the processing chain. This not only assists the researchers in defining optimal processing steps, but also provides suggestions when certain changes or new details emerge from the point cloud. Our approach benefits from the advancement in knowledge technologies within the Semantic Web framework. This advancement has provided a strong base for applications based on knowledge management. In the article we will present and describe the knowledge technologies used for our approach such as Web Ontology Language (OWL), used for formulating the knowledge base and the Semantic Web Rule Language (SWRL) with 3D processing and topologic built-ins, aiming to combine geometrical analysis of 3D point clouds, and specialists' knowledge of the scene and algorithmic processing.

Computational neuroanatomy: ontology-based representation of neural components and connectivity

PubMed Central

Rubin, Daniel L; Talos, Ion-Florin; Halle, Michael; Musen, Mark A; Kikinis, Ron

2009-01-01

Background A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning. Results We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems. We built a prototype model of the motor system that integrates descriptive anatomic and qualitative functional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, our approach provides an explicit representation of abnormal neural connectivity in disease states, such as common movement disorders. The ontology-based representation encodes both structural and functional aspects of neuroanatomy. The ontology-based models can be evaluated computationally, enabling development of automated computer reasoning applications. Conclusion Neuroanatomical knowledge can be represented in machine-accessible format using ontologies. Computational neuroanatomical approaches such as described in this work could become a key tool in translational informatics, leading to decision support applications that inform and guide surgical planning and personalized care for neurological disease in the future. PMID:19208191

Representations and Rafts

ERIC Educational Resources Information Center

Hartweg, Kimberly Sipes

2011-01-01

To build on prior knowledge and mathematical understanding, middle school students need to be given the opportunity to make connections among a variety of representations. Graphs, tables, algebraic formulas, and models are just a few examples of representations that can help students explore quantitative relationships. As a mathematics educator,…

Incorporating climate-system and carbon-cycle uncertainties in integrated assessments of climate change. (Invited)

NASA Astrophysics Data System (ADS)

Rogelj, J.; McCollum, D. L.; Reisinger, A.; Knutti, R.; Riahi, K.; Meinshausen, M.

2013-12-01

The field of integrated assessment draws from a large body of knowledge across a range of disciplines to gain robust insights about possible interactions, trade-offs, and synergies. Integrated assessment of climate change, for example, uses knowledge from the fields of energy system science, economics, geophysics, demography, climate change impacts, and many others. Each of these fields comes with its associated caveats and uncertainties, which should be taken into account when assessing any results. The geophysical system and its associated uncertainties are often represented by models of reduced complexity in integrated assessment modelling frameworks. Such models include simple representations of the carbon-cycle and climate system, and are often based on the global energy balance equation. A prominent example of such model is the 'Model for the Assessment of Greenhouse Gas Induced Climate Change', MAGICC. Here we show how a model like MAGICC can be used for the representation of geophysical uncertainties. Its strengths, weaknesses, and limitations are discussed and illustrated by means of an analysis which attempts to integrate socio-economic and geophysical uncertainties. These uncertainties in the geophysical response of the Earth system to greenhouse gases remains key for estimating the cost of greenhouse gas emission mitigation scenarios. We look at uncertainties in four dimensions: geophysical, technological, social and political. Our results indicate that while geophysical uncertainties are an important factor influencing projections of mitigation costs, political choices that delay mitigation by one or two decades a much more pronounced effect.

Astronomical Data Integration Beyond the Virtual Observatory

NASA Astrophysics Data System (ADS)

Lemson, G.; Laurino, O.

