Going beyond the lesson: Self-generating new factual knowledge in the classroom

PubMed Central

Esposito, Alena G.; Bauer, Patricia J.

2016-01-01

For children to build a knowledge base, they must integrate and extend knowledge acquired across separate episodes of new learning. Children’s performance was assessed in a task requiring them to self-generate new factual knowledge from the integration of novel facts presented through separate lessons in the classroom. Whether self-generation performance predicted academic outcomes in reading comprehension and mathematics was also examined. The 278 participating children were in grades K-3 (mean age 7.7 years; range 5.5–10.3 years). Children self-generated new factual knowledge through integration in the classroom; age-related increases were observed. Self-generation performance predicted both reading comprehension and mathematics academic outcomes, even when controlling for caregiver education. PMID:27728784

Advanced software development workstation. Knowledge base design: Design of knowledge base for flight planning application

NASA Technical Reports Server (NTRS)

Izygon, Michel E.

1992-01-01

The development process of the knowledge base for the generation of Test Libraries for Mission Operations Computer (MOC) Command Support focused on a series of information gathering interviews. These knowledge capture sessions are supporting the development of a prototype for evaluating the capabilities of INTUIT on such an application. the prototype includes functions related to POCC (Payload Operation Control Center) processing. It prompts the end-users for input through a series of panels and then generates the Meds associated with the initialization and the update of hazardous command tables for a POCC Processing TLIB.

Ontology-Based Multiple Choice Question Generation

PubMed Central

Al-Yahya, Maha

2014-01-01

With recent advancements in Semantic Web technologies, a new trend in MCQ item generation has emerged through the use of ontologies. Ontologies are knowledge representation structures that formally describe entities in a domain and their relationships, thus enabling automated inference and reasoning. Ontology-based MCQ item generation is still in its infancy, but substantial research efforts are being made in the field. However, the applicability of these models for use in an educational setting has not been thoroughly evaluated. In this paper, we present an experimental evaluation of an ontology-based MCQ item generation system known as OntoQue. The evaluation was conducted using two different domain ontologies. The findings of this study show that ontology-based MCQ generation systems produce satisfactory MCQ items to a certain extent. However, the evaluation also revealed a number of shortcomings with current ontology-based MCQ item generation systems with regard to the educational significance of an automatically constructed MCQ item, the knowledge level it addresses, and its language structure. Furthermore, for the task to be successful in producing high-quality MCQ items for learning assessments, this study suggests a novel, holistic view that incorporates learning content, learning objectives, lexical knowledge, and scenarios into a single cohesive framework. PMID:24982937

Health Knowledge Among the Millennial Generation

PubMed Central

Lloyd, Tom; Shaffer, Michele L.; Christy, Stetter; Widome, Mark D.; Repke, John; Weitekamp, Michael R.; Eslinger, Paul J.; Bargainnier, Sandra S.; Paul, Ian M.

2013-01-01

The Millennial Generation, also known as Generation Y, is the demographic cohort following Generation X, and is generally regarded to be composed of those individuals born between 1980 and 2000. They are the first to grow up in an environment where health-related information is widely available by internet, TV and other electronic media, yet we know very little about the scope of their health knowledge. This study was undertaken to quantify two domains of clinically relevant health knowledge: factual content and ability to solve health related questions (application) in nine clinically related medical areas. Study subjects correctly answered, on average, 75% of health application questions but only 54% of health content questions. Since students were better able to correctly answer questions dealing with applications compared to those on factual content contemporary US high school students may not use traditional hierarchical learning models in acquisition of their health knowledge. PMID:25170479

Health knowledge among the millennial generation.

PubMed

Lloyd, Tom; Shaffer, Michele L; Christy, Stetter; Widome, Mark D; Repke, John; Weitekamp, Michael R; Eslinger, Paul J; Bargainnier, Sandra S; Paul, Ian M

2013-04-28

The Millennial Generation, also known as Generation Y, is the demographic cohort following Generation X, and is generally regarded to be composed of those individuals born between 1980 and 2000. They are the first to grow up in an environment where health-related information is widely available by internet, TV and other electronic media, yet we know very little about the scope of their health knowledge. This study was undertaken to quantify two domains of clinically relevant health knowledge: factual content and ability to solve health related questions (application) in nine clinically related medical areas. Study subjects correctly answered, on average, 75% of health application questions but only 54% of health content questions. Since students were better able to correctly answer questions dealing with applications compared to those on factual content contemporary US high school students may not use traditional hierarchical learning models in acquisition of their health knowledge.

Knowledge-Based Entrepreneurship in a Boundless Research System

ERIC Educational Resources Information Center

Dell'Anno, Davide

2008-01-01

International entrepreneurship and knowledge-based entrepreneurship have recently generated considerable academic and non-academic attention. This paper explores the "new" field of knowledge-based entrepreneurship in a boundless research system. Cultural barriers to the development of business opportunities by researchers persist in some academic…

Sociopathic Knowledge Bases: Correct Knowledge Can Be Harmful Even Given Unlimited Computation

DTIC Science & Technology

1989-08-01

pobitive, as false positives generated by a medical program can often be caught by a physician upon further testing . False negatives, however, may be...improvement over the knowledge base tested is obtained. Although our work is pretty much theoretical research oriented one example of ex- periments is...knowledge base, improves the performance by about 10%. of tests . First, we divide the cases into a training set and a validation set with 70% vs. 30% each

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.

Geospatial Standards and the Knowledge Generation Lifescycle

NASA Technical Reports Server (NTRS)

Khalsa, Siri Jodha S.; Ramachandran, Rahul

2014-01-01

Standards play an essential role at each stage in the sequence of processes by which knowledge is generated from geoscience observations, simulations and analysis. This paper provides an introduction to the field of informatics and the knowledge generation lifecycle in the context of the geosciences. In addition we discuss how the newly formed Earth Science Informatics Technical Committee is helping to advance the application of standards and best practices to make data and data systems more usable and interoperable.

Ontology-based configuration of problem-solving methods and generation of knowledge-acquisition tools: application of PROTEGE-II to protocol-based decision support.

PubMed

Tu, S W; Eriksson, H; Gennari, J H; Shahar, Y; Musen, M A

1995-06-01

PROTEGE-II is a suite of tools and a methodology for building knowledge-based systems and domain-specific knowledge-acquisition tools. In this paper, we show how PROTEGE-II can be applied to the task of providing protocol-based decision support in the domain of treating HIV-infected patients. To apply PROTEGE-II, (1) we construct a decomposable problem-solving method called episodic skeletal-plan refinement, (2) we build an application ontology that consists of the terms and relations in the domain, and of method-specific distinctions not already captured in the domain terms, and (3) we specify mapping relations that link terms from the application ontology to the domain-independent terms used in the problem-solving method. From the application ontology, we automatically generate a domain-specific knowledge-acquisition tool that is custom-tailored for the application. The knowledge-acquisition tool is used for the creation and maintenance of domain knowledge used by the problem-solving method. The general goal of the PROTEGE-II approach is to produce systems and components that are reusable and easily maintained. This is the rationale for constructing ontologies and problem-solving methods that can be composed from a set of smaller-grained methods and mechanisms. This is also why we tightly couple the knowledge-acquisition tools to the application ontology that specifies the domain terms used in the problem-solving systems. Although our evaluation is still preliminary, for the application task of providing protocol-based decision support, we show that these goals of reusability and easy maintenance can be achieved. We discuss design decisions and the tradeoffs that have to be made in the development of the system.

Generative and Item-Specific Knowledge of Language

ERIC Educational Resources Information Center

Morgan, Emily Ida Popper

2016-01-01

The ability to generate novel utterances compositionally using generative knowledge is a hallmark property of human language. At the same time, languages contain non-compositional or idiosyncratic items, such as irregular verbs, idioms, etc. This dissertation asks how and why language achieves a balance between these two systems--generative and…

A Natural Language Interface Concordant with a Knowledge Base.

PubMed

Han, Yong-Jin; Park, Seong-Bae; Park, Se-Young

2016-01-01

The discordance between expressions interpretable by a natural language interface (NLI) system and those answerable by a knowledge base is a critical problem in the field of NLIs. In order to solve this discordance problem, this paper proposes a method to translate natural language questions into formal queries that can be generated from a graph-based knowledge base. The proposed method considers a subgraph of a knowledge base as a formal query. Thus, all formal queries corresponding to a concept or a predicate in the knowledge base can be generated prior to query time and all possible natural language expressions corresponding to each formal query can also be collected in advance. A natural language expression has a one-to-one mapping with a formal query. Hence, a natural language question is translated into a formal query by matching the question with the most appropriate natural language expression. If the confidence of this matching is not sufficiently high the proposed method rejects the question and does not answer it. Multipredicate queries are processed by regarding them as a set of collected expressions. The experimental results show that the proposed method thoroughly handles answerable questions from the knowledge base and rejects unanswerable ones effectively.

Applying Knowledge to Generate Action: A Community-Based Knowledge Translation Framework

ERIC Educational Resources Information Center

Campbell, Barbara

2010-01-01

Introduction: Practical strategies are needed to translate research knowledge between researchers and users into action. For effective translation to occur, researchers and users should partner during the research process, recognizing the impact that knowledge, when translated into practice, will have on those most affected by that research.…

Comparison of clinical knowledge bases for summarization of electronic health records.

PubMed

McCoy, Allison B; Sittig, Dean F; Wright, Adam

2013-01-01

Automated summarization tools that create condition-specific displays may improve clinician efficiency. These tools require new kinds of knowledge that is difficult to obtain. We compared five problem-medication pair knowledge bases generated using four previously described knowledge base development approaches. The number of pairs in the resulting mapped knowledge bases varied widely due to differing mapping techniques from the source terminologies, ranging from 2,873 to 63,977,738 pairs. The number of overlapping pairs across knowledge bases was low, with one knowledge base having half of the pairs overlapping with another knowledge base, and most having less than a third overlapping. Further research is necessary to better evaluate the knowledge bases independently in additional settings, and to identify methods to integrate the knowledge bases.

Semantics-based plausible reasoning to extend the knowledge coverage of medical knowledge bases for improved clinical decision support.

PubMed

Mohammadhassanzadeh, Hossein; Van Woensel, William; Abidi, Samina Raza; Abidi, Syed Sibte Raza

2017-01-01

Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians' experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning , which generalizes the commonalities among the data to induce new rules, and analogical reasoning , which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries. We evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15

Extending Cross-Generational Knowledge Flow Research in Edge Organizations

DTIC Science & Technology

2008-06-01

letting Protégé generate the basic user interface, and then gradually write widgets and plug-ins to customize its look-and- feel and behavior . 4 3.0...2007a) focused on cross-generational knowledge flows in edge organizations. We found that cross- generational biases affect tacit knowledge transfer...the software engineering field, many matured methodologies already exist, such as Rational Unified Process (Hunt, 2003) or Extreme Programming (Beck

Factors Influencing the Creation of a Wiki Culture for Knowledge Management in a Cross-Generational Organizational Setting

ERIC Educational Resources Information Center

Macro, Kenneth L., Jr.

2011-01-01

Initiatives within organizations that promote sharing of knowledge may be hampered by generational differences. Research on relationships between generations and technology-based knowledge sharing campaigns provides little managerial guidance for practitioners. The purpose of this ethnographic study was to identify the factors that influence the…

When generating answers benefits arithmetic skill: the importance of prior knowledge.

PubMed

Rittle-Johnson, Bethany; Kmicikewycz, Alexander Oleksij

2008-09-01

People remember information better if they generate the information while studying rather than read the information. However, prior research has not investigated whether this generation effect extends to related but unstudied items and has not been conducted in classroom settings. We compared third graders' success on studied and unstudied multiplication problems after they spent a class period generating answers to problems or reading the answers from a calculator. The effect of condition interacted with prior knowledge. Students with low prior knowledge had higher accuracy in the generate condition, but as prior knowledge increased, the advantage of generating answers decreased. The benefits of generating answers may extend to unstudied items and to classroom settings, but only for learners with low prior knowledge.

Route Generation for a Synthetic Character (BOT) Using a Partial or Incomplete Knowledge Route Generation Algorithm in UT2004 Virtual Environment

NASA Technical Reports Server (NTRS)

Hanold, Gregg T.; Hanold, David T.

2010-01-01

This paper presents a new Route Generation Algorithm that accurately and realistically represents human route planning and navigation for Military Operations in Urban Terrain (MOUT). The accuracy of this algorithm in representing human behavior is measured using the Unreal Tournament(Trademark) 2004 (UT2004) Game Engine to provide the simulation environment in which the differences between the routes taken by the human player and those of a Synthetic Agent (BOT) executing the A-star algorithm and the new Route Generation Algorithm can be compared. The new Route Generation Algorithm computes the BOT route based on partial or incomplete knowledge received from the UT2004 game engine during game play. To allow BOT navigation to occur continuously throughout the game play with incomplete knowledge of the terrain, a spatial network model of the UT2004 MOUT terrain is captured and stored in an Oracle 11 9 Spatial Data Object (SOO). The SOO allows a partial data query to be executed to generate continuous route updates based on the terrain knowledge, and stored dynamic BOT, Player and environmental parameters returned by the query. The partial data query permits the dynamic adjustment of the planned routes by the Route Generation Algorithm based on the current state of the environment during a simulation. The dynamic nature of this algorithm more accurately allows the BOT to mimic the routes taken by the human executing under the same conditions thereby improving the realism of the BOT in a MOUT simulation environment.

Need to Knowledge (NtK) Model: an evidence-based framework for generating technological innovations with socio-economic impacts.

PubMed

Flagg, Jennifer L; Lane, Joseph P; Lockett, Michelle M

2013-02-15

Traditional government policies suggest that upstream investment in scientific research is necessary and sufficient to generate technological innovations. The expected downstream beneficial socio-economic impacts are presumed to occur through non-government market mechanisms. However, there is little quantitative evidence for such a direct and formulaic relationship between public investment at the input end and marketplace benefits at the impact end. Instead, the literature demonstrates that the technological innovation process involves a complex interaction between multiple sectors, methods, and stakeholders. The authors theorize that accomplishing the full process of technological innovation in a deliberate and systematic manner requires an operational-level model encompassing three underlying methods, each designed to generate knowledge outputs in different states: scientific research generates conceptual discoveries; engineering development generates prototype inventions; and industrial production generates commercial innovations. Given the critical roles of engineering and business, the entire innovation process should continuously consider the practical requirements and constraints of the commercial marketplace.The Need to Knowledge (NtK) Model encompasses the activities required to successfully generate innovations, along with associated strategies for effectively communicating knowledge outputs in all three states to the various stakeholders involved. It is intentionally grounded in evidence drawn from academic analysis to facilitate objective and quantitative scrutiny, and industry best practices to enable practical application. The Need to Knowledge (NtK) Model offers a practical, market-oriented approach that avoids the gaps, constraints and inefficiencies inherent in undirected activities and disconnected sectors. The NtK Model is a means to realizing increased returns on public investments in those science and technology programs expressly intended to

Need to Knowledge (NtK) Model: an evidence-based framework for generating technological innovations with socio-economic impacts

PubMed Central

2013-01-01

Background Traditional government policies suggest that upstream investment in scientific research is necessary and sufficient to generate technological innovations. The expected downstream beneficial socio-economic impacts are presumed to occur through non-government market mechanisms. However, there is little quantitative evidence for such a direct and formulaic relationship between public investment at the input end and marketplace benefits at the impact end. Instead, the literature demonstrates that the technological innovation process involves a complex interaction between multiple sectors, methods, and stakeholders. Discussion The authors theorize that accomplishing the full process of technological innovation in a deliberate and systematic manner requires an operational-level model encompassing three underlying methods, each designed to generate knowledge outputs in different states: scientific research generates conceptual discoveries; engineering development generates prototype inventions; and industrial production generates commercial innovations. Given the critical roles of engineering and business, the entire innovation process should continuously consider the practical requirements and constraints of the commercial marketplace. The Need to Knowledge (NtK) Model encompasses the activities required to successfully generate innovations, along with associated strategies for effectively communicating knowledge outputs in all three states to the various stakeholders involved. It is intentionally grounded in evidence drawn from academic analysis to facilitate objective and quantitative scrutiny, and industry best practices to enable practical application. Summary The Need to Knowledge (NtK) Model offers a practical, market-oriented approach that avoids the gaps, constraints and inefficiencies inherent in undirected activities and disconnected sectors. The NtK Model is a means to realizing increased returns on public investments in those science and technology

Automated Knowledge Generation with Persistent Surveillance Video

DTIC Science & Technology

2008-03-26

5 2.1 Artificial Intelligence . . . . . . . . . . . . . . . . . . . . 5 2.1.1 Formal Logic . . . . . . . . . . . . . . . . . . . 6 2.1.2...background of Artificial Intelligence and the reasoning engines that will be applied to generate knowledge from data. Section 2.2 discusses background on...generation from persistent video. 4 II. Background In this chapter, we will discuss the background of Artificial Intelligence, Semantic Web, image

Ontologies, Knowledge Bases and Knowledge Management

DTIC Science & Technology

2002-07-01

AFRL-IF-RS-TR-2002-163 Final Technical Report July 2002 ONTOLOGIES, KNOWLEDGE BASES AND KNOWLEDGE MANAGEMENT USC Information ...and layer additional information necessary to make specific uses of the knowledge in this core. Finally, while we were able to find adequate solutions... knowledge base and inference engine. Figure 3.2: SDA Editor Interface 46 Although the SDA has access to information about the situation, we wanted the user

New approach to generating insights for aging research based on literature mining and knowledge integration

PubMed Central

Kwon, Yeondae; Natori, Yukikazu

2017-01-01

The proportion of the elderly population in most countries worldwide is increasing dramatically. Therefore, social interest in the fields of health, longevity, and anti-aging has been increasing as well. However, the basic research results obtained from a reductionist approach in biology and a bioinformatic approach in genome science have limited usefulness for generating insights on future health, longevity, and anti-aging-related research on a case by case basis. We propose a new approach that uses our literature mining technique and bioinformatics, which lead to a better perspective on research trends by providing an expanded knowledge base to work from. We demonstrate that our approach provides useful information that deepens insights on future trends which differs from data obtained conventionally, and this methodology is already paving the way for a new field in aging-related research based on literature mining. One compelling example of this is how our new approach can be a useful tool in drug repositioning. PMID:28817730

A Knowledge-Base for a Personalized Infectious Disease Risk Prediction System.

PubMed

Vinarti, Retno; Hederman, Lucy

2018-01-01

We present a knowledge-base to represent collated infectious disease risk (IDR) knowledge. The knowledge is about personal and contextual risk of contracting an infectious disease obtained from declarative sources (e.g. Atlas of Human Infectious Diseases). Automated prediction requires encoding this knowledge in a form that can produce risk probabilities (e.g. Bayesian Network - BN). The knowledge-base presented in this paper feeds an algorithm that can auto-generate the BN. The knowledge from 234 infectious diseases was compiled. From this compilation, we designed an ontology and five rule types for modelling IDR knowledge in general. The evaluation aims to assess whether the knowledge-base structure, and its application to three disease-country contexts, meets the needs of personalized IDR prediction system. From the evaluation results, the knowledge-base conforms to the system's purpose: personalization of infectious disease risk.

Critical Analysis of Textbooks: Knowledge-Generating Logics and the Emerging Image of "Global Economic Contexts"

ERIC Educational Resources Information Center

Thoma, Michael

2017-01-01

This paper presents an approach to the critical analysis of textbook knowledge, which, working from a discourse theory perspective (based on the work of Foucault), refers to the performative nature of language. The critical potential of the approach derives from an analysis of knowledge-generating logics, which produce particular images of reality…

Knowledge-based IMRT treatment planning for prostate cancer.