2015-09-01

"Data integration" generally refers to the process of combining data from different source data bases into a unified view. Much work has been devoted in this area by the International Virtual Observatory Alliance (IVOA), allowing users to discover and access databases through standard protocols. However, different archives present their data through their own schemas and users must still select, filter, and combine data for each archive individually. An important reason for this is that the creation of common data models that satisfy all sub-disciplines is fraught with difficulties. Furthermore it requires a substantial amount of work for data providers to present their data according to some standard representation. We will argue that existing standards allow us to build a data integration framework that works around these problems. The particular framework requires the implementation of the IVOA Table Access Protocol (TAP) only. It uses the newly developed VO data modelling language (VO-DML) specification, which allows one to define extensible object-oriented data models using a subset of UML concepts through a simple XML serialization language. A rich mapping language allows one to describe how instances of VO-DML data models are represented by the TAP service, bridging the possible mismatch between a local archive's schema and some agreed-upon representation of the astronomical domain. In this so called local-as-view approach to data integration, “mediators" use the mapping prescriptions to translate queries phrased in terms of the common schema to the underlying TAP service. This mapping language has a graphical representation, which we expose through a web based graphical “drag-and-drop-and-connect" interface. This service allows any user to map the holdings of any TAP service to the data model(s) of choice. The mappings are defined and stored outside of the data sources themselves, which allows the interface to be used in a kind of crowd-sourcing effort to annotate any remote database of interest. This reduces the burden of publishing one's data and allows a great flexibility in the definition of the views through which particular communities might wish to access remote archives. At the same time, the framework easies the user's effort to select, filter, and combine data from many different archives, so as to build knowledge bases for their analysis. We will present the framework and demonstrate a prototype implementation. We will discuss ideas for producing the missing elements, in particular the query language and the implementation of mediator tools to translate object queries to ADQL

Is the Classroom Obsolete in the Twenty-First Century?

ERIC Educational Resources Information Center

Benade, Leon

2017-01-01

Lefebvre's triadic conception of "spatial practice, representations of space and representational spaces" provides the theoretical framework of this article, which recognises a productive relationship between space and social relations. Its writing stems from a current and ongoing qualitative study of innovative teaching and learning…

Marginal Shape Deep Learning: Applications to Pediatric Lung Field Segmentation.

PubMed

Mansoor, Awais; Cerrolaza, Juan J; Perez, Geovanny; Biggs, Elijah; Nino, Gustavo; Linguraru, Marius George

2017-02-11

Representation learning through deep learning (DL) architecture has shown tremendous potential for identification, localization, and texture classification in various medical imaging modalities. However, DL applications to segmentation of objects especially to deformable objects are rather limited and mostly restricted to pixel classification. In this work, we propose marginal shape deep learning (MaShDL), a framework that extends the application of DL to deformable shape segmentation by using deep classifiers to estimate the shape parameters. MaShDL combines the strength of statistical shape models with the automated feature learning architecture of DL. Unlike the iterative shape parameters estimation approach of classical shape models that often leads to a local minima, the proposed framework is robust to local minima optimization and illumination changes. Furthermore, since the direct application of DL framework to a multi-parameter estimation problem results in a very high complexity, our framework provides an excellent run-time performance solution by independently learning shape parameter classifiers in marginal eigenspaces in the decreasing order of variation. We evaluated MaShDL for segmenting the lung field from 314 normal and abnormal pediatric chest radiographs and obtained a mean Dice similarity coefficient of 0.927 using only the four highest modes of variation (compared to 0.888 with classical ASM 1 (p-value=0.01) using same configuration). To the best of our knowledge this is the first demonstration of using DL framework for parametrized shape learning for the delineation of deformable objects.

Marginal shape deep learning: applications to pediatric lung field segmentation

NASA Astrophysics Data System (ADS)

Mansoor, Awais; Cerrolaza, Juan J.; Perez, Geovany; Biggs, Elijah; Nino, Gustavo; Linguraru, Marius George