PubMed

Chanyavanich, Vorakarn; Das, Shiva K; Lee, William R; Lo, Joseph Y

2011-05-01

To demonstrate the feasibility of using a knowledge base of prior treatment plans to generate new prostate intensity modulated radiation therapy (IMRT) plans. Each new case would be matched against others in the knowledge base. Once the best match is identified, that clinically approved plan is used to generate the new plan. A database of 100 prostate IMRT treatment plans was assembled into an information-theoretic system. An algorithm based on mutual information was implemented to identify similar patient cases by matching 2D beam's eye view projections of contours. Ten randomly selected query cases were each matched with the most similar case from the database of prior clinically approved plans. Treatment parameters from the matched case were used to develop new treatment plans. A comparison of the differences in the dose-volume histograms between the new and the original treatment plans were analyzed. On average, the new knowledge-based plan is capable of achieving very comparable planning target volume coverage as the original plan, to within 2% as evaluated for D98, D95, and D1. Similarly, the dose to the rectum and dose to the bladder are also comparable to the original plan. For the rectum, the mean and standard deviation of the dose percentage differences for D20, D30, and D50 are 1.8% +/- 8.5%, -2.5% +/- 13.9%, and -13.9% +/- 23.6%, respectively. For the bladder, the mean and standard deviation of the dose percentage differences for D20, D30, and D50 are -5.9% +/- 10.8%, -12.2% +/- 14.6%, and -24.9% +/- 21.2%, respectively. A negative percentage difference indicates that the new plan has greater dose sparing as compared to the original plan. The authors demonstrate a knowledge-based approach of using prior clinically approved treatment plans to generate clinically acceptable treatment plans of high quality. This semiautomated approach has the potential to improve the efficiency of the treatment planning process while ensuring that high quality plans are

Semantic computing and language knowledge bases

NASA Astrophysics Data System (ADS)

Wang, Lei; Wang, Houfeng; Yu, Shiwen

2017-09-01

As the proposition of the next-generation Web - semantic Web, semantic computing has been drawing more and more attention within the circle and the industries. A lot of research has been conducted on the theory and methodology of the subject, and potential applications have also been investigated and proposed in many fields. The progress of semantic computing made so far cannot be detached from its supporting pivot - language resources, for instance, language knowledge bases. This paper proposes three perspectives of semantic computing from a macro view and describes the current status of affairs about the construction of language knowledge bases and the related research and applications that have been carried out on the basis of these resources via a case study in the Institute of Computational Linguistics at Peking University.

Drug knowledge bases and their applications in biomedical informatics research.

PubMed

Zhu, Yongjun; Elemento, Olivier; Pathak, Jyotishman; Wang, Fei

2018-01-03

Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many drug knowledge bases have been constructed. They range from simple ones with specific focuses to comprehensive ones that contain information on almost every aspect of a drug. These curated drug knowledge bases have made significant contributions to the development of efficient and effective health information technologies for better health-care service delivery. Understanding and comparing existing drug knowledge bases and how they are applied in various biomedical studies will help us recognize the state of the art and design better knowledge bases in the future. In addition, researchers can get insights on novel applications of the drug knowledge bases through a review of successful use cases. In this study, we provide a review of existing popular drug knowledge bases and their applications in drug-related studies. We discuss challenges in constructing and using drug knowledge bases as well as future research directions toward a better ecosystem of drug knowledge bases. © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The Knowledge Building Paradigm: A Model of Learning for Net Generation Students

ERIC Educational Resources Information Center

Philip, Donald

2005-01-01

In this article Donald Philip describes Knowledge Building, a pedagogy based on the way research organizations function. The global economy, Philip argues, is driving a shift from older, industrial models to the model of the business as a learning organization. The cognitive patterns of today's Net Generation students, formed by lifetime exposure…

Reducing a Knowledge-Base Search Space When Data Are Missing

NASA Technical Reports Server (NTRS)

James, Mark

2007-01-01

This software addresses the problem of how to efficiently execute a knowledge base in the presence of missing data. Computationally, this is an exponentially expensive operation that without heuristics generates a search space of 1 + 2n possible scenarios, where n is the number of rules in the knowledge base. Even for a knowledge base of the most modest size, say 16 rules, it would produce 65,537 possible scenarios. The purpose of this software is to reduce the complexity of this operation to a more manageable size. The problem that this system solves is to develop an automated approach that can reason in the presence of missing data. This is a meta-reasoning capability that repeatedly calls a diagnostic engine/model to provide prognoses and prognosis tracking. In the big picture, the scenario generator takes as its input the current state of a system, including probabilistic information from Data Forecasting. Using model-based reasoning techniques, it returns an ordered list of fault scenarios that could be generated from the current state, i.e., the plausible future failure modes of the system as it presently stands. The scenario generator models a Potential Fault Scenario (PFS) as a black box, the input of which is a set of states tagged with priorities and the output of which is one or more potential fault scenarios tagged by a confidence factor. The results from the system are used by a model-based diagnostician to predict the future health of the monitored system.

Knowledge Base for Automatic Generation of Online IMS LD Compliant Course Structures

ERIC Educational Resources Information Center

Pacurar, Ecaterina Giacomini; Trigano, Philippe; Alupoaie, Sorin

2006-01-01

Our article presents a pedagogical scenarios-based web application that allows the automatic generation and development of pedagogical websites. These pedagogical scenarios are represented in the IMS Learning Design standard. Our application is a web portal helping teachers to dynamically generate web course structures, to edit pedagogical content…

Cooperative Knowledge Bases.

DTIC Science & Technology

1988-02-01

intellegent knowledge bases. The present state of our system for concurrent evaluation of a knowledge base of logic clauses using static allocation...de Kleer, J., An assumption-based TMS, Artificial Intelligence, Vol. 28, No. 2, 1986. [Doyle 79) Doyle, J. A truth maintenance system, Artificial

Knowledge, attitudes and intention regarding mHealth in generation Y: evidence from a population based cross sectional study in Chakaria, Bangladesh

PubMed Central

Rahman, M Shafiqur; Hanifi, Syed; Khatun, Fatema; Iqbal, Mohammad; Rasheed, Sabrina; Ahmed, Tanvir; Hoque, Shahidul; Sharmin, Tamanna; Khan, Nazib-Uz Zaman; Mahmood, Shehrin Shaila; Bhuiya, Abbas

2017-01-01

Background and objectives mHealth offers a new opportunity to ensure access to qualified healthcare providers. Therefore, to better understand its potential in Bangladesh, it is important to understand how young people use mobile phones for healthcare. Here we examine the knowledge, attitudes and intentions to use mHealth services among young population. Design Population based cross sectional household survey. Setting and participants A total of 4909 respondents, aged 18 years and above, under the Chakaria Health and Demographic Surveillance System (HDSS) area, were interviewed during the period November 2012 to April 2013. Methods Participants younger than 30 years of age were defined as young (or generation Y). To examine the level of knowledge about and intention towards mHealth services in generation Y compared with their older counterparts, the percentage of the respective outcome measure from a 2×2 contingency table and adjusted odds ratio (aOR), which controls for potential confounders such as mobile ownership, sex, education, occupation and socioeconomic status, were estimated. The aOR was estimated using both the Cochran–Mantel–Haenszel approach and multivariable logistic regression models controlling for confounders. Results Generation Y had significantly greater access to mobile phones (50%vs40%) and better knowledge about its use for healthcare (37.8%vs27.5%;aOR 1.6 (95% CI1.3 to 2.0)). Furthermore, the level of knowledge about two existing mHealth services in generation Y was significantly higher compared with their older counterparts, with aOR values of 3.2 (95% CI 2.6 to 5.5) and 1.5 (95% CI 1.1 to 1.8), respectively. Similarly, generation Y showed significantly greater intention towards future use of mHealth services compared with their older counterparts (aOR 1.3 (95% CI 1.1 to 1.4)). The observed associations were not modified by sociodemographic factors. Conclusion There is a greater potential for mHealth services in the future among

Multi-Generational Knowledge Sharing for NASA Engineers

NASA Technical Reports Server (NTRS)

Topousis, Daria E.

2009-01-01

NASA, like many other organizations, is facing major challenges when it comes to its workforce. The average age of its personnel is 46, and 68 percent of its population is between 35 and 55. According to the U.S. Government Accounting Office, if the workforce continues aging, not enough engineers will have moved up the ranks and have the requisite skills to enable NASA to meet its vision for space exploration. In order to meet its goals of developing a new generation of spacecraft to support human spaceflight to the moon and Mars, the agency must engage and retain younger generations of workers and bridge the gaps between the four generations working today. Knowledge sharing among the generations is more critical than ever. This paper describes the strategies used to develop the NASA Engineering Network with the goal of engaging different generations.

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.

Knowledge 'Translation' as social learning: negotiating the uptake of research-based knowledge in practice.

PubMed

Salter, K L; Kothari, A

2016-02-29

Knowledge translation and evidence-based practice have relied on research derived from clinical trials, which are considered to be methodologically rigorous. The result is practice recommendations based on a narrow view of evidence. We discuss how, within a practice environment, in fact individuals adopt and apply new evidence derived from multiple sources through ongoing, iterative learning cycles. The discussion is presented in four sections. After elaborating on the multiple forms of evidence used in practice, in section 2 we argue that the practitioner derives contextualized knowledge through reflective practice. Then, in section 3, the focus shifts from the individual to the team with consideration of social learning and theories of practice. In section 4 we discuss the implications of integrative and negotiated knowledge exchange and generation within the practice environment. Namely, how can we promote the use of research within a team-based, contextualized knowledge environment? We suggest support for: 1) collaborative learning environments for active learning and reflection, 2) engaged scholarship approaches so that practice can inform research in a collaborative manner and 3) leveraging authoritative opinion leaders for their clinical expertise during the shared negotiation of knowledge and research. Our approach also points to implications for studying evidence-informed practice: the identification of practice change (as an outcome) ought to be supplemented with understandings of how and when social negotiation processes occur to achieve integrated knowledge. This article discusses practice knowledge as dependent on the practice context and on social learning processes, and suggests how research knowledge uptake might be supported from this vantage point.

Contrasting burnout, turnover intention, control, value congruence and knowledge sharing between Baby Boomers and Generation X.

PubMed

Leiter, Michael P; Jackson, Nicole J; Shaughnessy, Krystelle

2009-01-01

This paper examines the contrasting role of work values for nurses from two generations: Baby Boomers and Generation X. Differences among nurses regarding core values pertaining to their work has a potential to influence the quality of their work life. These differences may have implications for their vulnerability to job burnout. The analysis is based upon questionnaire surveys of nurses representing Generation X (n = 255) and Baby Boomers (n = 193) that contrasted their responses on job burnout, areas of work life, knowledge transfer and intention to quit. The analysis identified a greater person/organization value mismatch for Generation X nurses than for Baby Boomer nurses. Their greater value mismatch was associated with a greater susceptibility to burnout and a stronger intention to quit for Generation X nurses. The article notes the influence of Baby Boomer nurses in the structure of work and the application of new knowledge in health care work settings. Implications for recruitment and retention are discussed with a focus on knowledge transfer activities associated with distinct learning styles. Understanding value differences between generations will help nursing managers to develop more responsive work settings for nurses of all ages.

Knowledge-based and model-based hybrid methodology for comprehensive waste minimization in electroplating plants

NASA Astrophysics Data System (ADS)

Luo, Keqin

1999-11-01

The electroplating industry of over 10,000 planting plants nationwide is one of the major waste generators in the industry. Large quantities of wastewater, spent solvents, spent process solutions, and sludge are the major wastes generated daily in plants, which costs the industry tremendously for waste treatment and disposal and hinders the further development of the industry. It becomes, therefore, an urgent need for the industry to identify technically most effective and economically most attractive methodologies and technologies to minimize the waste, while the production competitiveness can be still maintained. This dissertation aims at developing a novel WM methodology using artificial intelligence, fuzzy logic, and fundamental knowledge in chemical engineering, and an intelligent decision support tool. The WM methodology consists of two parts: the heuristic knowledge-based qualitative WM decision analysis and support methodology and fundamental knowledge-based quantitative process analysis methodology for waste reduction. In the former, a large number of WM strategies are represented as fuzzy rules. This becomes the main part of the knowledge base in the decision support tool, WMEP-Advisor. In the latter, various first-principles-based process dynamic models are developed. These models can characterize all three major types of operations in an electroplating plant, i.e., cleaning, rinsing, and plating. This development allows us to perform a thorough process analysis on bath efficiency, chemical consumption, wastewater generation, sludge generation, etc. Additional models are developed for quantifying drag-out and evaporation that are critical for waste reduction. The models are validated through numerous industrial experiments in a typical plating line of an industrial partner. The unique contribution of this research is that it is the first time for the electroplating industry to (i) use systematically available WM strategies, (ii) know quantitatively and

Drawing on Dynamic Local Knowledge through Student-Generated Photography

ERIC Educational Resources Information Center

Coles-Ritchie, Marilee; Monson, Bayley; Moses, Catherine

2015-01-01

In this research, the authors explored how teachers using student-generated photography draw on local knowledge. The study draws on the framework of funds of knowledge to highlight the assets marginalized students bring to the classroom and the need for culturally relevant pedagogy to address the needs of a diverse public school population. The…

Knowledge Acquisition Using Linguistic-Based Knowledge Analysis

Treesearch

Daniel L. Schmoldt

1998-01-01

Most knowledge-based system developmentefforts include acquiring knowledge from one or more sources. difficulties associated with this knowledge acquisition task are readily acknowledged by most researchers. While a variety of knowledge acquisition methods have been reported, little has been done to organize those different methods and to suggest how to apply them...

Foundation: Transforming data bases into knowledge bases

NASA Technical Reports Server (NTRS)

Purves, R. B.; Carnes, James R.; Cutts, Dannie E.

1987-01-01

One approach to transforming information stored in relational data bases into knowledge based representations and back again is described. This system, called Foundation, allows knowledge bases to take advantage of vast amounts of pre-existing data. A benefit of this approach is inspection, and even population, of data bases through an intelligent knowledge-based front-end.

Bermuda Triangle or three to tango: generation Y, e-health and knowledge management.

PubMed

Yee, Kwang Chien

2007-01-01

Generation Y workers are slowly gathering critical mass in the healthcare sector. The sustainability of future healthcare is highly dependent on this group of workers. This generation of workers loves technology and thrives in stimulating environments. They have great thirst for life-experience and therefore they move from one working environment to the other. The healthcare system has a hierarchical operational, information and knowledge structure, which unfortunately might not be the ideal ground to integrate with generation Y. The challenges ahead present a fantastic opportunity for electronic health implementation and knowledge management to flourish. Generation Y workers, however, have very different expectation of technology utilisation, technology design and knowledge presentation. This paper will argue that a clear understanding of this group of workers is essential for researchers in health informatics and knowledge management in order to provide socio-technical integrated solution for this group of future workers. The sustainability of a quality healthcare system will depend upon the integration of generation Y, health informatics and knowledge management strategies in a re-invented healthcare system.

[Trends on generation and reproduction of knowledge about economic evaluation and health].

PubMed

Arredondo, A; Parada, I

2001-08-01

This paper identifies the trends and recent progress in the generation and reproduction of knowledge on health economic evaluation. Analysis is organized along nine public health action fields, namely: health determinants and predictors, economic value of health, healthcare demand, healthcare supply, microeconomic evaluation of healthcare, healthcare market balance, evaluation of policy instruments, general evaluation of the health system, and healthcare planning, regulation and supervision. Each action field is defined to place the reader in the proper setting and level of analysis. In addition, thematic research topics developed in each action field are proposed and discussed. The generation and reproduction of knowledge on the different action fields was based on the review of the bibliographic databases MEDLINE and LILACS for the 1992-2000 period. Results lead to the conclusion that development and application of economic evaluation of healthcare has been uneven across different countries and that there is a growing increase of applications starting in 1994, the year of initiation of healthcare reform in Latin America.

Knowledge, attitudes and intention regarding mHealth in generation Y: evidence from a population based cross sectional study in Chakaria, Bangladesh.

PubMed

Rahman, M Shafiqur; Hanifi, Syed; Khatun, Fatema; Iqbal, Mohammad; Rasheed, Sabrina; Ahmed, Tanvir; Hoque, Shahidul; Sharmin, Tamanna; Khan, Nazib-Uz Zaman; Mahmood, Shehrin Shaila; Bhuiya, Abbas

2017-11-15

mHealth offers a new opportunity to ensure access to qualified healthcare providers. Therefore, to better understand its potential in Bangladesh, it is important to understand how young people use mobile phones for healthcare. Here we examine the knowledge, attitudes and intentions to use mHealth services among young population. Population based cross sectional household survey. A total of 4909 respondents, aged 18 years and above, under the Chakaria Health and Demographic Surveillance System (HDSS) area, were interviewed during the period November 2012 to April 2013. Participants younger than 30 years of age were defined as young (or generation Y). To examine the level of knowledge about and intention towards mHealth services in generation Y compared with their older counterparts, the percentage of the respective outcome measure from a 2×2 contingency table and adjusted odds ratio (aOR), which controls for potential confounders such as mobile ownership, sex, education, occupation and socioeconomic status, were estimated. The aOR was estimated using both the Cochran-Mantel-Haenszel approach and multivariable logistic regression models controlling for confounders. Generation Y had significantly greater access to mobile phones (50%vs40%) and better knowledge about its use for healthcare (37.8%vs27.5%;aOR 1.6 (95% CI1.3 to 2.0)). Furthermore, the level of knowledge about two existing mHealth services in generation Y was significantly higher compared with their older counterparts, with aOR values of 3.2 (95% CI 2.6 to 5.5) and 1.5 (95% CI 1.1 to 1.8), respectively. Similarly, generation Y showed significantly greater intention towards future use of mHealth services compared with their older counterparts (aOR 1.3 (95% CI 1.1 to 1.4)). The observed associations were not modified by sociodemographic factors. There is a greater potential for mHealth services in the future among young people compared with older age groups. However, given the low overall use of m

Integrative pathway knowledge bases as a tool for systems molecular medicine.

PubMed

Liang, Mingyu

2007-08-20

There exists a sense of urgency to begin to generate a cohesive assembly of biomedical knowledge as the pace of knowledge accumulation accelerates. The urgency is in part driven by the emergence of systems molecular medicine that emphasizes the combination of systems analysis and molecular dissection in the future of medical practice and research. A potentially powerful approach is to build integrative pathway knowledge bases that link organ systems function with molecules.

Organizational culture and knowledge management in the electric power generation industry

NASA Astrophysics Data System (ADS)

Mayfield, Robert D.

Scarcity of knowledge and expertise is a challenge in the electric power generation industry. Today's most pervasive knowledge issues result from employee turnover and the constant movement of employees from project to project inside organizations. To address scarcity of knowledge and expertise, organizations must enable employees to capture, transfer, and use mission-critical explicit and tacit knowledge. The purpose of this qualitative grounded theory research was to examine the relationship between and among organizations within the electric power generation industry developing knowledge management processes designed to retain, share, and use the industry, institutional, and technical knowledge upon which the organizations depend. The research findings show that knowledge management is a business problem within the domain of information systems and management. The risks associated with losing mission critical-knowledge can be measured using metrics on employee retention, recruitment, productivity, training and benchmarking. Certain enablers must be in place in order to engage people, encourage cooperation, create a knowledge-sharing culture, and, ultimately change behavior. The research revealed the following change enablers that support knowledge management strategies: (a) training - blended learning, (b) communities of practice, (c) cross-functional teams, (d) rewards and recognition programs, (e) active senior management support, (f) communication and awareness, (g) succession planning, and (h) team organizational culture.

Generating new knowledge in cardiac interventions.

PubMed

Blackstone, Eugene H

2013-06-01

Cardiac interventions are among the most quantitatively studied therapies. It is important for all involved with cardiac interventions to understand how information generated from observations made during patient care is transformed into data suitable for analysis, to appreciate at a high level what constitutes appropriate analyses of those data, to effectively evaluate inferences drawn from those analyses, and to apply new knowledge to better care for individual patients. Copyright © 2013 Elsevier Inc. All rights reserved.

A prototype knowledge-based simulation support system

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

Hill, T.R.; Roberts, S.D.

1987-04-01

As a preliminary step toward the goal of an intelligent automated system for simulation modeling support, we explore the feasibility of the overall concept by generating and testing a prototypical framework. A prototype knowledge-based computer system was developed to support a senior level course in industrial engineering so that the overall feasibility of an expert simulation support system could be studied in a controlled and observable setting. The system behavior mimics the diagnostic (intelligent) process performed by the course instructor and teaching assistants, finding logical errors in INSIGHT simulation models and recommending appropriate corrective measures. The system was programmed inmore » a non-procedural language (PROLOG) and designed to run interactively with students working on course homework and projects. The knowledge-based structure supports intelligent behavior, providing its users with access to an evolving accumulation of expert diagnostic knowledge. The non-procedural approach facilitates the maintenance of the system and helps merge the roles of expert and knowledge engineer by allowing new knowledge to be easily incorporated without regard to the existing flow of control. The background, features and design of the system are describe and preliminary results are reported. Initial success is judged to demonstrate the utility of the reported approach and support the ultimate goal of an intelligent modeling system which can support simulation modelers outside the classroom environment. Finally, future extensions are suggested.« less

Knowledge-based fragment binding prediction.

PubMed

Tang, Grace W; Altman, Russ B

2014-04-01

Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening.

Knowledge-based Fragment Binding Prediction

PubMed Central

Tang, Grace W.; Altman, Russ B.

2014-01-01

Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening. PMID:24762971

Knowledge-based machine indexing from natural language text: Knowledge base design, development, and maintenance

NASA Technical Reports Server (NTRS)

Genuardi, Michael T.

1993-01-01

One strategy for machine-aided indexing (MAI) is to provide a concept-level analysis of the textual elements of documents or document abstracts. In such systems, natural-language phrases are analyzed in order to identify and classify concepts related to a particular subject domain. The overall performance of these MAI systems is largely dependent on the quality and comprehensiveness of their knowledge bases. These knowledge bases function to (1) define the relations between a controlled indexing vocabulary and natural language expressions; (2) provide a simple mechanism for disambiguation and the determination of relevancy; and (3) allow the extension of concept-hierarchical structure to all elements of the knowledge file. After a brief description of the NASA Machine-Aided Indexing system, concerns related to the development and maintenance of MAI knowledge bases are discussed. Particular emphasis is given to statistically-based text analysis tools designed to aid the knowledge base developer. One such tool, the Knowledge Base Building (KBB) program, presents the domain expert with a well-filtered list of synonyms and conceptually-related phrases for each thesaurus concept. Another tool, the Knowledge Base Maintenance (KBM) program, functions to identify areas of the knowledge base affected by changes in the conceptual domain (for example, the addition of a new thesaurus term). An alternate use of the KBM as an aid in thesaurus construction is also discussed.

An Ontology-Based Conceptual Model For Accumulating And Reusing Knowledge In A DMAIC Process

NASA Astrophysics Data System (ADS)

Nguyen, ThanhDat; Kifor, Claudiu Vasile

2015-09-01

DMAIC (Define, Measure, Analyze, Improve, and Control) is an important process used to enhance quality of processes basing on knowledge. However, it is difficult to access DMAIC knowledge. Conventional approaches meet a problem arising from structuring and reusing DMAIC knowledge. The main reason is that DMAIC knowledge is not represented and organized systematically. In this article, we overcome the problem basing on a conceptual model that is a combination of DMAIC process, knowledge management, and Ontology engineering. The main idea of our model is to utilizing Ontologies to represent knowledge generated by each of DMAIC phases. We build five different knowledge bases for storing all knowledge of DMAIC phases with the support of necessary tools and appropriate techniques in Information Technology area. Consequently, these knowledge bases provide knowledge available to experts, managers, and web users during or after DMAIC execution in order to share and reuse existing knowledge.

An object-oriented, knowledge-based system for cardiovascular rehabilitation--phase II.

PubMed Central

Ryder, R. M.; Inamdar, B.

1995-01-01

The Heart Monitor is an object-oriented, knowledge-based system designed to support the clinical activities of cardiovascular (CV) rehabilitation. The original concept was developed as part of graduate research completed in 1992. This paper describes the second generation system which is being implemented in collaboration with a local heart rehabilitation program. The PC UNIX-based system supports an extensive patient database organized by clinical areas. In addition, a knowledge base is employed to monitor patient status. Rule-based automated reasoning is employed to assess risk factors contraindicative to exercise therapy and to monitor administrative and statutory requirements. PMID:8563285

The experimenters' regress reconsidered: Replication, tacit knowledge, and the dynamics of knowledge generation.

PubMed

Feest, Uljana

2016-08-01

This paper revisits the debate between Harry Collins and Allan Franklin, concerning the experimenters' regress. Focusing my attention on a case study from recent psychology (regarding experimental evidence for the existence of a Mozart Effect), I argue that Franklin is right to highlight the role of epistemological strategies in scientific practice, but that his account does not sufficiently appreciate Collins's point about the importance of tacit knowledge in experimental practice. In turn, Collins rightly highlights the epistemic uncertainty (and skepticism) surrounding much experimental research. However, I will argue that his analysis of tacit knowledge fails to elucidate the reasons why scientists often are (and should be) skeptical of other researchers' experimental results. I will present an analysis of tacit knowledge in experimental research that not only answers to this desideratum, but also shows how such skepticism can in fact be a vital enabling factor for the dynamic processes of experimental knowledge generation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Validation of a Crowdsourcing Methodology for Developing a Knowledge Base of Related Problem-Medication Pairs.

PubMed

McCoy, A B; Wright, A; Krousel-Wood, M; Thomas, E J; McCoy, J A; Sittig, D F

2015-01-01

Clinical knowledge bases of problem-medication pairs are necessary for many informatics solutions that improve patient safety, such as clinical summarization. However, developing these knowledge bases can be challenging. We sought to validate a previously developed crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large, non-university health care system with a widely used, commercially available electronic health record. We first retrieved medications and problems entered in the electronic health record by clinicians during routine care during a six month study period. Following the previously published approach, we calculated the link frequency and link ratio for each pair then identified a threshold cutoff for estimated problem-medication pair appropriateness through clinician review; problem-medication pairs meeting the threshold were included in the resulting knowledge base. We selected 50 medications and their gold standard indications to compare the resulting knowledge base to the pilot knowledge base developed previously and determine its recall and precision. The resulting knowledge base contained 26,912 pairs, had a recall of 62.3% and a precision of 87.5%, and outperformed the pilot knowledge base containing 11,167 pairs from the previous study, which had a recall of 46.9% and a precision of 83.3%. We validated the crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large non-university health care system with a widely used, commercially available electronic health record, indicating that the approach may be generalizable across healthcare settings and clinical systems. Further research is necessary to better evaluate the knowledge, to compare crowdsourcing with other approaches, and to evaluate if incorporating the knowledge into electronic health records improves patient outcomes.

Generating Pedagogical Content Knowledge in Teacher Education Students

ERIC Educational Resources Information Center

van den Berg, Ed

2015-01-01

Some pre-service teaching activities can contribute much to the learning of pedagogical content knowledge (PCK) and subsequent teaching as these activities are "generating" PCK within the pre-service teacher's own classroom. Three examples are described: preparing exhibitions of science experiments, assessing preconceptions, and teaching…

Computer based interpretation of infrared spectra-structure of the knowledge-base, automatic rule generation and interpretation

NASA Astrophysics Data System (ADS)

Ehrentreich, F.; Dietze, U.; Meyer, U.; Abbas, S.; Schulz, H.

1995-04-01

It is a main task within the SpecInfo-Project to develop interpretation tools that can handle a great deal more of the complicated, more specific spectrum-structure-correlations. In the first step the empirical knowledge about the assignment of structural groups and their characteristic IR-bands has been collected from literature and represented in a computer readable well-structured form. Vague, verbal rules are managed by introduction of linguistic variables. The next step was the development of automatic rule generating procedures. We had combined and enlarged the IDIOTS algorithm with the algorithm by Blaffert relying on set theory. The procedures were successfully applied to the SpecInfo database. The realization of the preceding items is a prerequisite for the improvement of the computerized structure elucidation procedure.

A knowledge-based approach to automated flow-field zoning for computational fluid dynamics

NASA Technical Reports Server (NTRS)

Vogel, Alison Andrews

1989-01-01

An automated three-dimensional zonal grid generation capability for computational fluid dynamics is shown through the development of a demonstration computer program capable of automatically zoning the flow field of representative two-dimensional (2-D) aerodynamic configurations. The applicability of a knowledge-based programming approach to the domain of flow-field zoning is examined. Several aspects of flow-field zoning make the application of knowledge-based techniques challenging: the need for perceptual information, the role of individual bias in the design and evaluation of zonings, and the fact that the zoning process is modeled as a constructive, design-type task (for which there are relatively few examples of successful knowledge-based systems in any domain). Engineering solutions to the problems arising from these aspects are developed, and a demonstration system is implemented which can design, generate, and output flow-field zonings for representative 2-D aerodynamic configurations.

A knowledge base architecture for distributed knowledge agents

NASA Technical Reports Server (NTRS)

Riedesel, Joel; Walls, Bryan

1990-01-01

A tuple space based object oriented model for knowledge base representation and interpretation is presented. An architecture for managing distributed knowledge agents is then implemented within the model. The general model is based upon a database implementation of a tuple space. Objects are then defined as an additional layer upon the database. The tuple space may or may not be distributed depending upon the database implementation. A language for representing knowledge and inference strategy is defined whose implementation takes advantage of the tuple space. The general model may then be instantiated in many different forms, each of which may be a distinct knowledge agent. Knowledge agents may communicate using tuple space mechanisms as in the LINDA model as well as using more well known message passing mechanisms. An implementation of the model is presented describing strategies used to keep inference tractable without giving up expressivity. An example applied to a power management and distribution network for Space Station Freedom is given.

A Discussion of Knowledge Based Design

NASA Technical Reports Server (NTRS)

Wood, Richard M.; Bauer, Steven X. S.

1999-01-01

A discussion of knowledge and Knowledge- Based design as related to the design of aircraft is presented. The paper discusses the perceived problem with existing design studies and introduces the concepts of design and knowledge for a Knowledge- Based design system. A review of several Knowledge-Based design activities is provided. A Virtual Reality, Knowledge-Based system is proposed and reviewed. The feasibility of Virtual Reality to improve the efficiency and effectiveness of aerodynamic and multidisciplinary design, evaluation, and analysis of aircraft through the coupling of virtual reality technology and a Knowledge-Based design system is also reviewed. The final section of the paper discusses future directions for design and the role of Knowledge-Based design.

NASDA knowledge-based network planning system

NASA Technical Reports Server (NTRS)

Yamaya, K.; Fujiwara, M.; Kosugi, S.; Yambe, M.; Ohmori, M.

1993-01-01

One of the SODS (space operation and data system) sub-systems, NP (network planning) was the first expert system used by NASDA (national space development agency of Japan) for tracking and control of satellite. The major responsibilities of the NP system are: first, the allocation of network and satellite control resources and, second, the generation of the network operation plan data (NOP) used in automated control of the stations and control center facilities. Up to now, the first task of network resource scheduling was done by network operators. NP system automatically generates schedules using its knowledge base, which contains information on satellite orbits, station availability, which computer is dedicated to which satellite, and how many stations must be available for a particular satellite pass or a certain time period. The NP system is introduced.

Validation of a Crowdsourcing Methodology for Developing a Knowledge Base of Related Problem-Medication Pairs

PubMed Central

Wright, A.; Krousel-Wood, M.; Thomas, E. J.; McCoy, J. A.; Sittig, D. F.

2015-01-01

Summary Background Clinical knowledge bases of problem-medication pairs are necessary for many informatics solutions that improve patient safety, such as clinical summarization. However, developing these knowledge bases can be challenging. Objective We sought to validate a previously developed crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large, non-university health care system with a widely used, commercially available electronic health record. Methods We first retrieved medications and problems entered in the electronic health record by clinicians during routine care during a six month study period. Following the previously published approach, we calculated the link frequency and link ratio for each pair then identified a threshold cutoff for estimated problem-medication pair appropriateness through clinician review; problem-medication pairs meeting the threshold were included in the resulting knowledge base. We selected 50 medications and their gold standard indications to compare the resulting knowledge base to the pilot knowledge base developed previously and determine its recall and precision. Results The resulting knowledge base contained 26,912 pairs, had a recall of 62.3% and a precision of 87.5%, and outperformed the pilot knowledge base containing 11,167 pairs from the previous study, which had a recall of 46.9% and a precision of 83.3%. Conclusions We validated the crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large non-university health care system with a widely used, commercially available electronic health record, indicating that the approach may be generalizable across healthcare settings and clinical systems. Further research is necessary to better evaluate the knowledge, to compare crowdsourcing with other approaches, and to evaluate if incorporating the knowledge into electronic health records improves patient outcomes. PMID:26171079

Literature search strategies for conducting knowledge-building and theory-generating qualitative systematic reviews.

PubMed

Finfgeld-Connett, Deborah; Johnson, E Diane

2013-01-01

To report literature search strategies for the purpose of conducting knowledge-building and theory-generating qualitative systematic reviews. Qualitative systematic reviews lie on a continuum from knowledge-building and theory-generating to aggregating and summarizing. Different types of literature searches are needed to optimally support these dissimilar reviews. Articles published between 1989-Autumn 2011. These documents were identified using a hermeneutic approach and multiple literature search strategies. Redundancy is not the sole measure of validity when conducting knowledge-building and theory-generating systematic reviews. When conducting these types of reviews, literature searches should be consistent with the goal of fully explicating concepts and the interrelationships among them. To accomplish this objective, a 'berry picking' approach is recommended along with strategies for overcoming barriers to finding qualitative research reports. To enhance integrity of knowledge-building and theory-generating systematic reviews, reviewers are urged to make literature search processes as transparent as possible, despite their complexity. This includes fully explaining and rationalizing what databases were used and how they were searched. It also means describing how literature tracking was conducted and grey literature was searched. In the end, the decision to cease searching also needs to be fully explained and rationalized. Predetermined linear search strategies are unlikely to generate search results that are adequate for purposes of conducting knowledge-building and theory-generating qualitative systematic reviews. Instead, it is recommended that iterative search strategies take shape as reviews evolve. © 2012 Blackwell Publishing Ltd.

Supervised Learning Based Hypothesis Generation from Biomedical Literature.

PubMed

Sang, Shengtian; Yang, Zhihao; Li, Zongyao; Lin, Hongfei

2015-01-01

Nowadays, the amount of biomedical literatures is growing at an explosive speed, and there is much useful knowledge undiscovered in this literature. Researchers can form biomedical hypotheses through mining these works. In this paper, we propose a supervised learning based approach to generate hypotheses from biomedical literature. This approach splits the traditional processing of hypothesis generation with classic ABC model into AB model and BC model which are constructed with supervised learning method. Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts. Then through combining the two models, the approach reconstructs the ABC model and generates biomedical hypotheses from literature. The experimental results on the three classic Swanson hypotheses show that our approach outperforms SemRep system.

Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making.

PubMed

Capalbo, Susan M; Antle, John M; Seavert, Clark

2017-07-01

Research on next generation agricultural systems models shows that the most important current limitation is data, both for on-farm decision support and for research investment and policy decision making. One of the greatest data challenges is to obtain reliable data on farm management decision making, both for current conditions and under scenarios of changed bio-physical and socio-economic conditions. This paper presents a framework for the use of farm-level and landscape-scale models and data to provide analysis that could be used in NextGen knowledge products, such as mobile applications or personal computer data analysis and visualization software. We describe two analytical tools - AgBiz Logic and TOA-MD - that demonstrate the current capability of farmlevel and landscape-scale models. The use of these tools is explored with a case study of an oilseed crop, Camelina sativa , which could be used to produce jet aviation fuel. We conclude with a discussion of innovations needed to facilitate the use of farm and policy-level models to generate data and analysis for improved knowledge products.

Web-Based Learning as a Tool of Knowledge Continuity

ERIC Educational Resources Information Center

Jaaman, Saiful Hafizah; Ahmad, Rokiah Rozita; Rambely, Azmin Sham

2013-01-01

The outbreak of information in a borderless world has prompted lecturers to move forward together with the technological innovation and erudition of knowledge in performing his/her responsibility to educate the young generations to be able to stand above the crowd at the global scene. Teaching and Learning through web-based learning platform is a…

Knowledge-based approach to system integration

NASA Technical Reports Server (NTRS)

Blokland, W.; Krishnamurthy, C.; Biegl, C.; Sztipanovits, J.

1988-01-01

To solve complex problems one can often use the decomposition principle. However, a problem is seldom decomposable into completely independent subproblems. System integration deals with problem of resolving the interdependencies and the integration of the subsolutions. A natural method of decomposition is the hierarchical one. High-level specifications are broken down into lower level specifications until they can be transformed into solutions relatively easily. By automating the hierarchical decomposition and solution generation an integrated system is obtained in which the declaration of high level specifications is enough to solve the problem. We offer a knowledge-based approach to integrate the development and building of control systems. The process modeling is supported by using graphic editors. The user selects and connects icons that represent subprocesses and might refer to prewritten programs. The graphical editor assists the user in selecting parameters for each subprocess and allows the testing of a specific configuration. Next, from the definitions created by the graphical editor, the actual control program is built. Fault-diagnosis routines are generated automatically as well. Since the user is not required to write program code and knowledge about the process is present in the development system, the user is not required to have expertise in many fields.

Distributed, cooperating knowledge-based systems

NASA Technical Reports Server (NTRS)

Truszkowski, Walt

1991-01-01

Some current research in the development and application of distributed, cooperating knowledge-based systems technology is addressed. The focus of the current research is the spacecraft ground operations environment. The underlying hypothesis is that, because of the increasing size, complexity, and cost of planned systems, conventional procedural approaches to the architecture of automated systems will give way to a more comprehensive knowledge-based approach. A hallmark of these future systems will be the integration of multiple knowledge-based agents which understand the operational goals of the system and cooperate with each other and the humans in the loop to attain the goals. The current work includes the development of a reference model for knowledge-base management, the development of a formal model of cooperating knowledge-based agents, the use of testbed for prototyping and evaluating various knowledge-based concepts, and beginning work on the establishment of an object-oriented model of an intelligent end-to-end (spacecraft to user) system. An introductory discussion of these activities is presented, the major concepts and principles being investigated are highlighted, and their potential use in other application domains is indicated.

Developing genomic knowledge bases and databases to support clinical management: current perspectives.

PubMed

Huser, Vojtech; Sincan, Murat; Cimino, James J

2014-01-01

Personalized medicine, the ability to tailor diagnostic and treatment decisions for individual patients, is seen as the evolution of modern medicine. We characterize here the informatics resources available today or envisioned in the near future that can support clinical interpretation of genomic test results. We assume a clinical sequencing scenario (germline whole-exome sequencing) in which a clinical specialist, such as an endocrinologist, needs to tailor patient management decisions within his or her specialty (targeted findings) but relies on a genetic counselor to interpret off-target incidental findings. We characterize the genomic input data and list various types of knowledge bases that provide genomic knowledge for generating clinical decision support. We highlight the need for patient-level databases with detailed lifelong phenotype content in addition to genotype data and provide a list of recommendations for personalized medicine knowledge bases and databases. We conclude that no single knowledge base can currently support all aspects of personalized recommendations and that consolidation of several current resources into larger, more dynamic and collaborative knowledge bases may offer a future path forward.

Developing genomic knowledge bases and databases to support clinical management: current perspectives

PubMed Central

Huser, Vojtech; Sincan, Murat; Cimino, James J

2014-01-01

Personalized medicine, the ability to tailor diagnostic and treatment decisions for individual patients, is seen as the evolution of modern medicine. We characterize here the informatics resources available today or envisioned in the near future that can support clinical interpretation of genomic test results. We assume a clinical sequencing scenario (germline whole-exome sequencing) in which a clinical specialist, such as an endocrinologist, needs to tailor patient management decisions within his or her specialty (targeted findings) but relies on a genetic counselor to interpret off-target incidental findings. We characterize the genomic input data and list various types of knowledge bases that provide genomic knowledge for generating clinical decision support. We highlight the need for patient-level databases with detailed lifelong phenotype content in addition to genotype data and provide a list of recommendations for personalized medicine knowledge bases and databases. We conclude that no single knowledge base can currently support all aspects of personalized recommendations and that consolidation of several current resources into larger, more dynamic and collaborative knowledge bases may offer a future path forward. PMID:25276091

Establishing a national knowledge translation and generation network in kidney disease: the CAnadian KidNey KNowledge TraNslation and GEneration NeTwork.

PubMed

Manns, Braden; Barrett, Brendan; Evans, Michael; Garg, Amit; Hemmelgarn, Brenda; Kappel, Joanne; Klarenbach, Scott; Madore, Francois; Parfrey, Patrick; Samuel, Susan; Soroka, Steven; Suri, Rita; Tonelli, Marcello; Wald, Ron; Walsh, Michael; Zappitelli, Michael

2014-01-01

Patients with chronic kidney disease (CKD) do not always receive care consistent with guidelines, in part due to complexities in CKD management, lack of randomized trial data to inform care, and a failure to disseminate best practice. At a 2007 conference of key Canadian stakeholders in kidney disease, attendees noted that the impact of Canadian Society of Nephrology (CSN) guidelines was attenuated given limited formal linkages between the CSN Clinical Practice Guidelines Group, kidney researchers, decision makers and knowledge users, and that further knowledge was required to guide care in patients with kidney disease. The idea for the Canadian Kidney Knowledge Translation and Generation Network (CANN-NET) developed from this meeting. CANN-NET is a pan-Canadian network established in partnership with CSN, the Kidney Foundation of Canada and other professional societies to improve the care and outcomes of patients with and at risk for kidney disease. The initial priority areas for knowledge translation include improving optimal timing of dialysis initiation, and increasing the appropriate use of home dialysis. Given the urgent need for new knowledge, CANN-NET has also brought together a national group of experienced Canadian researchers to address knowledge gaps by encouraging and supporting multicentre randomized trials in priority areas, including management of cardiovascular disease in patients with kidney failure.

New Proposals for Generating and Exploiting Solution-Oriented Knowledge

ERIC Educational Resources Information Center

Gredig, Daniel; Sommerfeld, Peter

2008-01-01

The claim that professional social work should be based on scientific knowledge is many decades old with knowledge transfer usually moving in the direction from science to practice. The authors critique this model of knowledge transfer and support a hybrid one that places more of an emphasis on professional knowledge and action occurring in the…

A knowledge-based framework for image enhancement in aviation security.