2017-02-01

Representation learning through deep learning (DL) architecture has shown tremendous potential for identification, local- ization, and texture classification in various medical imaging modalities. However, DL applications to segmentation of objects especially to deformable objects are rather limited and mostly restricted to pixel classification. In this work, we propose marginal shape deep learning (MaShDL), a framework that extends the application of DL to deformable shape segmentation by using deep classifiers to estimate the shape parameters. MaShDL combines the strength of statistical shape models with the automated feature learning architecture of DL. Unlike the iterative shape parameters estimation approach of classical shape models that often leads to a local minima, the proposed framework is robust to local minima optimization and illumination changes. Furthermore, since the direct application of DL framework to a multi-parameter estimation problem results in a very high complexity, our framework provides an excellent run-time performance solution by independently learning shape parameter classifiers in marginal eigenspaces in the decreasing order of variation. We evaluated MaShDL for segmenting the lung field from 314 normal and abnormal pediatric chest radiographs and obtained a mean Dice similarity coefficient of 0:927 using only the four highest modes of variation (compared to 0:888 with classical ASM1 (p-value=0:01) using same configuration). To the best of our knowledge this is the first demonstration of using DL framework for parametrized shape learning for the delineation of deformable objects.

Marginal Shape Deep Learning: Applications to Pediatric Lung Field Segmentation

PubMed Central

Mansoor, Awais; Cerrolaza, Juan J.; Perez, Geovanny; Biggs, Elijah; Nino, Gustavo; Linguraru, Marius George

2017-01-01

Representation learning through deep learning (DL) architecture has shown tremendous potential for identification, localization, and texture classification in various medical imaging modalities. However, DL applications to segmentation of objects especially to deformable objects are rather limited and mostly restricted to pixel classification. In this work, we propose marginal shape deep learning (MaShDL), a framework that extends the application of DL to deformable shape segmentation by using deep classifiers to estimate the shape parameters. MaShDL combines the strength of statistical shape models with the automated feature learning architecture of DL. Unlike the iterative shape parameters estimation approach of classical shape models that often leads to a local minima, the proposed framework is robust to local minima optimization and illumination changes. Furthermore, since the direct application of DL framework to a multi-parameter estimation problem results in a very high complexity, our framework provides an excellent run-time performance solution by independently learning shape parameter classifiers in marginal eigenspaces in the decreasing order of variation. We evaluated MaShDL for segmenting the lung field from 314 normal and abnormal pediatric chest radiographs and obtained a mean Dice similarity coefficient of 0.927 using only the four highest modes of variation (compared to 0.888 with classical ASM1 (p-value=0.01) using same configuration). To the best of our knowledge this is the first demonstration of using DL framework for parametrized shape learning for the delineation of deformable objects. PMID:28592911

The Role of Functionality in the Mental Representations of Engineering Students: Some Differences in the Early Stages of Expertise

ERIC Educational Resources Information Center

Moss, Jarrod; Kotovsky, Kenneth; Cagan, Jonathan

2006-01-01

As engineers gain experience and become experts in their domain, the structure and content of their knowledge changes. Two studies are presented that examine differences in knowledge representation among freshman and senior engineering students. The first study examines recall of mechanical devices and chunking of components, and the second…

Promoting Pedagogical Content Knowledge Development for Early Career Secondary Teachers in Science and Technology Using Content Representations

ERIC Educational Resources Information Center

Williams, John; Eames, Chris; Hume, Anne; Lockley, John

2012-01-01

Background: This research addressed the key area of early career teacher education and aimed to explore the use of a "content representation" (CoRe) as a mediational tool to develop early career secondary teacher pedagogical content knowledge (PCK). This study was situated in the subject areas of science and technology, where sound…

Invisible Brain: Knowledge in Research Works and Neuron Activity.

PubMed

Segev, Aviv; Curtis, Dorothy; Jung, Sukhwan; Chae, Suhyun

2016-01-01

If the market has an invisible hand, does knowledge creation and representation have an "invisible brain"? While knowledge is viewed as a product of neuron activity in the brain, can we identify knowledge that is outside the brain but reflects the activity of neurons in the brain? This work suggests that the patterns of neuron activity in the brain can be seen in the representation of knowledge-related activity. Here we show that the neuron activity mechanism seems to represent much of the knowledge learned in the past decades based on published articles, in what can be viewed as an "invisible brain" or collective hidden neural networks. Similar results appear when analyzing knowledge activity in patents. Our work also tries to characterize knowledge increase as neuron network activity growth. The results propose that knowledge-related activity can be seen outside of the neuron activity mechanism. Consequently, knowledge might exist as an independent mechanism.