PubMed

Singh, Maneesha; Singh, Sameer; Partridge, Derek

2004-12-01

The main aim of this paper is to present a knowledge-based framework for automatically selecting the best image enhancement algorithm from several available on a per image basis in the context of X-ray images of airport luggage. The approach detailed involves a system that learns to map image features that represent its viewability to one or more chosen enhancement algorithms. Viewability measures have been developed to provide an automatic check on the quality of the enhanced image, i.e., is it really enhanced? The choice is based on ground-truth information generated by human X-ray screening experts. Such a system, for a new image, predicts the best-suited enhancement algorithm. Our research details the various characteristics of the knowledge-based system and shows extensive results on real images.

D and D knowledge management information tool - a web based system developed to share D and D knowledge worldwide

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

Lagos, L.; Upadhyay, H.; Shoffner, P.

2013-07-01

Deactivation and decommissioning (D and D) work is a high risk and technically challenging enterprise within the U.S. Department of Energy complex. During the past three decades, the DOE's Office of Environmental Management has been in charge of carrying out one of the largest environmental restoration efforts in the world: the cleanup of the Manhattan Project legacy. In today's corporate world, worker experiences and knowledge that have developed over time represent a valuable corporate asset. The ever-dynamic workplace, coupled with an aging workforce, presents corporations with the ongoing challenge of preserving work-related experiences and knowledge for cross-generational knowledge transfer tomore » the future workforce [5]. To prevent the D and D knowledge base and expertise from being lost over time, the DOE and the Applied Research Center at Florida International University (FIU) have developed the web-based Knowledge Management Information Tool (KM-IT) to capture and maintain this valuable information in a universally available and easily accessible and usable system. The D and D KM-IT was developed in collaboration with DOE Headquarters (HQ), the Energy Facility Contractors Group (EFCOG), and the ALARA [as low as reasonably achievable] Centers at Savannah River Sites to preserve the D and D information generated and collected by the D and D community. This is an open secured system that can be accessed from https://www.dndkm.org over the web and through mobile devices at https://m.dndkm.org. This knowledge system serves as a centralized repository and provides a common interface for D and D-related activities. It also improves efficiency by reducing the need to rediscover knowledge and promotes the reuse of existing knowledge. It is a community-driven system that facilitates the gathering, analyzing, storing, and sharing of knowledge and information within the D and D community. It assists the DOE D and D community in identifying potential solutions to

Knowledge-based requirements analysis for automating software development

NASA Technical Reports Server (NTRS)

Markosian, Lawrence Z.

1988-01-01

We present a new software development paradigm that automates the derivation of implementations from requirements. In this paradigm, informally-stated requirements are expressed in a domain-specific requirements specification language. This language is machine-understable and requirements expressed in it are captured in a knowledge base. Once the requirements are captured, more detailed specifications and eventually implementations are derived by the system using transformational synthesis. A key characteristic of the process is that the required human intervention is in the form of providing problem- and domain-specific engineering knowledge, not in writing detailed implementations. We describe a prototype system that applies the paradigm in the realm of communication engineering: the prototype automatically generates implementations of buffers following analysis of the requirements on each buffer.

Development and evaluation of a crowdsourcing methodology for knowledge base construction: identifying relationships between clinical problems and medications

PubMed Central

Wright, Adam; Laxmisan, Archana; Ottosen, Madelene J; McCoy, Jacob A; Butten, David; Sittig, Dean F

2012-01-01

Objective We describe a novel, crowdsourcing method for generating a knowledge base of problem–medication pairs that takes advantage of manually asserted links between medications and problems. Methods Through iterative review, we developed metrics to estimate the appropriateness of manually entered problem–medication links for inclusion in a knowledge base that can be used to infer previously unasserted links between problems and medications. Results Clinicians manually linked 231 223 medications (55.30% of prescribed medications) to problems within the electronic health record, generating 41 203 distinct problem–medication pairs, although not all were accurate. We developed methods to evaluate the accuracy of the pairs, and after limiting the pairs to those meeting an estimated 95% appropriateness threshold, 11 166 pairs remained. The pairs in the knowledge base accounted for 183 127 total links asserted (76.47% of all links). Retrospective application of the knowledge base linked 68 316 medications not previously linked by a clinician to an indicated problem (36.53% of unlinked medications). Expert review of the combined knowledge base, including inferred and manually linked problem–medication pairs, found a sensitivity of 65.8% and a specificity of 97.9%. Conclusion Crowdsourcing is an effective, inexpensive method for generating a knowledge base of problem–medication pairs that is automatically mapped to local terminologies, up-to-date, and reflective of local prescribing practices and trends. PMID:22582202

Knowledge Base Editor (SharpKBE)

NASA Technical Reports Server (NTRS)

Tikidjian, Raffi; James, Mark; Mackey, Ryan

2007-01-01

The SharpKBE software provides a graphical user interface environment for domain experts to build and manage knowledge base systems. Knowledge bases can be exported/translated to various target languages automatically, including customizable target languages.

An automated knowledge-based textual summarization system for longitudinal, multivariate clinical data.

PubMed

Goldstein, Ayelet; Shahar, Yuval

2016-06-01

Design and implement an intelligent free-text summarization system: The system's input includes large numbers of longitudinal, multivariate, numeric and symbolic clinical raw data, collected over varying periods of time, and in different complex contexts, and a suitable medical knowledge base. The system then automatically generates a textual summary of the data. We aim to prove the feasibility of implementing such a system, and to demonstrate its potential benefits for clinicians and for enhancement of quality of care. We have designed a new, domain-independent, knowledge-based system, the CliniText system, for automated summarization in free text of longitudinal medical records of any duration, in any context. The system is composed of six components: (1) A temporal abstraction module generates all possible abstractions from the patient's raw data using a temporal-abstraction knowledge base; (2) The abductive reasoning module infers abstractions or events from the data, which were not explicitly included in the database; (3) The pruning module filters out raw or abstract data based on predefined heuristics; (4) The document structuring module organizes the remaining raw or abstract data, according to the desired format; (5) The microplanning module, groups the raw or abstract data and creates referring expressions; (6) The surface realization module, generates the text, and applies the grammar rules of the chosen language. We have performed an initial technical evaluation of the system in the cardiac intensive-care and diabetes domains. We also summarize the results of a more detailed evaluation study that we have performed in the intensive-care domain that assessed the completeness, correctness, and overall quality of the system's generated text, and its potential benefits to clinical decision making. We assessed these measures for 31 letters originally composed by clinicians, and for the same letters when generated by the CliniText system. We have successfully

Knowledge-based nursing diagnosis

NASA Astrophysics Data System (ADS)

Roy, Claudette; Hay, D. Robert

1991-03-01

Nursing diagnosis is an integral part of the nursing process and determines the interventions leading to outcomes for which the nurse is accountable. Diagnoses under the time constraints of modern nursing can benefit from a computer assist. A knowledge-based engineering approach was developed to address these problems. A number of problems were addressed during system design to make the system practical extended beyond capture of knowledge. The issues involved in implementing a professional knowledge base in a clinical setting are discussed. System functions, structure, interfaces, health care environment, and terminology and taxonomy are discussed. An integrated system concept from assessment through intervention and evaluation is outlined.

Intrusion Detection Systems with Live Knowledge System

DTIC Science & Technology

2016-05-31

Ripple -down Rule (RDR) to maintain the knowledge from human experts with knowledge base generated by the Induct RDR, which is a machine-learning based RDR...propose novel approach that uses Ripple -down Rule (RDR) to maintain the knowledge from human experts with knowledge base generated by the Induct RDR...detection model by applying Induct RDR approach. The proposed induct RDR ( Ripple Down Rules) approach allows to acquire the phishing detection

Cross-Generational Knowledge Flows in Edge Organizations: Research in Progress

DTIC Science & Technology

2007-06-01

organizational knowledge management. A storytelling culture through formal and informal mechanism should reflect organizational story- times and story-places...customer’s language; Role-play; Show empathy ; Measure customer satisfaction. Values and Motivation Aldisert, 1999 Generational distinctions Matures: born

Integrating knowledge and control into hypermedia-based training environments: Experiments with HyperCLIPS

NASA Technical Reports Server (NTRS)

Hill, Randall W., Jr.

1990-01-01

The issues of knowledge representation and control in hypermedia-based training environments are discussed. The main objective is to integrate the flexible presentation capability of hypermedia with a knowledge-based approach to lesson discourse management. The instructional goals and their associated concepts are represented in a knowledge representation structure called a 'concept network'. Its functional usages are many: it is used to control the navigation through a presentation space, generate tests for student evaluation, and model the student. This architecture was implemented in HyperCLIPS, a hybrid system that creates a bridge between HyperCard, a popular hypertext-like system used for building user interfaces to data bases and other applications, and CLIPS, a highly portable government-owned expert system shell.

The Effects of Domain Knowledge and Instructional Manipulation on Creative Idea Generation

ERIC Educational Resources Information Center

Hao, Ning

2010-01-01

The experiment was designed to explore the effects of domain knowledge, instructional manipulation, and the interaction between them on creative idea generation. Three groups of participants who respectively possessed the domain knowledge of biology, sports, or neither were asked to finish two tasks: imagining an extraterrestrial animal and…

Reusing Design Knowledge Based on Design Cases and Knowledge Map

ERIC Educational Resources Information Center

Yang, Cheng; Liu, Zheng; Wang, Haobai; Shen, Jiaoqi

2013-01-01

Design knowledge was reused for innovative design work to support designers with product design knowledge and help designers who lack rich experiences to improve their design capacity and efficiency. First, based on the ontological model of product design knowledge constructed by taxonomy, implicit and explicit knowledge was extracted from some…

Automated knowledge-base refinement

NASA Technical Reports Server (NTRS)

Mooney, Raymond J.

1994-01-01

Over the last several years, we have developed several systems for automatically refining incomplete and incorrect knowledge bases. These systems are given an imperfect rule base and a set of training examples and minimally modify the knowledge base to make it consistent with the examples. One of our most recent systems, FORTE, revises first-order Horn-clause knowledge bases. This system can be viewed as automatically debugging Prolog programs based on examples of correct and incorrect I/O pairs. In fact, we have already used the system to debug simple Prolog programs written by students in a programming language course. FORTE has also been used to automatically induce and revise qualitative models of several continuous dynamic devices from qualitative behavior traces. For example, it has been used to induce and revise a qualitative model of a portion of the Reaction Control System (RCS) of the NASA Space Shuttle. By fitting a correct model of this portion of the RCS to simulated qualitative data from a faulty system, FORTE was also able to correctly diagnose simple faults in this system.

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing.

PubMed

Li, Chunhua; Zhao, Pengpeng; Sheng, Victor S; Xian, Xuefeng; Wu, Jian; Cui, Zhiming

2017-01-01

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing

PubMed Central

Xian, Xuefeng; Cui, Zhiming

2017-01-01

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost. PMID:28588611

Selection of Construction Methods: A Knowledge-Based Approach

PubMed Central

Skibniewski, Miroslaw

2013-01-01

The appropriate selection of construction methods to be used during the execution of a construction project is a major determinant of high productivity, but sometimes this selection process is performed without the care and the systematic approach that it deserves, bringing negative consequences. This paper proposes a knowledge management approach that will enable the intelligent use of corporate experience and information and help to improve the selection of construction methods for a project. Then a knowledge-based system to support this decision-making process is proposed and described. To define and design the system, semistructured interviews were conducted within three construction companies with the purpose of studying the way that the method' selection process is carried out in practice and the knowledge associated with it. A prototype of a Construction Methods Knowledge System (CMKS) was developed and then validated with construction industry professionals. As a conclusion, the CMKS was perceived as a valuable tool for construction methods' selection, by helping companies to generate a corporate memory on this issue, reducing the reliance on individual knowledge and also the subjectivity of the decision-making process. The described benefits as provided by the system favor a better performance of construction projects. PMID:24453925

MO-D-BRC-03: Knowledge-Based Planning

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

Wu, Q.

Treatment planning is a central part of radiation therapy, including delineation in tumor volumes and critical organs, setting treatment goals of prescription doses to the tumor targets and tolerance doses to the critical organs, and finally generation of treatment plans to meet the treatment goals. National groups like RTOG have led the effort to standardize treatment goals of the prescription doses to the tumor targets and tolerance doses to the critical organs based on accumulated knowledge from decades of abundant clinical trial experience. The challenge for each clinical department is how to achieve or surpass these set goals within themore » time constraints of clinical practice. Using fifteen testing cases from different treatment sites such as head and neck, prostate with and without pelvic lymph nodes, SBRT spine, we will present clinically utility of advanced planning tools, including knowledge based, automatic based, and multiple criteria based tools that are clinically implemented. The objectives of this session are: Understand differences among these three advanced planning tools Provide clinical assessments on the utility of the advanced planning tools Discuss clinical challenges of treatment planning with large variations in tumor volumes and their relationships with adjacent critical organs. Ping Xia received research grant from Philips. Jackie Wu received research grant from Varian; P. Xia, Research support by Philips and Varian; Q. Wu, NIH, Varian Medical.« less

XML-Based SHINE Knowledge Base Interchange Language

NASA Technical Reports Server (NTRS)

James, Mark; Mackey, Ryan; Tikidjian, Raffi

2008-01-01

The SHINE Knowledge Base Interchange Language software has been designed to more efficiently send new knowledge bases to spacecraft that have been embedded with the Spacecraft Health Inference Engine (SHINE) tool. The intention of the behavioral model is to capture most of the information generally associated with a spacecraft functional model, while specifically addressing the needs of execution within SHINE and Livingstone. As such, it has some constructs that are based on one or the other.

Case-based reasoning: The marriage of knowledge base and data base

NASA Technical Reports Server (NTRS)

Pulaski, Kirt; Casadaban, Cyprian

1988-01-01

The coupling of data and knowledge has a synergistic effect when building an intelligent data base. The goal is to integrate the data and knowledge almost to the point of indistinguishability, permitting them to be used interchangeably. Examples given in this paper suggest that Case-Based Reasoning is a more integrated way to link data and knowledge than pure rule-based reasoning.

Concept maps: A tool for knowledge management and synthesis in web-based conversational learning.

PubMed

Joshi, Ankur; Singh, Satendra; Jaswal, Shivani; Badyal, Dinesh Kumar; Singh, Tejinder

2016-01-01

Web-based conversational learning provides an opportunity for shared knowledge base creation through collaboration and collective wisdom extraction. Usually, the amount of generated information in such forums is very huge, multidimensional (in alignment with the desirable preconditions for constructivist knowledge creation), and sometimes, the nature of expected new information may not be anticipated in advance. Thus, concept maps (crafted from constructed data) as "process summary" tools may be a solution to improve critical thinking and learning by making connections between the facts or knowledge shared by the participants during online discussion This exploratory paper begins with the description of this innovation tried on a web-based interacting platform (email list management software), FAIMER-Listserv, and generated qualitative evidence through peer-feedback. This process description is further supported by a theoretical construct which shows how social constructivism (inclusive of autonomy and complexity) affects the conversational learning. The paper rationalizes the use of concept map as mid-summary tool for extracting information and further sense making out of this apparent intricacy.

Medical Knowledge Bases.

ERIC Educational Resources Information Center

Miller, Randolph A.; Giuse, Nunzia B.

1991-01-01

Few commonly available, successful computer-based tools exist in medical informatics. Faculty expertise can be included in computer-based medical information systems. Computers allow dynamic recombination of knowledge to answer questions unanswerable with print textbooks. Such systems can also create stronger ties between academic and clinical…

Prior knowledge-based approach for associating ...

EPA Pesticide Factsheets

Evaluating the potential human health and/or ecological risks associated with exposures to complex chemical mixtures in the ambient environment is one of the central challenges of chemical safety assessment and environmental protection. There is a need for approaches that can help to integrate chemical monitoring and bio-effects data to evaluate risks associated with chemicals present in the environment. We used prior knowledge about chemical-gene interactions to develop a knowledge assembly model for detected chemicals at five locations near two wastewater treatment plants. The assembly model was used to generate hypotheses about the biological impacts of the chemicals at each location. The hypotheses were tested using empirical hepatic gene expression data from fathead minnows exposed for 12 d at each location. Empirical gene expression data was also mapped to the assembly models to statistically evaluate the likelihood of a chemical contributing to the observed biological responses. The prior knowledge approach was able reasonably hypothesize the biological impacts at one site but not the other. Chemicals most likely contributing to the observed biological responses were identified at each location. Despite limitations to the approach, knowledge assembly models have strong potential for associating chemical occurrence with potential biological effects and providing a foundation for hypothesis generation to guide research and/or monitoring efforts relat

Knowledge-Based Image Analysis.

DTIC Science & Technology

1981-04-01

UNCLASSIF1 ED ETL-025s N IIp ETL-0258 AL Ai01319 S"Knowledge-based image analysis u George C. Stockman Barbara A. Lambird I David Lavine Laveen N. Kanal...extraction, verification, region classification, pattern recognition, image analysis . 3 20. A. CT (Continue on rever.. d. It necessary and Identify by...UNCLgSTFTF n In f SECURITY CLASSIFICATION OF THIS PAGE (When Date Entered) .L1 - I Table of Contents Knowledge Based Image Analysis I Preface

Next generation agricultural system data, models and knowledge products: Introduction.

PubMed

Antle, John M; Jones, James W; Rosenzweig, Cynthia E

2017-07-01

Agricultural system models have become important tools to provide predictive and assessment capability to a growing array of decision-makers in the private and public sectors. Despite ongoing research and model improvements, many of the agricultural models today are direct descendants of research investments initially made 30-40 years ago, and many of the major advances in data, information and communication technology (ICT) of the past decade have not been fully exploited. The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the next generation of agricultural systems data, models and knowledge products. The Special Issue is based on a "NextGen" study led by the Agricultural Model Intercomparison and Improvement Project (AgMIP) with support from the Bill and Melinda Gates Foundation.

Next Generation Agricultural System Data, Models and Knowledge Products: Introduction

NASA Technical Reports Server (NTRS)

Antle, John M.; Jones, James W.; Rosenzweig, Cynthia E.

2016-01-01

Agricultural system models have become important tools to provide predictive and assessment capability to a growing array of decision-makers in the private and public sectors. Despite ongoing research and model improvements, many of the agricultural models today are direct descendants of research investments initially made 30-40 years ago, and many of the major advances in data, information and communication technology (ICT) of the past decade have not been fully exploited. The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the next generation of agricultural systems data, models and knowledge products. The Special Issue is based on a 'NextGen' study led by the Agricultural Model Intercomparison and Improvement Project (AgMIP) with support from the Bill and Melinda Gates Foundation.

Enhancing Learning Outcomes with an Interactive Knowledge-Based Learning Environment Providing Narrative Feedback

ERIC Educational Resources Information Center

Stranieri, Andrew; Yearwood, John

2008-01-01

This paper describes a narrative-based interactive learning environment which aims to elucidate reasoning using interactive scenarios that may be used in training novices in decision-making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a…

From Data to Knowledge: GEOSS experience and the GEOSS Knowledge Base contribution to the GCI

NASA Astrophysics Data System (ADS)

Santoro, M.; Nativi, S.; Mazzetti, P., Sr.; Plag, H. P.

2016-12-01

According to systems theory, data is raw, it simply exists and has no significance beyond its existence; while, information is data that has been given meaning by way of relational connection. The appropriate collection of information, such that it contributes to understanding, is a process of knowledge creation.The Global Earth Observation System of Systems (GEOSS) developed by the Group on Earth Observations (GEO) is a set of coordinated, independent Earth observation, information and processing systems that interact and provide access to diverse information for a broad range of users in both public and private sectors. GEOSS links these systems to strengthen the monitoring of the state of the Earth. In the past ten years, the development of GEOSS has taught several lessons dealing with the need to move from (open) data to information and knowledge sharing. Advanced user-focused services require to move from a data-driven framework to a knowledge sharing platform. Such a platform needs to manage information and knowledge, in addition to datasets linked to them. For this scope, GEO has launched a specific task called "GEOSS Knowledge Base", which deals with resources, like user requirements, Sustainable Development Goals (SDGs), observation and processing ontologies, publications, guidelines, best practices, business processes/algorithms, definition of advanced concepts like Essential Variables (EVs), indicators, strategic goals, etc. In turn, information and knowledge (e.g. guidelines, best practices, user requirements, business processes, algorithms, etc.) can be used to generate additional information and knowledge from shared datasets. To fully utilize and leverage the GEOSS Knowledge Base, the current GEOSS Common Infrastructure (GCI) model will be extended and advanced to consider important concepts and implementation artifacts, such as data processing services and environmental/economic models as well as EVs, Primary Indicators, and SDGs. The new GCI model

A knowledge based system for scientific data visualization

NASA Technical Reports Server (NTRS)

Senay, Hikmet; Ignatius, Eve

1992-01-01

A knowledge-based system, called visualization tool assistant (VISTA), which was developed to assist scientists in the design of scientific data visualization techniques, is described. The system derives its knowledge from several sources which provide information about data characteristics, visualization primitives, and effective visual perception. The design methodology employed by the system is based on a sequence of transformations which decomposes a data set into a set of data partitions, maps this set of partitions to visualization primitives, and combines these primitives into a composite visualization technique design. Although the primary function of the system is to generate an effective visualization technique design for a given data set by using principles of visual perception the system also allows users to interactively modify the design, and renders the resulting image using a variety of rendering algorithms. The current version of the system primarily supports visualization techniques having applicability in earth and space sciences, although it may easily be extended to include other techniques useful in other disciplines such as computational fluid dynamics, finite-element analysis and medical imaging.

Participatory approach to the development of a knowledge base for problem-solving in diabetes self-management.

PubMed

Cole-Lewis, Heather J; Smaldone, Arlene M; Davidson, Patricia R; Kukafka, Rita; Tobin, Jonathan N; Cassells, Andrea; Mynatt, Elizabeth D; Hripcsak, George; Mamykina, Lena

2016-01-01

To develop an expandable knowledge base of reusable knowledge related to self-management of diabetes that can be used as a foundation for patient-centric decision support tools. The structure and components of the knowledge base were created in participatory design with academic diabetes educators using knowledge acquisition methods. The knowledge base was validated using scenario-based approach with practicing diabetes educators and individuals with diabetes recruited from Community Health Centers (CHCs) serving economically disadvantaged communities and ethnic minorities in New York. The knowledge base includes eight glycemic control problems, over 150 behaviors known to contribute to these problems coupled with contextual explanations, and over 200 specific action-oriented self-management goals for correcting problematic behaviors, with corresponding motivational messages. The validation of the knowledge base suggested high level of completeness and accuracy, and identified improvements in cultural appropriateness. These were addressed in new iterations of the knowledge base. The resulting knowledge base is theoretically grounded, incorporates practical and evidence-based knowledge used by diabetes educators in practice settings, and allows for personally meaningful choices by individuals with diabetes. Participatory design approach helped researchers to capture implicit knowledge of practicing diabetes educators and make it explicit and reusable. The knowledge base proposed here is an important step towards development of new generation patient-centric decision support tools for facilitating chronic disease self-management. While this knowledge base specifically targets diabetes, its overall structure and composition can be generalized to other chronic conditions. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Participatory approach to the development of a knowledge base for problem-solving in diabetes self-management

PubMed Central

Cole-Lewis, Heather J.; Smaldone, Arlene M.; Davidson, Patricia R.; Kukafka, Rita; Tobin, Jonathan N.; Cassells, Andrea; Mynatt, Elizabeth D.; Hripcsak, George; Mamykina, Lena

2015-01-01

Objective To develop an expandable knowledge base of reusable knowledge related to self-management of diabetes that can be used as a foundation for patient-centric decision support tools. Materials and methods The structure and components of the knowledge base were created in participatory design with academic diabetes educators using knowledge acquisition methods. The knowledge base was validated using scenario-based approach with practicing diabetes educators and individuals with diabetes recruited from Community Health Centers (CHCs) serving economically disadvantaged communities and ethnic minorities in New York. Results The knowledge base includes eight glycemic control problems, over 150 behaviors known to contribute to these problems coupled with contextual explanations, and over 200 specific action-oriented self-management goals for correcting problematic behaviors, with corresponding motivational messages. The validation of the knowledge base suggested high level of completeness and accuracy, and identified improvements in cultural appropriateness. These were addressed in new iterations of the knowledge base. Discussion The resulting knowledge base is theoretically grounded, incorporates practical and evidence-based knowledge used by diabetes educators in practice settings, and allows for personally meaningful choices by individuals with diabetes. Participatory design approach helped researchers to capture implicit knowledge of practicing diabetes educators and make it explicit and reusable. Conclusion The knowledge base proposed here is an important step towards development of new generation patient-centric decision support tools for facilitating chronic disease self-management. While this knowledge base specifically targets diabetes, its overall structure and composition can be generalized to other chronic conditions. PMID:26547253

The COPD Knowledge Base: enabling data analysis and computational simulation in translational COPD research.

PubMed

Cano, Isaac; Tényi, Ákos; Schueller, Christine; Wolff, Martin; Huertas Migueláñez, M Mercedes; Gomez-Cabrero, David; Antczak, Philipp; Roca, Josep; Cascante, Marta; Falciani, Francesco; Maier, Dieter

2014-11-28

Previously we generated a chronic obstructive pulmonary disease (COPD) specific knowledge base (http://www.copdknowledgebase.eu) from clinical and experimental data, text-mining results and public databases. This knowledge base allowed the retrieval of specific molecular networks together with integrated clinical and experimental data. The COPDKB has now been extended to integrate over 40 public data sources on functional interaction (e.g. signal transduction, transcriptional regulation, protein-protein interaction, gene-disease association). In addition we integrated COPD-specific expression and co-morbidity networks connecting over 6 000 genes/proteins with physiological parameters and disease states. Three mathematical models describing different aspects of systemic effects of COPD were connected to clinical and experimental data. We have completely redesigned the technical architecture of the user interface and now provide html and web browser-based access and form-based searches. A network search enables the use of interconnecting information and the generation of disease-specific sub-networks from general knowledge. Integration with the Synergy-COPD Simulation Environment enables multi-scale integrated simulation of individual computational models while integration with a Clinical Decision Support System allows delivery into clinical practice. The COPD Knowledge Base is the only publicly available knowledge resource dedicated to COPD and combining genetic information with molecular, physiological and clinical data as well as mathematical modelling. Its integrated analysis functions provide overviews about clinical trends and connections while its semantically mapped content enables complex analysis approaches. We plan to further extend the COPDKB by offering it as a repository to publish and semantically integrate data from relevant clinical trials. The COPDKB is freely available after registration at http://www.copdknowledgebase.eu.

A feature dictionary supporting a multi-domain medical knowledge base.

PubMed

Naeymi-Rad, F

1989-01-01

Because different terminology is used by physicians of different specialties in different locations to refer to the same feature (signs, symptoms, test results), it is essential that our knowledge development tools provide a means to access a common pool of terms. This paper discusses the design of an online medical dictionary that provides a solution to this problem for developers of multi-domain knowledge bases for MEDAS (Medical Emergency Decision Assistance System). Our Feature Dictionary supports phrase equivalents for features, feature interactions, feature classifications, and translations to the binary features generated by the expert during knowledge creation. It is also used in the conversion of a domain knowledge to the database used by the MEDAS inference diagnostic sessions. The Feature Dictionary also provides capabilities for complex queries across multiple domains using the supported relations. The Feature Dictionary supports three methods for feature representation: (1) for binary features, (2) for continuous valued features, and (3) for derived features.

Content and structure of knowledge base used for virtual control of android arm motion in specified environment

NASA Astrophysics Data System (ADS)

Pritykin, F. N.; Nebritov, V. I.

2018-01-01

The paper presents the configuration of knowledge base necessary for intelligent control of android arm mechanism motion with different positions of certain forbidden regions taken into account. The present structure of the knowledge base characterizes the past experience of arm motion synthesis in the vector of velocities with due regard for the known obstacles. This structure also specifies its intrinsic properties. Knowledge base generation is based on the study of the arm mechanism instantaneous states implementations. Computational experiments connected with the virtual control of android arm motion with known forbidden regions using the developed knowledge base are introduced. Using the developed knowledge base to control virtually the arm motion reduces the time of test assignments calculation. The results of the research can be used in developing control systems of autonomous android robots in the known in advance environment.

Learning Ecosystem Complexity: A Study on Small-Scale Fishers' Ecological Knowledge Generation

ERIC Educational Resources Information Center

Garavito-Bermúdez, Diana

2018-01-01

Small-scale fisheries are learning contexts of importance for generating, transferring and updating ecological knowledge of natural environments through everyday work practices. The rich knowledge fishers have of local ecosystems is the result of the intimate relationship fishing communities have had with their natural environments across…

Building validation tools for knowledge-based systems

NASA Technical Reports Server (NTRS)

Stachowitz, R. A.; Chang, C. L.; Stock, T. S.; Combs, J. B.

1987-01-01

The Expert Systems Validation Associate (EVA), a validation system under development at the Lockheed Artificial Intelligence Center for more than a year, provides a wide range of validation tools to check the correctness, consistency and completeness of a knowledge-based system. A declarative meta-language (higher-order language), is used to create a generic version of EVA to validate applications written in arbitrary expert system shells. The architecture and functionality of EVA are presented. The functionality includes Structure Check, Logic Check, Extended Structure Check (using semantic information), Extended Logic Check, Semantic Check, Omission Check, Rule Refinement, Control Check, Test Case Generation, Error Localization, and Behavior Verification.

Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction

PubMed Central

Marks, Claire; Nowak, Jaroslaw; Klostermann, Stefan; Georges, Guy; Dunbar, James; Shi, Jiye; Kelm, Sebastian

2017-01-01

Abstract Motivation: Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction. Results: We show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the ab initio algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific ab initio methods, both in accuracy and speed. Availability and Implementation: Sphinx is available at http://opig.stats.ox.ac.uk/webapps/sphinx. Contact: deane@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28453681

Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction.

PubMed

Marks, Claire; Nowak, Jaroslaw; Klostermann, Stefan; Georges, Guy; Dunbar, James; Shi, Jiye; Kelm, Sebastian; Deane, Charlotte M

2017-05-01

Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction. We show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the ab initio algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific ab initio methods, both in accuracy and speed. Sphinx is available at http://opig.stats.ox.ac.uk/webapps/sphinx. deane@stats.ox.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

Health Care Leadership: Managing Knowledge Bases as Stakeholders.

PubMed

Rotarius, Timothy

Communities are composed of many organizations. These organizations naturally form clusters based on common patterns of knowledge, skills, and abilities of the individual organizations. Each of these spontaneous clusters represents a distinct knowledge base. The health care knowledge base is shown to be the natural leader of any community. Using the Central Florida region's 5 knowledge bases as an example, each knowledge base is categorized as a distinct type of stakeholder, and then a specific stakeholder management strategy is discussed to facilitate managing both the cooperative potential and the threatening potential of each "knowledge base" stakeholder.

Design of Composite Structures Using Knowledge-Based and Case Based Reasoning

NASA Technical Reports Server (NTRS)

Lambright, Jonathan Paul

1996-01-01

A method of using knowledge based and case based reasoning to assist designers during conceptual design tasks of composite structures was proposed. The cooperative use of heuristics, procedural knowledge, and previous similar design cases suggests a potential reduction in design cycle time and ultimately product lead time. The hypothesis of this work is that the design process of composite structures can be improved by using Case-Based Reasoning (CBR) and Knowledge-Based (KB) reasoning in the early design stages. The technique of using knowledge-based and case-based reasoning facilitates the gathering of disparate information into one location that is easily and readily available. The method suggests that the inclusion of downstream life-cycle issues into the conceptual design phase reduces potential of defective, and sub-optimal composite structures. Three industry experts were interviewed extensively. The experts provided design rules, previous design cases, and test problems. A Knowledge Based Reasoning system was developed using the CLIPS (C Language Interpretive Procedural System) environment and a Case Based Reasoning System was developed using the Design Memory Utility For Sharing Experiences (MUSE) xviii environment. A Design Characteristic State (DCS) was used to document the design specifications, constraints, and problem areas using attribute-value pair relationships. The DCS provided consistent design information between the knowledge base and case base. Results indicated that the use of knowledge based and case based reasoning provided a robust design environment for composite structures. The knowledge base provided design guidance from well defined rules and procedural knowledge. The case base provided suggestions on design and manufacturing techniques based on previous similar designs and warnings of potential problems and pitfalls. The case base complemented the knowledge base and extended the problem solving capability beyond the existence of

SU-E-T-129: Are Knowledge-Based Planning Dose Estimates Valid for Distensible Organs?

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

Lalonde, R; Heron, D; Huq, M

2015-06-15

Purpose: Knowledge-based planning programs have become available to assist treatment planning in radiation therapy. Such programs can be used to generate estimated DVHs and planning constraints for organs at risk (OARs), based upon a model generated from previous plans. These estimates are based upon the planning CT scan. However, for distensible OARs like the bladder and rectum, daily variations in volume may make the dose estimates invalid. The purpose of this study is to determine whether knowledge-based DVH dose estimates may be valid for distensible OARs. Methods: The Varian RapidPlan™ knowledge-based planning module was used to generate OAR dose estimatesmore » and planning objectives for 10 prostate cases previously planned with VMAT, and final plans were calculated for each. Five weekly setup CBCT scans of each patient were then downloaded and contoured (assuming no change in size and shape of the target volume), and rectum and bladder DVHs were recalculated for each scan. Dose volumes were then compared at 75, 60,and 40 Gy for the bladder and rectum between the planning scan and the CBCTs. Results: Plan doses and estimates matched well at all dose points., Volumes of the rectum and bladder varied widely between planning CT and the CBCTs, ranging from 0.46 to 2.42 for the bladder and 0.71 to 2.18 for the rectum, causing relative dose volumes to vary between planning CT and CBCT, but absolute dose volumes were more consistent. The overall ratio of CBCT/plan dose volumes was 1.02 ±0.27 for rectum and 0.98 ±0.20 for bladder in these patients. Conclusion: Knowledge-based planning dose volume estimates for distensible OARs are still valid, in absolute volume terms, between treatment planning scans and CBCT’s taken during daily treatment. Further analysis of the data is being undertaken to determine how differences depend upon rectum and bladder filling state. This work has been supported by Varian Medical Systems.« less

Automated knowledge base development from CAD/CAE databases

NASA Technical Reports Server (NTRS)

Wright, R. Glenn; Blanchard, Mary

1988-01-01

Knowledge base development requires a substantial investment in time, money, and resources in order to capture the knowledge and information necessary for anything other than trivial applications. This paper addresses a means to integrate the design and knowledge base development process through automated knowledge base development from CAD/CAE databases and files. Benefits of this approach include the development of a more efficient means of knowledge engineering, resulting in the timely creation of large knowledge based systems that are inherently free of error.

Evolution of co-management: role of knowledge generation, bridging organizations and social learning.

PubMed

Berkes, Fikret

2009-04-01

Over a period of some 20 years, different aspects of co-management (the sharing of power and responsibility between the government and local resource users) have come to the forefront. The paper focuses on a selection of these: knowledge generation, bridging organizations, social learning, and the emergence of adaptive co-management. Co-management can be considered a knowledge partnership. Different levels of organization, from local to international, have comparative advantages in the generation and mobilization of knowledge acquired at different scales. Bridging organizations provide a forum for the interaction of these different kinds of knowledge, and the coordination of other tasks that enable co-operation: accessing resources, bringing together different actors, building trust, resolving conflict, and networking. Social learning is one of these tasks, essential both for the co-operation of partners and an outcome of the co-operation of partners. It occurs most efficiently through joint problem solving and reflection within learning networks. Through successive rounds of learning and problem solving, learning networks can incorporate new knowledge to deal with problems at increasingly larger scales, with the result that maturing co-management arrangements become adaptive co-management in time.

A digital protection system incorporating knowledge based learning

NASA Astrophysics Data System (ADS)

Watson, Karan; Russell, B. Don; McCall, Kurt

A digital system architecture used to diagnoses the operating state and health of electric distribution lines and to generate actions for line protection is presented. The architecture is described functionally and to a limited extent at the hardware level. This architecture incorporates multiple analysis and fault-detection techniques utilizing a variety of parameters. In addition, a knowledge-based decision maker, a long-term memory retention and recall scheme, and a learning environment are described. Preliminary laboratory implementations of the system elements have been completed. Enhanced protection for electric distribution feeders is provided by this system. Advantages of the system are enumerated.

Learning Physics-based Models in Hydrology under the Framework of Generative Adversarial Networks

NASA Astrophysics Data System (ADS)

Karpatne, A.; Kumar, V.

2017-12-01

Generative adversarial networks (GANs), that have been highly successful in a number of applications involving large volumes of labeled and unlabeled data such as computer vision, offer huge potential for modeling the dynamics of physical processes that have been traditionally studied using simulations of physics-based models. While conventional physics-based models use labeled samples of input/output variables for model calibration (estimating the right parametric forms of relationships between variables) or data assimilation (identifying the most likely sequence of system states in dynamical systems), there is a greater opportunity to explore the full power of machine learning (ML) methods (e.g, GANs) for studying physical processes currently suffering from large knowledge gaps, e.g. ground-water flow. However, success in this endeavor requires a principled way of combining the strengths of ML methods with physics-based numerical models that are founded on a wealth of scientific knowledge. This is especially important in scientific domains like hydrology where the number of data samples is small (relative to Internet-scale applications such as image recognition where machine learning methods has found great success), and the physical relationships are complex (high-dimensional) and non-stationary. We will present a series of methods for guiding the learning of GANs using physics-based models, e.g., by using the outputs of physics-based models as input data to the generator-learner framework, and by using physics-based models as generators trained using validation data in the adversarial learning framework. These methods are being developed under the broad paradigm of theory-guided data science that we are developing to integrate scientific knowledge with data science methods for accelerating scientific discovery.

Conceptual model of knowledge base system

NASA Astrophysics Data System (ADS)

Naykhanova, L. V.; Naykhanova, I. V.

2018-05-01

In the article, the conceptual model of the knowledge based system by the type of the production system is provided. The production system is intended for automation of problems, which solution is rigidly conditioned by the legislation. A core component of the system is a knowledge base. The knowledge base consists of a facts set, a rules set, the cognitive map and ontology. The cognitive map is developed for implementation of a control strategy, ontology - the explanation mechanism. Knowledge representation about recognition of a situation in the form of rules allows describing knowledge of the pension legislation. This approach provides the flexibility, originality and scalability of the system. In the case of changing legislation, it is necessary to change the rules set. This means that the change of the legislation would not be a big problem. The main advantage of the system is that there is an opportunity to be adapted easily to changes of the legislation.

Studies in knowledge-based diagnosis of failures in robotic assembly

NASA Technical Reports Server (NTRS)

Lam, Raymond K.; Pollard, Nancy S.; Desai, Rajiv S.

1990-01-01

The telerobot diagnostic system (TDS) is a knowledge-based system that is being developed for identification and diagnosis of failures in the space robotic domain. The system is able to isolate the symptoms of the failure, generate failure hypotheses based on these symptoms, and test their validity at various levels by interpreting or simulating the effects of the hypotheses on results of plan execution. The implementation of the TDS is outlined. The classification of failures and the types of system models used by the TDS are discussed. A detailed example of the TDS approach to failure diagnosis is provided.

Knowledge-based tracking algorithm

NASA Astrophysics Data System (ADS)

Corbeil, Allan F.; Hawkins, Linda J.; Gilgallon, Paul F.

1990-10-01

This paper describes the Knowledge-Based Tracking (KBT) algorithm for which a real-time flight test demonstration was recently conducted at Rome Air Development Center (RADC). In KBT processing, the radar signal in each resolution cell is thresholded at a lower than normal setting to detect low RCS targets. This lower threshold produces a larger than normal false alarm rate. Therefore, additional signal processing including spectral filtering, CFAR and knowledge-based acceptance testing are performed to eliminate some of the false alarms. TSC's knowledge-based Track-Before-Detect (TBD) algorithm is then applied to the data from each azimuth sector to detect target tracks. In this algorithm, tentative track templates are formed for each threshold crossing and knowledge-based association rules are applied to the range, Doppler, and azimuth measurements from successive scans. Lastly, an M-association out of N-scan rule is used to declare a detection. This scan-to-scan integration enhances the probability of target detection while maintaining an acceptably low output false alarm rate. For a real-time demonstration of the KBT algorithm, the L-band radar in the Surveillance Laboratory (SL) at RADC was used to illuminate a small Cessna 310 test aircraft. The received radar signal wa digitized and processed by a ST-100 Array Processor and VAX computer network in the lab. The ST-100 performed all of the radar signal processing functions, including Moving Target Indicator (MTI) pulse cancelling, FFT Doppler filtering, and CFAR detection. The VAX computers performed the remaining range-Doppler clustering, beamsplitting and TBD processing functions. The KBT algorithm provided a 9.5 dB improvement relative to single scan performance with a nominal real time delay of less than one second between illumination and display.

Knowledge-based segmentation and feature analysis of hand and wrist radiographs

NASA Astrophysics Data System (ADS)

Efford, Nicholas D.

1993-07-01

The segmentation of hand and wrist radiographs for applications such as skeletal maturity assessment is best achieved by model-driven approaches incorporating anatomical knowledge. The reasons for this are discussed, and a particular frame-based or 'blackboard' strategy for the simultaneous segmentation of the hand and estimation of bone age via the TW2 method is described. The new approach is structured for optimum robustness and computational efficiency: features of interest are detected and analyzes in order of their size and prominence in the image, the largest and most distinctive being dealt with first, and the evidence generated by feature analysis is used to update a model of hand anatomy and hence guide later stages of the segmentation. Closed bone boundaries are formed by a hybrid technique combining knowledge-based, one-dimensional edge detection with model-assisted heuristic tree searching.