Invisible Brain: Knowledge in Research Works and Neuron Activity

PubMed Central

Segev, Aviv; Curtis, Dorothy; Jung, Sukhwan; Chae, Suhyun

2016-01-01

If the market has an invisible hand, does knowledge creation and representation have an “invisible brain”? While knowledge is viewed as a product of neuron activity in the brain, can we identify knowledge that is outside the brain but reflects the activity of neurons in the brain? This work suggests that the patterns of neuron activity in the brain can be seen in the representation of knowledge-related activity. Here we show that the neuron activity mechanism seems to represent much of the knowledge learned in the past decades based on published articles, in what can be viewed as an “invisible brain” or collective hidden neural networks. Similar results appear when analyzing knowledge activity in patents. Our work also tries to characterize knowledge increase as neuron network activity growth. The results propose that knowledge-related activity can be seen outside of the neuron activity mechanism. Consequently, knowledge might exist as an independent mechanism. PMID:27439199

Overcoming the Subject-Object Dichotomy in Urban Modeling: Axial Maps as Geometric Representations of Affordances in the Built Environment.

PubMed

Marcus, Lars

2018-01-01

The world is witnessing unprecedented urbanization, bringing extreme challenges to contemporary practices in urban planning and design. This calls for improved urban models that can generate new knowledge and enhance practical skill. Importantly, any urban model embodies a conception of the relation between humans and the physical environment. In urban modeling this is typically conceived of as a relation between human subjects and an environmental object, thereby reproducing a humans-environment dichotomy. Alternative modeling traditions, such as space syntax that originates in architecture rather than geography, have tried to overcome this dichotomy. Central in this effort is the development of new representations of urban space, such as in the case of space syntax, the axial map. This form of representation aims to integrate both human behavior and the physical environment into one and the same description. Interestingly, models based on these representations have proved to better capture pedestrian movement than regular models. Pedestrian movement, as well as other kinds of human flows in urban space, is essential for urban modeling, since increasingly flows of this kind are understood as the driver in urban processes. Critical for a full understanding of space syntax modeling is the ontology of its' representations, such as the axial map. Space syntax theory here often refers to James Gibson's "Theory of affordances," where the concept of affordances, in a manner similar to axial maps, aims to bridge the subject-object dichotomy by neither constituting physical properties of the environment or human behavior, but rather what emerges in the meeting between the two. In extension of this, the axial map can be interpreted as a representation of how the physical form of the environment affords human accessibility and visibility in urban space. This paper presents a close examination of the form of representations developed in space syntax methodology, in particular in the light of Gibson's "theory of affordances." The overarching aim is to contribute to a theoretical framework for urban models based on affordances, which may support the overcoming of the subject-object dichotomy in such models, here deemed essential for a greater social-ecological sustainability of cities.

The IHMC CmapTools software in research and education: a multi-level use case in Space Meteorology

NASA Astrophysics Data System (ADS)

Messerotti, Mauro

2010-05-01

The IHMC (Institute for Human and Machine Cognition, Florida University System, USA) CmapTools software is a powerful multi-platform tool for knowledge modelling in graphical form based on concept maps. In this work we present its application for the high-level development of a set of multi-level concept maps in the framework of Space Meteorology to act as the kernel of a space meteorology domain ontology. This is an example of a research use case, as a domain ontology coded in machine-readable form via e.g. OWL (Web Ontology Language) is suitable to be an active layer of any knowledge management system embedded in a Virtual Observatory (VO). Apart from being manageable at machine level, concept maps developed via CmapTools are intrinsically human-readable and can embed hyperlinks and objects of many kinds. Therefore they are suitable to be published on the web: the coded knowledge can be exploited for educational purposes by the students and the public, as the level of information can be naturally organized among linked concept maps in progressively increasing complexity levels. Hence CmapTools and its advanced version COE (Concept-map Ontology Editor) represent effective and user-friendly software tools for high-level knowledge represention in research and education.