Adaptive Knowledge Management of Project-Based Learning

ERIC Educational Resources Information Center

Tilchin, Oleg; Kittany, Mohamed

2016-01-01

The goal of an approach to Adaptive Knowledge Management (AKM) of project-based learning (PBL) is to intensify subject study through guiding, inducing, and facilitating development knowledge, accountability skills, and collaborative skills of students. Knowledge development is attained by knowledge acquisition, knowledge sharing, and knowledge…

Network-based approaches to climate knowledge discovery

NASA Astrophysics Data System (ADS)

Budich, Reinhard; Nyberg, Per; Weigel, Tobias

2011-11-01

Climate Knowledge Discovery Workshop; Hamburg, Germany, 30 March to 1 April 2011 Do complex networks combined with semantic Web technologies offer the next generation of solutions in climate science? To address this question, a first Climate Knowledge Discovery (CKD) Workshop, hosted by the German Climate Computing Center (Deutsches Klimarechenzentrum (DKRZ)), brought together climate and computer scientists from major American and European laboratories, data centers, and universities, as well as representatives from industry, the broader academic community, and the semantic Web communities. The participants, representing six countries, were concerned with large-scale Earth system modeling and computational data analysis. The motivation for the meeting was the growing problem that climate scientists generate data faster than it can be interpreted and the need to prepare for further exponential data increases. Current analysis approaches are focused primarily on traditional methods, which are best suited for large-scale phenomena and coarse-resolution data sets. The workshop focused on the open discussion of ideas and technologies to provide the next generation of solutions to cope with the increasing data volumes in climate science.

Empirical Analysis and Refinement of Expert System Knowledge Bases

DTIC Science & Technology

1988-08-31

refinement. Both a simulated case generation program, and a random rule basher were developed to enhance rule refinement experimentation. *Substantial...the second fiscal year 88 objective was fully met. Rule Refinement System Simulated Rule Basher Case Generator Stored Cases Expert System Knowledge...generated until the rule is satisfied. Cases may be randomly generated for a given rule or hypothesis. Rule Basher Given that one has a correct

Quantum mechanical energy-based screening of combinatorially generated library of tautomers. TauTGen: a tautomer generator program.

PubMed

Harańczyk, Maciej; Gutowski, Maciej

2007-01-01

We describe a procedure of finding low-energy tautomers of a molecule. The procedure consists of (i) combinatorial generation of a library of tautomers, (ii) screening based on the results of geometry optimization of initial structures performed at the density functional level of theory, and (iii) final refinement of geometry for the top hits at the second-order Möller-Plesset level of theory followed by single-point energy calculations at the coupled cluster level of theory with single, double, and perturbative triple excitations. The library of initial structures of various tautomers is generated with TauTGen, a tautomer generator program. The procedure proved to be successful for these molecular systems for which common chemical knowledge had not been sufficient to predict the most stable structures.

The importance of knowledge-based technology.

PubMed

Cipriano, Pamela F

2012-01-01

Nurse executives are responsible for a workforce that can provide safer and more efficient care in a complex sociotechnical environment. National quality priorities rely on technologies to provide data collection, share information, and leverage analytic capabilities to interpret findings and inform approaches to care that will achieve better outcomes. As a key steward for quality, the nurse executive exercises leadership to provide the infrastructure to build and manage nursing knowledge and instill accountability for following evidence-based practices. These actions contribute to a learning health system where new knowledge is captured as a by-product of care delivery enabled by knowledge-based electronic systems. The learning health system also relies on rigorous scientific evidence embedded into practice at the point of care. The nurse executive optimizes use of knowledge-based technologies, integrated throughout the organization, that have the capacity to help transform health care.

A relational data-knowledge base system and its potential in developing a distributed data-knowledge system

NASA Technical Reports Server (NTRS)

Rahimian, Eric N.; Graves, Sara J.

1988-01-01

A new approach used in constructing a rational data knowledge base system is described. The relational database is well suited for distribution due to its property of allowing data fragmentation and fragmentation transparency. An example is formulated of a simple relational data knowledge base which may be generalized for use in developing a relational distributed data knowledge base system. The efficiency and ease of application of such a data knowledge base management system is briefly discussed. Also discussed are the potentials of the developed model for sharing the data knowledge base as well as the possible areas of difficulty in implementing the relational data knowledge base management system.

School-Based Educational Intervention to Improve Children's Oral Health-Related Knowledge.

PubMed

Blake, Holly; Dawett, Bhupinder; Leighton, Paul; Rose-Brady, Laura; Deery, Chris

2015-07-01

To evaluate a brief oral health promotion intervention delivered in schools by a primary care dental practice, aimed at changing oral health care knowledge and oral health-related behaviors in children. Cohort study with pretest-posttest design. Three primary schools. One hundred and fifty children (aged 9-12 years). Children received a 60-minute theory-driven classroom-based interactive educational session delivered by a dental care professional and received take-home literature on oral health. All children completed a questionnaire on oral health-related knowledge and self-reported oral health-related behaviors before, immediately after, and 6 weeks following the intervention. Children's dental knowledge significantly improved following the intervention, with improvement evident at immediate follow-up and maintained 6 weeks later. Significantly more children reported using dental floss 6 weeks after the intervention compared with baseline. No significant differences were detected in toothbrushing or dietary behaviors. School-based preventative oral health education delivered by primary care dental practices can generate short-term improvements in children's knowledge of oral health and some aspects of oral hygiene behavior. Future research should engage parents/carers and include objective clinical and behavioral outcomes in controlled study designs. © 2014 Society for Public Health Education.

Problem-Oriented Corporate Knowledge Base Models on the Case-Based Reasoning Approach Basis

NASA Astrophysics Data System (ADS)

Gluhih, I. N.; Akhmadulin, R. K.

2017-07-01

One of the urgent directions of efficiency enhancement of production processes and enterprises activities management is creation and use of corporate knowledge bases. The article suggests a concept of problem-oriented corporate knowledge bases (PO CKB), in which knowledge is arranged around possible problem situations and represents a tool for making and implementing decisions in such situations. For knowledge representation in PO CKB a case-based reasoning approach is encouraged to use. Under this approach, the content of a case as a knowledge base component has been defined; based on the situation tree a PO CKB knowledge model has been developed, in which the knowledge about typical situations as well as specific examples of situations and solutions have been represented. A generalized problem-oriented corporate knowledge base structural chart and possible modes of its operation have been suggested. The obtained models allow creating and using corporate knowledge bases for support of decision making and implementing, training, staff skill upgrading and analysis of the decisions taken. The universal interpretation of terms “situation” and “solution” adopted in the work allows using the suggested models to develop problem-oriented corporate knowledge bases in different subject domains. It has been suggested to use the developed models for making corporate knowledge bases of the enterprises that operate engineer systems and networks at large production facilities.

A Knowledge-Based System Developer for aerospace applications

NASA Technical Reports Server (NTRS)

Shi, George Z.; Wu, Kewei; Fensky, Connie S.; Lo, Ching F.

1993-01-01

A prototype Knowledge-Based System Developer (KBSD) has been developed for aerospace applications by utilizing artificial intelligence technology. The KBSD directly acquires knowledge from domain experts through a graphical interface then builds expert systems from that knowledge. This raises the state of the art of knowledge acquisition/expert system technology to a new level by lessening the need for skilled knowledge engineers. The feasibility, applicability , and efficiency of the proposed concept was established, making a continuation which would develop the prototype to a full-scale general-purpose knowledge-based system developer justifiable. The KBSD has great commercial potential. It will provide a marketable software shell which alleviates the need for knowledge engineers and increase productivity in the workplace. The KBSD will therefore make knowledge-based systems available to a large portion of industry.

A knowledge base browser using hypermedia

NASA Technical Reports Server (NTRS)

Pocklington, Tony; Wang, Lui

1990-01-01

A hypermedia system is being developed to browse CLIPS (C Language Integrated Production System) knowledge bases. This system will be used to help train flight controllers for the Mission Control Center. Browsing this knowledge base will be accomplished either by having navigating through the various collection nodes that have already been defined, or through the query languages.

Caregiving Antecedents of Secure Base Script Knowledge: A Comparative Analysis of Young Adult Attachment Representations

ERIC Educational Resources Information Center

Steele, Ryan D.; Waters, Theodore E. A.; Bost, Kelly K.; Vaughn, Brian E.; Truitt, Warren; Waters, Harriet S.; Booth-LaForce, Cathryn; Roisman, Glenn I.

2014-01-01

Based on a subsample (N = 673) of the NICHD Study of Early Child Care and Youth Development (SECCYD) cohort, this article reports data from a follow-up assessment at age 18 years on the antecedents of "secure base script knowledge", as reflected in the ability to generate narratives in which attachment-related difficulties are…

Placement Mentors Making Sense of Research-Based Knowledge

ERIC Educational Resources Information Center

Raaen, Finn Daniel

2017-01-01

Placement mentors' role increasingly implies demonstrating to student teachers how research-based knowledge in combination with experience-based knowledge may be relevant in teachers' professional work. This is a challenge. Placement mentors are often unsure how to make sense of research-based knowledge. Frequently there is a mismatch between what…

Teacher Education: Considerations for a Knowledge Base Framework.

ERIC Educational Resources Information Center

Tumposky, Nancy

Traditionally, the knowledge base has been defined more as product than process and has encompassed definitions, principles, values, and facts. Recent reforms in teaching and teacher education have brought about efforts to redefine the knowledge base. The reconceptualized knowledge base builds upon the earlier model but gives higher priority to…

Building a knowledge based economy in Russia using guided entrepreneurship

NASA Astrophysics Data System (ADS)

Reznik, Boris N.; Daniels, Marc; Ichim, Thomas E.; Reznik, David L.

2005-06-01

Despite advanced scientific and technological (S&T) expertise, the Russian economy is presently based upon manufacturing and raw material exports. Currently, governmental incentives are attempting to leverage the existing scientific infrastructure through the concept of building a Knowledge Based Economy. However, socio-economic changes do not occur solely by decree, but by alteration of approach to the market. Here we describe the "Guided Entrepreneurship" plan, a series of steps needed for generation of an army of entrepreneurs, which initiate a chain reaction of S&T-driven growth. The situation in Russia is placed in the framework of other areas where Guided Entrepreneurship has been successful.

Knowledge-based support for the participatory design and implementation of shift systems.

PubMed

Gissel, A; Knauth, P

1998-01-01

This study developed a knowledge-based software system to support the participatory design and implementation of shift systems as a joint planning process including shift workers, the workers' committee, and management. The system was developed using a model-based approach. During the 1st phase, group discussions were repeatedly conducted with 2 experts. Thereafter a structure model of the process was generated and subsequently refined by the experts in additional semistructured interviews. Next, a factual knowledge base of 1713 relevant studies was collected on the effects of shift work. Finally, a prototype of the knowledge-based system was tested on 12 case studies. During the first 2 phases of the system, important basic information about the tasks to be carried out is provided for the user. During the 3rd phase this approach uses the problem-solving method of case-based reasoning to determine a shift rota which has already proved successful in other applications. It can then be modified in the 4th phase according to the shift workers' preferences. The last 2 phases support the final testing and evaluation of the system. The application of this system has shown that it is possible to obtain shift rotas suitable to actual problems and representative of good ergonomic solutions. A knowledge-based approach seems to provide valuable support for the complex task of designing and implementing a new shift system. The separation of the task into several phases, the provision of information at all stages, and the integration of all parties concerned seem to be essential factors for the success of the application.

A knowledge-based design framework for airplane conceptual and preliminary design

NASA Astrophysics Data System (ADS)

Anemaat, Wilhelmus A. J.

The goal of work described herein is to develop the second generation of Advanced Aircraft Analysis (AAA) into an object-oriented structure which can be used in different environments. One such environment is the third generation of AAA with its own user interface, the other environment with the same AAA methods (i.e. the knowledge) is the AAA-AML program. AAA-AML automates the initial airplane design process using current AAA methods in combination with AMRaven methodologies for dependency tracking and knowledge management, using the TechnoSoft Adaptive Modeling Language (AML). This will lead to the following benefits: (1) Reduced design time: computer aided design methods can reduce design and development time and replace tedious hand calculations. (2) Better product through improved design: more alternative designs can be evaluated in the same time span, which can lead to improved quality. (3) Reduced design cost: due to less training and less calculation errors substantial savings in design time and related cost can be obtained. (4) Improved Efficiency: the design engineer can avoid technically correct but irrelevant calculations on incomplete or out of sync information, particularly if the process enables robust geometry earlier. Although numerous advancements in knowledge based design have been developed for detailed design, currently no such integrated knowledge based conceptual and preliminary airplane design system exists. The third generation AAA methods are tested over a ten year period on many different airplane designs. Using AAA methods will demonstrate significant time savings. The AAA-AML system will be exercised and tested using 27 existing airplanes ranging from single engine propeller, business jets, airliners, UAV's to fighters. Data for the varied sizing methods will be compared with AAA results, to validate these methods. One new design, a Light Sport Aircraft (LSA), will be developed as an exercise to use the tool for designing a new airplane

How Quality Improvement Practice Evidence Can Advance the Knowledge Base.

PubMed

OʼRourke, Hannah M; Fraser, Kimberly D

2016-01-01

Recommendations for the evaluation of quality improvement interventions have been made in order to improve the evidence base of whether, to what extent, and why quality improvement interventions affect chosen outcomes. The purpose of this article is to articulate why these recommendations are appropriate to improve the rigor of quality improvement intervention evaluation as a research endeavor, but inappropriate for the purposes of everyday quality improvement practice. To support our claim, we describe the differences between quality improvement interventions that occur for the purpose of practice as compared to research. We then carefully consider how feasibility, ethics, and the aims of evaluation each impact how quality improvement interventions that occur in practice, as opposed to research, can or should be evaluated. Recommendations that fit the evaluative goals of practice-based quality improvement interventions are needed to support fair appraisal of the distinct evidence they produce. We describe a current debate on the nature of evidence to assist in reenvisioning how quality improvement evidence generated from practice might complement that generated from research, and contribute in a value-added way to the knowledge base.

Modeling Guru: Knowledge Base for NASA Modelers

NASA Astrophysics Data System (ADS)

Seablom, M. S.; Wojcik, G. S.; van Aartsen, B. H.

2009-05-01

Modeling Guru is an on-line knowledge-sharing resource for anyone involved with or interested in NASA's scientific models or High End Computing (HEC) systems. Developed and maintained by the NASA's Software Integration and Visualization Office (SIVO) and the NASA Center for Computational Sciences (NCCS), Modeling Guru's combined forums and knowledge base for research and collaboration is becoming a repository for the accumulated expertise of NASA's scientific modeling and HEC communities. All NASA modelers and associates are encouraged to participate and provide knowledge about the models and systems so that other users may benefit from their experience. Modeling Guru is divided into a hierarchy of communities, each with its own set forums and knowledge base documents. Current modeling communities include those for space science, land and atmospheric dynamics, atmospheric chemistry, and oceanography. In addition, there are communities focused on NCCS systems, HEC tools and libraries, and programming and scripting languages. Anyone may view most of the content on Modeling Guru (available at http://modelingguru.nasa.gov/), but you must log in to post messages and subscribe to community postings. The site offers a full range of "Web 2.0" features, including discussion forums, "wiki" document generation, document uploading, RSS feeds, search tools, blogs, email notification, and "breadcrumb" links. A discussion (a.k.a. forum "thread") is used to post comments, solicit feedback, or ask questions. If marked as a question, SIVO will monitor the thread, and normally respond within a day. Discussions can include embedded images, tables, and formatting through the use of the Rich Text Editor. Also, the user can add "Tags" to their thread to facilitate later searches. The "knowledge base" is comprised of documents that are used to capture and share expertise with others. The default "wiki" document lets users edit within the browser so others can easily collaborate on the

Knowledge-acquisition tools for medical knowledge-based systems.

PubMed

Lanzola, G; Quaglini, S; Stefanelli, M

1995-03-01

Knowledge-based systems (KBS) have been proposed to solve a large variety of medical problems. A strategic issue for KBS development and maintenance are the efforts required for both knowledge engineers and domain experts. The proposed solution is building efficient knowledge acquisition (KA) tools. This paper presents a set of KA tools we are developing within a European Project called GAMES II. They have been designed after the formulation of an epistemological model of medical reasoning. The main goal is that of developing a computational framework which allows knowledge engineers and domain experts to interact cooperatively in developing a medical KBS. To this aim, a set of reusable software components is highly recommended. Their design was facilitated by the development of a methodology for KBS construction. It views this process as comprising two activities: the tailoring of the epistemological model to the specific medical task to be executed and the subsequent translation of this model into a computational architecture so that the connections between computational structures and their knowledge level counterparts are maintained. The KA tools we developed are illustrated taking examples from the behavior of a KBS we are building for the management of children with acute myeloid leukemia.

Knowledge-based diagnosis for aerospace systems

NASA Technical Reports Server (NTRS)

Atkinson, David J.

1988-01-01

The need for automated diagnosis in aerospace systems and the approach of using knowledge-based systems are examined. Research issues in knowledge-based diagnosis which are important for aerospace applications are treated along with a review of recent relevant research developments in Artificial Intelligence. The design and operation of some existing knowledge-based diagnosis systems are described. The systems described and compared include the LES expert system for liquid oxygen loading at NASA Kennedy Space Center, the FAITH diagnosis system developed at the Jet Propulsion Laboratory, the PES procedural expert system developed at SRI International, the CSRL approach developed at Ohio State University, the StarPlan system developed by Ford Aerospace, the IDM integrated diagnostic model, and the DRAPhys diagnostic system developed at NASA Langley Research Center.

Applying knowledge compilation techniques to model-based reasoning

NASA Technical Reports Server (NTRS)

Keller, Richard M.

1991-01-01

Researchers in the area of knowledge compilation are developing general purpose techniques for improving the efficiency of knowledge-based systems. In this article, an attempt is made to define knowledge compilation, to characterize several classes of knowledge compilation techniques, and to illustrate how some of these techniques can be applied to improve the performance of model-based reasoning systems.

Towards an Age-Phenome Knowledge-base

PubMed Central

2011-01-01

Background Currently, data about age-phenotype associations are not systematically organized and cannot be studied methodically. Searching for scientific articles describing phenotypic changes reported as occurring at a given age is not possible for most ages. Results Here we present the Age-Phenome Knowledge-base (APK), in which knowledge about age-related phenotypic patterns and events can be modeled and stored for retrieval. The APK contains evidence connecting specific ages or age groups with phenotypes, such as disease and clinical traits. Using a simple text mining tool developed for this purpose, we extracted instances of age-phenotype associations from journal abstracts related to non-insulin-dependent Diabetes Mellitus. In addition, links between age and phenotype were extracted from clinical data obtained from the NHANES III survey. The knowledge stored in the APK is made available for the relevant research community in the form of 'Age-Cards', each card holds the collection of all the information stored in the APK about a particular age. These Age-Cards are presented in a wiki, allowing community review, amendment and contribution of additional information. In addition to the wiki interaction, complex searches can also be conducted which require the user to have some knowledge of database query construction. Conclusions The combination of a knowledge model based repository with community participation in the evolution and refinement of the knowledge-base makes the APK a useful and valuable environment for collecting and curating existing knowledge of the connections between age and phenotypes. PMID:21651792

Using affective knowledge to generate and validate a set of emotion-related, action words.

PubMed

Portch, Emma; Havelka, Jelena; Brown, Charity; Giner-Sorolla, Roger

2015-01-01

Emotion concepts are built through situated experience. Abstract word meaning is grounded in this affective knowledge, giving words the potential to evoke emotional feelings and reactions (e.g., Vigliocco et al., 2009). In the present work we explore whether words differ in the extent to which they evoke 'specific' emotional knowledge. Using a categorical approach, in which an affective 'context' is created, it is possible to assess whether words proportionally activate knowledge relevant to different emotional states (e.g., 'sadness', 'anger', Stevenson, Mikels & James, 2007a). We argue that this method may be particularly effective when assessing the emotional meaning of action words (e.g., Schacht & Sommer, 2009). In study 1 we use a constrained feature generation task to derive a set of action words that participants associated with six, basic emotional states (see full list in Appendix S1). Generation frequencies were taken to indicate the likelihood that the word would evoke emotional knowledge relevant to the state to which it had been paired. In study 2 a rating task was used to assess the strength of association between the six most frequently generated, or 'typical', action words and corresponding emotion labels. Participants were presented with a series of sentences, in which action words (typical and atypical) and labels were paired e.g., "If you are feeling 'sad' how likely would you be to act in the following way?" … 'cry.' Findings suggest that typical associations were robust. Participants always gave higher ratings to typical vs. atypical action word and label pairings, even when (a) rating direction was manipulated (the label or verb appeared first in the sentence), and (b) the typical behaviours were to be performed by the rater themselves, or others. Our findings suggest that emotion-related action words vary in the extent to which they evoke knowledge relevant for different emotional states. When measuring affective grounding, it may then be

Use of knowledge-sharing web-based portal in gross and microscopic anatomy.

PubMed

Durosaro, Olayemi; Lachman, Nirusha; Pawlina, Wojciech

2008-12-01

Changes in worldwide healthcare delivery require review of current medical school curricula structure to develop learning outcomes that ensures mastery of knowledge and clinical competency. In the last 3 years, Mayo Medical School implemented outcomes-based curriculum to encompass new graduate outcomes. Standard courses were replaced by 6-week clinically-integrated didactic blocks separated by student-self selected academic enrichment activities. Gross and microscopic anatomy was integrated with radiology and genetics respectively. Laboratory components include virtual microscopy and anatomical dissection. Students assigned to teams utilise computer portals to share learning experiences. High-resolution computed tomographic (CT) scans of cadavers prior to dissection were made available for correlative learning between the cadaveric material and radiologic images. Students work in teams on assigned presentations that include histology, cell and molecular biology, genetics and genomic using the Nexus Portal, based on DrupalEd, to share their observations, reflections and dissection findings. New generation of medical students are clearly comfortable utilising web-based programmes that maximise their learning potential of conceptually difficult and labor intensive courses. Team-based learning approach emphasising the use of knowledge-sharing computer portals maximises opportunities for students to master their knowledge and improve cognitive skills to ensure clinical competency.

Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

PubMed Central

Roos, Marco; Marshall, M Scott; Gibson, Andrew P; Schuemie, Martijn; Meij, Edgar; Katrenko, Sophia; van Hage, Willem Robert; Krommydas, Konstantinos; Adriaans, Pieter W

2009-01-01

Background Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes. Results We describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence. Conclusion We demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific

The Relationship between Agriculture Knowledge Bases for Teaching and Sources of Knowledge

ERIC Educational Resources Information Center

Rice, Amber H.; Kitchel, Tracy

2015-01-01

The purpose of this study was to describe the agriculture knowledge bases for teaching of agriculture teachers and to see if a relationship existed between years of teaching experience, sources of knowledge, and development of pedagogical content knowledge (PCK), using quantitative methods. A model of PCK from mathematics was utilized as a…

Development of Korean Rare Disease Knowledge Base

PubMed Central

Seo, Heewon; Kim, Dokyoon; Chae, Jong-Hee; Kang, Hee Gyung; Lim, Byung Chan; Cheong, Hae Il

2012-01-01

Objectives Rare disease research requires a broad range of disease-related information for the discovery of causes of genetic disorders that are maladies caused by abnormalities in genes or chromosomes. A rarity in cases makes it difficult for researchers to elucidate definite inception. This knowledge base will be a major resource not only for clinicians, but also for the general public, who are unable to find consistent information on rare diseases in a single location. Methods We design a compact database schema for faster querying; its structure is optimized to store heterogeneous data sources. Then, clinicians at Seoul National University Hospital (SNUH) review and revise those resources. Additionally, we integrated other sources to capture genomic resources and clinical trials in detail on the Korean Rare Disease Knowledge base (KRDK). Results As a result, we have developed a Web-based knowledge base, KRDK, suitable for study of Mendelian diseases that commonly occur among Koreans. This knowledge base is comprised of disease summary and review, causal gene list, laboratory and clinic directory, patient registry, and so on. Furthermore, database for analyzing and giving access to human biological information and the clinical trial management system are integrated on KRDK. Conclusions We expect that KRDK, the first rare disease knowledge base in Korea, may contribute to collaborative research and be a reliable reference for application to clinical trials. Additionally, this knowledge base is ready for querying of drug information so that visitors can search a list of rare diseases that is relative to specific drugs. Visitors can have access to KRDK via http://www.snubi.org/software/raredisease/. PMID:23346478

Towards Modeling False Memory With Computational Knowledge Bases.

PubMed

Li, Justin; Kohanyi, Emma

2017-01-01

One challenge to creating realistic cognitive models of memory is the inability to account for the vast common-sense knowledge of human participants. Large computational knowledge bases such as WordNet and DBpedia may offer a solution to this problem but may pose other challenges. This paper explores some of these difficulties through a semantic network spreading activation model of the Deese-Roediger-McDermott false memory task. In three experiments, we show that these knowledge bases only capture a subset of human associations, while irrelevant information introduces noise and makes efficient modeling difficult. We conclude that the contents of these knowledge bases must be augmented and, more important, that the algorithms must be refined and optimized, before large knowledge bases can be widely used for cognitive modeling. Copyright © 2016 Cognitive Science Society, Inc.

A generator for unique quantum random numbers based on vacuum states

NASA Astrophysics Data System (ADS)

Gabriel, Christian; Wittmann, Christoffer; Sych, Denis; Dong, Ruifang; Mauerer, Wolfgang; Andersen, Ulrik L.; Marquardt, Christoph; Leuchs, Gerd

2010-10-01

Random numbers are a valuable component in diverse applications that range from simulations over gambling to cryptography. The quest for true randomness in these applications has engendered a large variety of different proposals for producing random numbers based on the foundational unpredictability of quantum mechanics. However, most approaches do not consider that a potential adversary could have knowledge about the generated numbers, so the numbers are not verifiably random and unique. Here we present a simple experimental setup based on homodyne measurements that uses the purity of a continuous-variable quantum vacuum state to generate unique random numbers. We use the intrinsic randomness in measuring the quadratures of a mode in the lowest energy vacuum state, which cannot be correlated to any other state. The simplicity of our source, combined with its verifiably unique randomness, are important attributes for achieving high-reliability, high-speed and low-cost quantum random number generators.

Knowledge-based environment for optical system design

NASA Astrophysics Data System (ADS)

Johnson, R. Barry

1991-01-01

Optical systems are extensively utilized by industry government and military organizations. The conceptual design engineering design fabrication and testing of these systems presently requires significant time typically on the order of 3-5 years. The Knowledge-Based Environment for Optical System Design (KB-OSD) Program has as its principal objectives the development of a methodology and tool(s) that will make a notable reduction in the development time of optical system projects reduce technical risk and overall cost. KB-OSD can be considered as a computer-based optical design associate for system engineers and design engineers. By utilizing artificial intelligence technology coupled with extensive design/evaluation computer application programs and knowledge bases the KB-OSD will provide the user with assistance and guidance to accomplish such activities as (i) develop system level and hardware level requirements from mission requirements (ii) formulate conceptual designs (iii) construct a statement of work for an RFP (iv) develop engineering level designs (v) evaluate an existing design and (vi) explore the sensitivity of a system to changing scenarios. The KB-OSD comprises a variety of computer platforms including a Stardent Titan supercomputer numerous design programs (lens design coating design thermal materials structural atmospherics etc. ) data bases and heuristic knowledge bases. An important element of the KB-OSD Program is the inclusion of the knowledge of individual experts in various areas of optics and optical system engineering. This knowledge is obtained by KB-OSD knowledge engineers performing

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

Generational Differences in Knowledge Markets

DTIC Science & Technology

2010-03-01

and Generation X generations. Following Generation X, Generation Y , or the Millennial Generation, includes those born between 1979 and 1994. The...positions but their numbers are small—approximately half the Baby Boomer population—and they’ll be leading Generation Y which is three times their size...boom” resulted in the 98.8 million-strong Generation Y (Sincavage, 2004). The resulting unevenness of the population distribution by age in the

Knowledge-based commodity distribution planning

NASA Technical Reports Server (NTRS)

Saks, Victor; Johnson, Ivan

1994-01-01

This paper presents an overview of a Decision Support System (DSS) that incorporates Knowledge-Based (KB) and commercial off the shelf (COTS) technology components. The Knowledge-Based Logistics Planning Shell (KBLPS) is a state-of-the-art DSS with an interactive map-oriented graphics user interface and powerful underlying planning algorithms. KBLPS was designed and implemented to support skilled Army logisticians to prepare and evaluate logistics plans rapidly, in order to support corps-level battle scenarios. KBLPS represents a substantial advance in graphical interactive planning tools, with the inclusion of intelligent planning algorithms that provide a powerful adjunct to the planning skills of commodity distribution planners.

Terminological reference of a knowledge-based system: the data dictionary.

PubMed

Stausberg, J; Wormek, A; Kraut, U

1995-01-01

The development of open and integrated knowledge bases makes new demands on the definition of the used terminology. The definition should be realized in a data dictionary separated from the knowledge base. Within the works done at a reference model of medical knowledge, a data dictionary has been developed and used in different applications: a term definition shell, a documentation tool and a knowledge base. The data dictionary includes that part of terminology, which is largely independent of a certain knowledge model. For that reason, the data dictionary can be used as a basis for integrating knowledge bases into information systems, for knowledge sharing and reuse and for modular development of knowledge-based systems.

Practice to Evidence: Using Evaluability Assessment to Generate Practice-Based Evidence in Rural South Georgia

ERIC Educational Resources Information Center

Honeycutt, Sally; Hermstad, April; Carvalho, Michelle L.; Arriola, Kimberly R. Jacob; Ballard, Denise; Escoffery, Cam; Kegler, Michelle C.

2017-01-01

Evidence from formal evaluation of real-world practice can address gaps in the public health knowledge base and provide information about feasible, relevant strategies for varied settings. Interest in evaluability assessment (EA) as an approach for generating practice-based evidence has grown. EA has been central to several structured assessment…

Construction of dynamic stochastic simulation models using knowledge-based techniques

NASA Technical Reports Server (NTRS)

Williams, M. Douglas; Shiva, Sajjan G.

1990-01-01

Over the past three decades, computer-based simulation models have proven themselves to be cost-effective alternatives to the more structured deterministic methods of systems analysis. During this time, many techniques, tools and languages for constructing computer-based simulation models have been developed. More recently, advances in knowledge-based system technology have led many researchers to note the similarities between knowledge-based programming and simulation technologies and to investigate the potential application of knowledge-based programming techniques to simulation modeling. The integration of conventional simulation techniques with knowledge-based programming techniques is discussed to provide a development environment for constructing knowledge-based simulation models. A comparison of the techniques used in the construction of dynamic stochastic simulation models and those used in the construction of knowledge-based systems provides the requirements for the environment. This leads to the design and implementation of a knowledge-based simulation development environment. These techniques were used in the construction of several knowledge-based simulation models including the Advanced Launch System Model (ALSYM).

Knowledge-based scheduling of arrival aircraft

NASA Technical Reports Server (NTRS)

Krzeczowski, K.; Davis, T.; Erzberger, H.; Lev-Ram, I.; Bergh, C.

1995-01-01

A knowledge-based method for scheduling arrival aircraft in the terminal area has been implemented and tested in real-time simulation. The scheduling system automatically sequences, assigns landing times, and assigns runways to arrival aircraft by utilizing continuous updates of aircraft radar data and controller inputs. The scheduling algorithms is driven by a knowledge base which was obtained in over two thousand hours of controller-in-the-loop real-time simulation. The knowledge base contains a series of hierarchical 'rules' and decision logic that examines both performance criteria, such as delay reduction, as well as workload reduction criteria, such as conflict avoidance. The objective of the algorithms is to devise an efficient plan to land the aircraft in a manner acceptable to the air traffic controllers. This paper will describe the scheduling algorithms, give examples of their use, and present data regarding their potential benefits to the air traffic system.

Reuse: A knowledge-based approach

NASA Technical Reports Server (NTRS)

Iscoe, Neil; Liu, Zheng-Yang; Feng, Guohui

1992-01-01

This paper describes our research in automating the reuse process through the use of application domain models. Application domain models are explicit formal representations of the application knowledge necessary to understand, specify, and generate application programs. Furthermore, they provide a unified repository for the operational structure, rules, policies, and constraints of a specific application area. In our approach, domain models are expressed in terms of a transaction-based meta-modeling language. This paper has described in detail the creation and maintenance of hierarchical structures. These structures are created through a process that includes reverse engineering of data models with supplementary enhancement from application experts. Source code is also reverse engineered but is not a major source of domain model instantiation at this time. In the second phase of the software synthesis process, program specifications are interactively synthesized from an instantiated domain model. These specifications are currently integrated into a manual programming process but will eventually be used to derive executable code with mechanically assisted transformations. This research is performed within the context of programming-in-the-large types of systems. Although our goals are ambitious, we are implementing the synthesis system in an incremental manner through which we can realize tangible results. The client/server architecture is capable of supporting 16 simultaneous X/Motif users and tens of thousands of attributes and classes. Domain models have been partially synthesized from five different application areas. As additional domain models are synthesized and additional knowledge is gathered, we will inevitably add to and modify our representation. However, our current experience indicates that it will scale and expand to meet our modeling needs.

Using affective knowledge to generate and validate a set of emotion-related, action words

PubMed Central

Havelka, Jelena; Brown, Charity; Giner-Sorolla, Roger

2015-01-01

Emotion concepts are built through situated experience. Abstract word meaning is grounded in this affective knowledge, giving words the potential to evoke emotional feelings and reactions (e.g., Vigliocco et al., 2009). In the present work we explore whether words differ in the extent to which they evoke ‘specific’ emotional knowledge. Using a categorical approach, in which an affective ‘context’ is created, it is possible to assess whether words proportionally activate knowledge relevant to different emotional states (e.g., ‘sadness’, ‘anger’, Stevenson, Mikels & James, 2007a). We argue that this method may be particularly effective when assessing the emotional meaning of action words (e.g., Schacht & Sommer, 2009). In study 1 we use a constrained feature generation task to derive a set of action words that participants associated with six, basic emotional states (see full list in Appendix S1). Generation frequencies were taken to indicate the likelihood that the word would evoke emotional knowledge relevant to the state to which it had been paired. In study 2 a rating task was used to assess the strength of association between the six most frequently generated, or ‘typical’, action words and corresponding emotion labels. Participants were presented with a series of sentences, in which action words (typical and atypical) and labels were paired e.g., “If you are feeling ‘sad’ how likely would you be to act in the following way?” … ‘cry.’ Findings suggest that typical associations were robust. Participants always gave higher ratings to typical vs. atypical action word and label pairings, even when (a) rating direction was manipulated (the label or verb appeared first in the sentence), and (b) the typical behaviours were to be performed by the rater themselves, or others. Our findings suggest that emotion-related action words vary in the extent to which they evoke knowledge relevant for different emotional states. When measuring

The generation of simple compliance boundaries for mobile communication base station antennas using formulae for SAR estimation.

PubMed

Thors, B; Hansson, B; Törnevik, C

2009-07-07

In this paper, a procedure is proposed for generating simple and practical compliance boundaries for mobile communication base station antennas. The procedure is based on a set of formulae for estimating the specific absorption rate (SAR) in certain directions around a class of common base station antennas. The formulae, given for both whole-body and localized SAR, require as input the frequency, the transmitted power and knowledge of antenna-related parameters such as dimensions, directivity and half-power beamwidths. With knowledge of the SAR in three key directions it is demonstrated how simple and practical compliance boundaries can be generated outside of which the exposure levels do not exceed certain limit values. The conservativeness of the proposed procedure is discussed based on results from numerical radio frequency (RF) exposure simulations with human body phantoms from the recently developed Virtual Family.

From science to action: Principles for undertaking environmental research that enables knowledge exchange and evidence-based decision-making.

PubMed

Cvitanovic, C; McDonald, J; Hobday, A J

2016-12-01

Effective conservation requires knowledge exchange among scientists and decision-makers to enable learning and support evidence-based decision-making. Efforts to improve knowledge exchange have been hindered by a paucity of empirically-grounded guidance to help scientists and practitioners design and implement research programs that actively facilitate knowledge exchange. To address this, we evaluated the Ningaloo Research Program (NRP), which was designed to generate new scientific knowledge to support evidence-based decisions about the management of the Ningaloo Marine Park in north-western Australia. Specifically, we evaluated (1) outcomes of the NRP, including the extent to which new knowledge informed management decisions; (2) the barriers that prevented knowledge exchange among scientists and managers; (3) the key requirements for improving knowledge exchange processes in the future; and (4) the core capacities that are required to support knowledge exchange processes. While the NRP generated expansive and multidisciplinary science outputs directly relevant to the management of the Ningaloo Marine Park, decision-makers are largely unaware of this knowledge and little has been integrated into decision-making processes. A range of barriers prevented efficient and effective knowledge exchange among scientists and decision-makers including cultural differences among the groups, institutional barriers within decision-making agencies, scientific outputs that were not translated for decision-makers and poor alignment between research design and actual knowledge needs. We identify a set of principles to be implemented routinely as part of any applied research program, including; (i) stakeholder mapping prior to the commencement of research programs to identify all stakeholders, (ii) research questions to be co-developed with stakeholders, (iii) implementation of participatory research approaches, (iv) use of a knowledge broker, and (v) tailored knowledge management

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.

Towards a New Generation of Agricultural System Data, Models and Knowledge Products: Design and Improvement

NASA Technical Reports Server (NTRS)

Antle, John M.; Basso, Bruno; Conant, Richard T.; Godfray, H. Charles J.; Jones, James W.; Herrero, Mario; Howitt, Richard E.; Keating, Brian A.; Munoz-Carpena, Rafael; Rosenzweig, Cynthia

2016-01-01

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.

Towards a new generation of agricultural system data, models and knowledge products: Design and improvement.

PubMed

Antle, John M; Basso, Bruno; Conant, Richard T; Godfray, H Charles J; Jones, James W; Herrero, Mario; Howitt, Richard E; Keating, Brian A; Munoz-Carpena, Rafael; Rosenzweig, Cynthia; Tittonell, Pablo; Wheeler, Tim R

2017-07-01

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.

Analyzing Data Generated Through Deliberative Dialogue: Bringing Knowledge Translation Into Qualitative Analysis.

PubMed

Plamondon, Katrina M; Bottorff, Joan L; Cole, Donald C

2015-11-01

Deliberative dialogue (DD) is a knowledge translation strategy that can serve to generate rich data and bridge health research with action. An intriguing alternative to other modes of generating data, the purposeful and evidence-informed conversations characteristic of DD generate data inclusive of collective interpretations. These data are thus dialogic, presenting complex challenges for qualitative analysis. In this article, we discuss the nature of data generated through DD, orienting ourselves toward a theoretically grounded approach to analysis. We offer an integrated framework for analysis, balancing analytical strategies of categorizing and connecting with the use of empathetic and suspicious interpretive lenses. In this framework, data generation and analysis occur in concert, alongside engaging participants and synthesizing evidence. An example of application is provided, demonstrating nuances of the framework. We conclude with reflections on the strengths and limitations of the framework, suggesting how it may be relevant in other qualitative health approaches. © The Author(s) 2015.

Knowledge-Based Information Retrieval.

ERIC Educational Resources Information Center

Ford, Nigel

1991-01-01

Discussion of information retrieval focuses on theoretical and empirical advances in knowledge-based information retrieval. Topics discussed include the use of natural language for queries; the use of expert systems; intelligent tutoring systems; user modeling; the need for evaluation of system effectiveness; and examples of systems, including…

Proposed Conceptual Requirements for the CTBT Knowledge Base,

DTIC Science & Technology

1995-08-14

knowledge available to automated processing routines and human analysts are significant, and solving these problems is an essential step in ensuring...knowledge storage in a CTBT system. In addition to providing regional knowledge to automated processing routines, the knowledge base will also address

How To Manage the Emerging Generational Divide in the Contemporary Knowledge-Rich Workplace.

ERIC Educational Resources Information Center

Novicevic, Milorad M.; Buckley, M. Ronald

2001-01-01

Addresses the manager's dilemmas and options in resolving emerging latent intergenerational conflict in the contemporary knowledge-rich workplace. Topics include a theoretical framework for generational divide management; the polarization in task requirements; social and environmental factors; differences in employee needs and expectations; and…

SAFOD Brittle Microstructure and Mechanics Knowledge Base (SAFOD BM2KB)

NASA Astrophysics Data System (ADS)

Babaie, H. A.; Hadizadeh, J.; di Toro, G.; Mair, K.; Kumar, A.