Improvement of sand filter and constructed wetland design using an environmental decision support system.

PubMed

Turon, Clàudia; Comas, Joaquim; Torrens, Antonina; Molle, Pascal; Poch, Manel

2008-01-01

With the aim of improving effluent quality of waste stabilization ponds, different designs of vertical flow constructed wetlands and intermittent sand filters were tested on an experimental full-scale plant within the framework of a European project. The information extracted from this study was completed and updated with heuristic and bibliographic knowledge. The data and knowledge acquired were difficult to integrate into mathematical models because they involve qualitative information and expert reasoning. Therefore, it was decided to develop an environmental decision support system (EDSS-Filter-Design) as a tool to integrate mathematical models and knowledge-based techniques. This paper describes the development of this support tool, emphasizing the collection of data and knowledge and representation of this information by means of mathematical equations and a rule-based system. The developed support tool provides the main design characteristics of filters: (i) required surface, (ii) media type, and (iii) media depth. These design recommendations are based on wastewater characteristics, applied load, and required treatment level data provided by the user. The results of the EDSS-Filter-Design provide appropriate and useful information and guidelines on how to design filters, according to the expert criteria. The encapsulation of the information into a decision support system reduces the design period and provides a feasible, reasoned, and positively evaluated proposal.

Multiple Domains of Parental Secure Base Support During Childhood and Adolescence Contribute to Adolescents’ Representations of Attachment as a Secure Base Script

PubMed Central

Vaughn, Brian E.; Waters, Theodore E. A.; Steele, Ryan D.; Roisman, Glenn I.; Bost, Kelly K.; Truitt, Warren; Waters, Harriet S.; Booth-LaForce, Cathryn

2016-01-01

Although attachment theory claims that early attachment representations reflecting the quality of the child’s “lived experiences” are maintained across developmental transitions, evidence that has emerged over the last decade suggests that the association between early relationship quality and adolescents’ attachment representations is fairly modest in magnitude. We used aspects of parenting beyond sensitivity over childhood and adolescence and early security to predict adolescents’ scripted attachment representations. At age 18 years, 673 participants from the NICHD Study of Early Child Care and Youth Development (SECCYD) completed the Attachment Script Assessment (ASA) from which we derived an assessment of secure base script knowledge. Measures of secure base support from childhood through age 15 years (e.g., parental monitoring of child activity, father presence in the home) were selected as predictors and accounted for an additional 8% of the variance in secure base script knowledge scores above and beyond direct observations of sensitivity and early attachment status alone, suggesting that adolescents’ scripted attachment representations reflect multiple domains of parenting. Cognitive and demographic variables also significantly increased predicted variance in secure base script knowledge by 2% each. PMID:27032953

The NeuARt II system: a viewing tool for neuroanatomical data based on published neuroanatomical atlases

PubMed Central

Burns, Gully APC; Cheng, Wei-Cheng; Thompson, Richard H; Swanson, Larry W

2006-01-01

Background Anatomical studies of neural circuitry describing the basic wiring diagram of the brain produce intrinsically spatial, highly complex data of great value to the neuroscience community. Published neuroanatomical atlases provide a spatial framework for these studies. We have built an informatics framework based on these atlases for the representation of neuroanatomical knowledge. This framework not only captures current methods of anatomical data acquisition and analysis, it allows these studies to be collated, compared and synthesized within a single system. Results We have developed an atlas-viewing application ('NeuARt II') in the Java language with unique functional properties. These include the ability to use copyrighted atlases as templates within which users may view, save and retrieve data-maps and annotate them with volumetric delineations. NeuARt II also permits users to view multiple levels on multiple atlases at once. Each data-map in this system is simply a stack of vector images with one image per atlas level, so any set of accurate drawings made onto a supported atlas (in vector graphics format) could be uploaded into NeuARt II. Presently the database is populated with a corpus of high-quality neuroanatomical data from the laboratory of Dr Larry Swanson (consisting 64 highly-detailed maps of PHAL tract-tracing experiments, made up of 1039 separate drawings that were published in 27 primary research publications over 17 years). Herein we take selective examples from these data to demonstrate the features of NeuArt II. Our informatics tool permits users to browse, query and compare these maps. The NeuARt II tool operates within a bioinformatics knowledge management platform (called 'NeuroScholar') either as a standalone or a plug-in application. Conclusion Anatomical localization is fundamental to neuroscientific work and atlases provide an easily-understood framework that is widely used by neuroanatomists and non-neuroanatomists alike. NeuARt II, the neuroinformatics tool presented here, provides an accurate and powerful way of representing neuroanatomical data in the context of commonly-used brain atlases for visualization, comparison and analysis. Furthermore, it provides a framework that supports the delivery and manipulation of mapped data either as a standalone system or as a component in a larger knowledge management system. PMID:17166289

Dissociations in mathematical knowledge: case studies in Down's syndrome and Williams syndrome.

PubMed

Robinson, Sally J; Temple, Christine M

2013-02-01

A study is reported of mathematical vocabulary and factual mathematical knowledge in PQ, a 22 year old with Down's syndrome (DS) who has a verbal mental age (MA) of 9 years 2 months and ST, a 15 year old with Williams syndrome (WS) who has a verbal MA of 9 years 6 months, matched to typically developing controls. The number of mathematical words contained within PQ's lexical stores was significantly reduced as reflected by performance on lexical decision. PQ was also impaired at both naming from descriptions and describing mathematical words. These results contrast with normal lexical decision and item descriptions for concrete words reported recently for PQ (Robinson and Temple, 2010). PQ's recall of mathematical facts was also impaired, whilst his recall of general knowledge facts was normal. This performance in DS indicates a deficit in both lexical representation and semantic knowledge for mathematical words and mathematical facts. In contrast, ST, the teenager with WS had good accuracy on lexical decision, naming and generating definitions for mathematical words. This contrasted with the atypical performance with concrete words recently reported for ST (Robinson and Temple, 2009). Knowledge of addition facts and general knowledge facts was also unimpaired for ST, though knowledge of multiplication facts was weak. Together the cases form a double dissociation and provide support for the distinct representation of mathematical and concrete items within the lexical-semantic system during development. The dissociations between mathematical and general factual knowledge also indicate that different types of factual knowledge may be selectively impaired during development. There is further support for a modular structure within which mathematical vocabulary and mathematical knowledge have distinct representations. This supports the case for the independent representation of factual and language-based knowledge within the semantic system during development. Copyright © 2011 Elsevier Ltd. All rights reserved.

Priority setting: what constitutes success? A conceptual framework for successful priority setting.

PubMed

Sibbald, Shannon L; Singer, Peter A; Upshur, Ross; Martin, Douglas K

2009-03-05

The sustainability of healthcare systems worldwide is threatened by a growing demand for services and expensive innovative technologies. Decision makers struggle in this environment to set priorities appropriately, particularly because they lack consensus about which values should guide their decisions. One way to approach this problem is to determine what all relevant stakeholders understand successful priority setting to mean. The goal of this research was to develop a conceptual framework for successful priority setting. Three separate empirical studies were completed using qualitative data collection methods (one-on-one interviews with healthcare decision makers from across Canada; focus groups with representation of patients, caregivers and policy makers; and Delphi study including scholars and decision makers from five countries). This paper synthesizes the findings from three studies into a framework of ten separate but interconnected elements germane to successful priority setting: stakeholder understanding, shifted priorities/reallocation of resources, decision making quality, stakeholder acceptance and satisfaction, positive externalities, stakeholder engagement, use of explicit process, information management, consideration of values and context, and revision or appeals mechanism. The ten elements specify both quantitative and qualitative dimensions of priority setting and relate to both process and outcome components. To our knowledge, this is the first framework that describes successful priority setting. The ten elements identified in this research provide guidance for decision makers and a common language to discuss priority setting success and work toward improving priority setting efforts.