2008-12-01

We have developed a knowledge base to store and present the data collected by a group of investigators studying the microstructures and mechanics of brittle faulting using core samples from the SAFOD (San Andreas Fault Observatory at Depth) project. The investigations are carried out with a variety of analytical and experimental methods primarily to better understand the physics of strain localization in fault gouge. The knowledge base instantiates an specially-designed brittle rock deformation ontology developed at Georgia State University. The inference rules embedded in the semantic web languages, such as OWL, RDF, and RDFS, which are used in our ontology, allow the Pellet reasoner used in this application to derive additional truths about the ontology and knowledge of this domain. Access to the knowledge base is via a public website, which is designed to provide the knowledge acquired by all the investigators involved in the project. The stored data will be products of studies such as: experiments (e.g., high-velocity friction experiment), analyses (e.g., microstructural, chemical, mass transfer, mineralogical, surface, image, texture), microscopy (optical, HRSEM, FESEM, HRTEM]), tomography, porosity measurement, microprobe, and cathodoluminesence. Data about laboratories, experimental conditions, methods, assumptions, equipments, and mechanical properties and lithology of the studied samples will also be presented on the website per investigation. The ontology was modeled applying the UML (Unified Modeling Language) in Rational Rose, and implemented in OWL-DL (Ontology Web Language) using the Protégé ontology editor. The UML model was converted to OWL-DL by first mapping it to Ecore (.ecore) and Generator model (.genmodel) with the help of the EMF (Eclipse Modeling Framework) plugin in Eclipse. The Ecore model was then mapped to a .uml file, which later was converted into an .owl file and subsequently imported into the Protégé ontology editing environment

Towards organizing health knowledge on community-based health services.

PubMed

Akbari, Mohammad; Hu, Xia; Nie, Liqiang; Chua, Tat-Seng

2016-12-01

Online community-based health services accumulate a huge amount of unstructured health question answering (QA) records at a continuously increasing pace. The ability to organize these health QA records has been found to be effective for data access. The existing approaches for organizing information are often not applicable to health domain due to its domain nature as characterized by complex relation among entities, large vocabulary gap, and heterogeneity of users. To tackle these challenges, we propose a top-down organization scheme, which can automatically assign the unstructured health-related records into a hierarchy with prior domain knowledge. Besides automatic hierarchy prototype generation, it also enables each data instance to be associated with multiple leaf nodes and profiles each node with terminologies. Based on this scheme, we design a hierarchy-based health information retrieval system. Experiments on a real-world dataset demonstrate the effectiveness of our scheme in organizing health QA into a topic hierarchy and retrieving health QA records from the topic hierarchy.

KBGIS-II: A knowledge-based geographic information system

NASA Technical Reports Server (NTRS)

Smith, Terence; Peuquet, Donna; Menon, Sudhakar; Agarwal, Pankaj

1986-01-01

The architecture and working of a recently implemented Knowledge-Based Geographic Information System (KBGIS-II), designed to satisfy several general criteria for the GIS, is described. The system has four major functions including query-answering, learning and editing. The main query finds constrained locations for spatial objects that are describable in a predicate-calculus based spatial object language. The main search procedures include a family of constraint-satisfaction procedures that use a spatial object knowledge base to search efficiently for complex spatial objects in large, multilayered spatial data bases. These data bases are represented in quadtree form. The search strategy is designed to reduce the computational cost of search in the average case. The learning capabilities of the system include the addition of new locations of complex spatial objects to the knowledge base as queries are answered, and the ability to learn inductively definitions of new spatial objects from examples. The new definitions are added to the knowledge base by the system. The system is performing all its designated tasks successfully. Future reports will relate performance characteristics of the system.

Systems, methods and apparatus for verification of knowledge-based systems

NASA Technical Reports Server (NTRS)

Rash, James L. (Inventor); Gracinin, Denis (Inventor); Erickson, John D. (Inventor); Rouff, Christopher A. (Inventor); Hinchey, Michael G. (Inventor)

2010-01-01

Systems, methods and apparatus are provided through which in some embodiments, domain knowledge is translated into a knowledge-based system. In some embodiments, a formal specification is derived from rules of a knowledge-based system, the formal specification is analyzed, and flaws in the formal specification are used to identify and correct errors in the domain knowledge, from which a knowledge-based system is translated.

Micromotor-based energy generation.

PubMed

Singh, Virendra V; Soto, Fernando; Kaufmann, Kevin; Wang, Joseph

2015-06-01

A micromotor-based strategy for energy generation, utilizing the conversion of liquid-phase hydrogen to usable hydrogen gas (H2), is described. The new motion-based H2-generation concept relies on the movement of Pt-black/Ti Janus microparticle motors in a solution of sodium borohydride (NaBH4) fuel. This is the first report of using NaBH4 for powering micromotors. The autonomous motion of these catalytic micromotors, as well as their bubble generation, leads to enhanced mixing and transport of NaBH4 towards the Pt-black catalytic surface (compared to static microparticles or films), and hence to a substantially faster rate of H2 production. The practical utility of these micromotors is illustrated by powering a hydrogen-oxygen fuel cell car by an on-board motion-based hydrogen and oxygen generation. The new micromotor approach paves the way for the development of efficient on-site energy generation for powering external devices or meeting growing demands on the energy grid. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A novel knowledge-based potential for RNA 3D structure evaluation

NASA Astrophysics Data System (ADS)

Yang, Yi; Gu, Qi; Zhang, Ben-Gong; Shi, Ya-Zhou; Shao, Zhi-Gang

2018-03-01

Ribonucleic acids (RNAs) play a vital role in biology, and knowledge of their three-dimensional (3D) structure is required to understand their biological functions. Recently structural prediction methods have been developed to address this issue, but a series of RNA 3D structures are generally predicted by most existing methods. Therefore, the evaluation of the predicted structures is generally indispensable. Although several methods have been proposed to assess RNA 3D structures, the existing methods are not precise enough. In this work, a new all-atom knowledge-based potential is developed for more accurately evaluating RNA 3D structures. The potential not only includes local and nonlocal interactions but also fully considers the specificity of each RNA by introducing a retraining mechanism. Based on extensive test sets generated from independent methods, the proposed potential correctly distinguished the native state and ranked near-native conformations to effectively select the best. Furthermore, the proposed potential precisely captured RNA structural features such as base-stacking and base-pairing. Comparisons with existing potential methods show that the proposed potential is very reliable and accurate in RNA 3D structure evaluation. Project supported by the National Science Foundation of China (Grants Nos. 11605125, 11105054, 11274124, and 11401448).

E-pharmacovigilance: development and implementation of a computable knowledge base to identify adverse drug reactions.

PubMed

Neubert, Antje; Dormann, Harald; Prokosch, Hans-Ulrich; Bürkle, Thomas; Rascher, Wolfgang; Sojer, Reinhold; Brune, Kay; Criegee-Rieck, Manfred

2013-09-01

Computer-assisted signal generation is an important issue for the prevention of adverse drug reactions (ADRs). However, due to poor standardization of patients' medical data and a lack of computable medical drug knowledge the specificity of computerized decision support systems for early ADR detection is too low and thus those systems are not yet implemented in daily clinical practice. We report on a method to formalize knowledge about ADRs based on the Summary of Product Characteristics (SmPCs) and linking them with structured patient data to generate safety signals automatically and with high sensitivity and specificity. A computable ADR knowledge base (ADR-KB) that inherently contains standardized concepts for ADRs (WHO-ART), drugs (ATC) and laboratory test results (LOINC) was built. The system was evaluated in study populations of paediatric and internal medicine inpatients. A total of 262 different ADR concepts related to laboratory findings were linked to 212 LOINC terms. The ADR knowledge base was retrospectively applied to a study population of 970 admissions (474 internal and 496 paediatric patients), who underwent intensive ADR surveillance. The specificity increased from 7% without ADR-KB up to 73% in internal patients and from 19.6% up to 91% in paediatric inpatients, respectively. This study shows that contextual linkage of patients' medication data with laboratory test results is a useful and reasonable instrument for computer-assisted ADR detection and a valuable step towards a systematic drug safety process. The system enables automated detection of ADRs during clinical practice with a quality close to intensive chart review. © 2013 The Authors. British Journal of Clinical Pharmacology © 2013 The British Pharmacological Society.

Effects of delays on 6-year-old children’s self-generation and retention of knowledge through integration

PubMed Central

Varga, Nicole L.; Bauer, Patricia J.

2013-01-01

The present research was an investigation of the effect of delay on self-generation and retention of knowledge derived through integration by 6-year-old children. Children were presented with novel facts from passages read aloud to them (stem facts) and tested for self-generation of new knowledge through integration of the facts. In Experiment 1, children integrated the stem facts at Session 1 and retained the self-generated memory traces over 1 week. In Experiment 2, 1-week delays were imposed either between the to-be-integrated facts (between-stem delay) or after the stem facts but before the test (before-test delay). Integration performance was diminished in both conditions. Moreover, memory for individual stem facts was lower in Experiment 2 than in Experiment 1, suggesting that self-generation through integration promoted memory for explicitly taught information. The results indicate the importance of tests for promoting self-generation through integration as well as for retaining newly self-generated and explicitly taught information. PMID:23563162

Knowledge Based Text Generation

DTIC Science & Technology

1989-08-01

Number 4, October-December, 1985, pp. 219-242. de Joia , A. and Stenton, A., Terms in Linguistics: A Guide to Halliday, London: Batsford Academic and...extraction of text schemata and their corresponding rhetorical predicates; design of a system motivated by the desire for domain and language independence...semantics and semantics effects syntax. Functional Linguistic Framework Page 19 The design of GENNY was guided by the functional paradigm. Provided a

Enormous knowledge base of disease diagnosis criteria.

PubMed

Xiao, Z H; Xiao, Y H; Pei, J H

1995-01-01

One of the problems in the development of the medical knowledge systems is the limitations of the system's knowledge. It is a common expectation to increase the number of diseases contained in a system. Using a high density knowledge representation method designed by us, we have developed the Enormous Knowledge Base of Disease Diagnosis Criteria (EKBDDC). It contains diagnostic criteria of 1,001 diagnostic entities and describes nearly 4,000 items of diagnostic indicators. It is the core of a huge medical project--the Electronic-Brain Medical Erudite (EBME). This enormous knowledge base was implemented initially on a low-cost popular microcomputer, which can aid in the prompting of typical disease and in teaching of diagnosis. The knowledge base is easy to expand. One of the main goals of EKBDDC is to increase the number of diseases included in it as far as possible using a low-cost computer with a comparatively small storage capacity. For this, we have designed a high density knowledge representation method. Criteria of various diagnostic entities are respectively stored in different records of the knowledge base. Each diagnostic entity corresponds to a diagnostic criterion data set; each data set consists of some diagnostic criterion data values (Table 1); each data is composed of two parts: integer and decimal; the integral part is the coding number of the given diagnostic information, and the decimal part is the diagnostic value of this information to the disease indicated by corresponding record number. For example, 75.02: the integer 75 is the coding number of "hemorrhagic skin rash"; the decimal 0.02 is the diagnostic value of this manifestation for diagnosing allergic purpura. TABULAR DATA, SEE PUBLISHED ABSTRACT. The algebraic sum method, a special form of the weighted summation, is adopted as mathematical model. In EKBDDC, the diagnostic values, which represent the significance of the disease manifestations for diagnosing corresponding diseases, were

Realizing Relevance: The Influence of Domain-Specific Information on Generation of New Knowledge through Integration in 4- to 8-Year-Old Children

ERIC Educational Resources Information Center

Bauer, Patricia J.; Larkina, Marina

2017-01-01

In accumulating knowledge, direct modes of learning are complemented by productive processes, including self-generation based on integration of separate episodes. Effects of the number of potentially relevant episodes on integration were examined in 4- to 8-year-olds (N = 121; racially/ethnically heterogeneous sample, English speakers, from large…

Bridging the gap: simulations meet knowledge bases

NASA Astrophysics Data System (ADS)

King, Gary W.; Morrison, Clayton T.; Westbrook, David L.; Cohen, Paul R.

2003-09-01

Tapir and Krill are declarative languages for specifying actions and agents, respectively, that can be executed in simulation. As such, they bridge the gap between strictly declarative knowledge bases and strictly executable code. Tapir and Krill components can be combined to produce models of activity which can answer questions about mechanisms and processes using conventional inference methods and simulation. Tapir was used in DARPA's Rapid Knowledge Formation (RKF) project to construct models of military tactics from the Army Field Manual FM3-90. These were then used to build Courses of Actions (COAs) which could be critiqued by declarative reasoning or via Monte Carlo simulation. Tapir and Krill can be read and written by non-knowledge engineers making it an excellent vehicle for Subject Matter Experts to build and critique knowledge bases.

Biotechnology as the engine for the Knowledge-Based Bio-Economy.

PubMed

Aguilar, Alfredo; Bochereau, Laurent; Matthiessen, Line

2010-01-01

The European Commission has defined the Knowledge-Based Bio-Economy (KBBE) as the process of transforming life science knowledge into new, sustainable, eco-efficient and competitive products. The term "Bio-Economy" encompasses all industries and economic sectors that produce, manage and otherwise exploit biological resources and related services. Over the last decades biotechnologies have led to innovations in many agricultural, industrial, medical sectors and societal activities. Biotechnology will continue to be a major contributor to the Bio-Economy, playing an essential role in support of economic growth, employment, energy supply and a new generation of bio-products, and to maintain the standard of living. The paper reviews some of the main biotechnology-related research activities at European level. Beyond the 7th Framework Program for Research and Technological Development (FP7), several initiatives have been launched to better integrate FP7 with European national research activities, promote public-private partnerships and create better market and regulatory environments for stimulating innovation.

Knowledge-based nonuniform sampling in multidimensional NMR.

PubMed

Schuyler, Adam D; Maciejewski, Mark W; Arthanari, Haribabu; Hoch, Jeffrey C

2011-07-01

The full resolution afforded by high-field magnets is rarely realized in the indirect dimensions of multidimensional NMR experiments because of the time cost of uniformly sampling to long evolution times. Emerging methods utilizing nonuniform sampling (NUS) enable high resolution along indirect dimensions by sampling long evolution times without sampling at every multiple of the Nyquist sampling interval. While the earliest NUS approaches matched the decay of sampling density to the decay of the signal envelope, recent approaches based on coupled evolution times attempt to optimize sampling by choosing projection angles that increase the likelihood of resolving closely-spaced resonances. These approaches employ knowledge about chemical shifts to predict optimal projection angles, whereas prior applications of tailored sampling employed only knowledge of the decay rate. In this work we adapt the matched filter approach as a general strategy for knowledge-based nonuniform sampling that can exploit prior knowledge about chemical shifts and is not restricted to sampling projections. Based on several measures of performance, we find that exponentially weighted random sampling (envelope matched sampling) performs better than shift-based sampling (beat matched sampling). While shift-based sampling can yield small advantages in sensitivity, the gains are generally outweighed by diminished robustness. Our observation that more robust sampling schemes are only slightly less sensitive than schemes highly optimized using prior knowledge about chemical shifts has broad implications for any multidimensional NMR study employing NUS. The results derived from simulated data are demonstrated with a sample application to PfPMT, the phosphoethanolamine methyltransferase of the human malaria parasite Plasmodium falciparum.

New knowledge-based genetic algorithm for excavator boom structural optimization

NASA Astrophysics Data System (ADS)

Hua, Haiyan; Lin, Shuwen

2014-03-01

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Generating Knowledge From Field Experience: Two Competing Conclusions About Potential Teacher Center Support

ERIC Educational Resources Information Center

Fox, G. Thomas, Jr.

1978-01-01

Suggests that there exist great potential and much professional support for practitioner involvement (teachers, students, and administrators) in generating knowledge (creating new understandings of what is occurring in our experience and why) from field experience and that the probable consequences noted when practitioners become involved in…

Translating knowledge into practice: An exploratory study of dementia-specific training for community-based service providers.

PubMed

O'Sullivan, Grace; Hocking, Clare; McPherson, Kathryn

2017-08-01

Objective To develop, deliver, and evaluate dementia-specific training designed to inform service delivery by enhancing the knowledge of community-based service providers. Methods This exploratory qualitative study used an interdisciplinary, interuniversity team approach to develop and deliver dementia-specific training. Participants included management, care staff, and clients from three organizations funded to provide services in the community. Data on the acceptability, applicability, and perceived outcomes of the training were gathered through focus group discussions and individual interviews. Transcripts were analyzed to generate open codes which were clustered into themes and sub-themes addressing the content, delivery, and value of the training. Findings Staff valued up-to-date knowledge and "real stories" grounded in practice. Clients welcomed the strengths-based approach. Contractual obligations impact on the application of knowledge in practice. Implications The capacity to implement new knowledge may be limited by the legislative policies which frame service provision, to the detriment of service users.

Monitoring Knowledge Base (MKB)

EPA Pesticide Factsheets

The Monitoring Knowledge Base (MKB) is a compilation of emissions measurement and monitoring techniques associated with air pollution control devices, industrial process descriptions, and permitting techniques, including flexible permit development. Using MKB, one can gain a comprehensive understanding of emissions sources, control devices, and monitoring techniques, enabling one to determine appropriate permit terms and conditions.

The Role of Domain Knowledge in Creative Generation

ERIC Educational Resources Information Center

Ward, Thomas B.

2008-01-01

Previous studies have shown that a predominant tendency in creative generation tasks is to base new ideas on well-known, specific instances of previous ideas (e.g., basing ideas for imaginary aliens on dogs, cats or bears). However, a substantial minority of individuals has been shown to adopt more abstract approaches to the task and to develop…

A standard based approach for biomedical knowledge representation.

PubMed

Farkash, Ariel; Neuvirth, Hani; Goldschmidt, Yaara; Conti, Costanza; Rizzi, Federica; Bianchi, Stefano; Salvi, Erika; Cusi, Daniele; Shabo, Amnon

2011-01-01

The new generation of health information standards, where the syntax and semantics of the content is explicitly formalized, allows for interoperability in healthcare scenarios and analysis in clinical research settings. Studies involving clinical and genomic data include accumulating knowledge as relationships between genotypic and phenotypic information as well as associations within the genomic and clinical worlds. Some involve analysis results targeted at a specific disease; others are of a predictive nature specific to a patient and may be used by decision support applications. Representing knowledge is as important as representing data since data is more useful when coupled with relevant knowledge. Any further analysis and cross-research collaboration would benefit from persisting knowledge and data in a unified way. This paper describes a methodology used in Hypergenes, an EC FP7 project targeting Essential Hypertension, which captures data and knowledge using standards such as HL7 CDA and Clinical Genomics, aligned with the CEN EHR 13606 specification. We demonstrate the benefits of such an approach for clinical research as well as in healthcare oriented scenarios.

Knowledge-based graphical interfaces for presenting technical information

NASA Technical Reports Server (NTRS)

Feiner, Steven

1988-01-01

Designing effective presentations of technical information is extremely difficult and time-consuming. Moreover, the combination of increasing task complexity and declining job skills makes the need for high-quality technical presentations especially urgent. We believe that this need can ultimately be met through the development of knowledge-based graphical interfaces that can design and present technical information. Since much material is most naturally communicated through pictures, our work has stressed the importance of well-designed graphics, concentrating on generating pictures and laying out displays containing them. We describe APEX, a testbed picture generation system that creates sequences of pictures that depict the performance of simple actions in a world of 3D objects. Our system supports rules for determining automatically the objects to be shown in a picture, the style and level of detail with which they should be rendered, the method by which the action itself should be indicated, and the picture's camera specification. We then describe work on GRIDS, an experimental display layout system that addresses some of the problems in designing displays containing these pictures, determining the position and size of the material to be presented.

Data Mining in Finance: Using Counterfactuals To Generate Knowledge from Organizational Information Systems.

ERIC Educational Resources Information Center

Dhar, Vasant

1998-01-01

Shows how counterfactuals and machine learning methods can be used to guide exploration of large databases that addresses some of the fundamental problems that organizations face in learning from data. Discusses data mining, particularly in the financial arena; generating useful knowledge from data; and the evaluation of counterfactuals. (LRW)

The research on construction and application of machining process knowledge base

NASA Astrophysics Data System (ADS)

Zhao, Tan; Qiao, Lihong; Qie, Yifan; Guo, Kai

2018-03-01

In order to realize the application of knowledge in machining process design, from the perspective of knowledge in the application of computer aided process planning(CAPP), a hierarchical structure of knowledge classification is established according to the characteristics of mechanical engineering field. The expression of machining process knowledge is structured by means of production rules and the object-oriented methods. Three kinds of knowledge base models are constructed according to the representation of machining process knowledge. In this paper, the definition and classification of machining process knowledge, knowledge model, and the application flow of the process design based on the knowledge base are given, and the main steps of the design decision of the machine tool are carried out as an application by using the knowledge base.

Enhancing acronym/abbreviation knowledge bases with semantic information.

PubMed

Torii, Manabu; Liu, Hongfang

2007-10-11

In the biomedical domain, a terminology knowledge base that associates acronyms/abbreviations (denoted as SFs) with the definitions (denoted as LFs) is highly needed. For the construction such terminology knowledge base, we investigate the feasibility to build a system automatically assigning semantic categories to LFs extracted from text. Given a collection of pairs (SF,LF) derived from text, we i) assess the coverage of LFs and pairs (SF,LF) in the UMLS and justify the need of a semantic category assignment system; and ii) automatically derive name phrases annotated with semantic category and construct a system using machine learning. Utilizing ADAM, an existing collection of (SF,LF) pairs extracted from MEDLINE, our system achieved an f-measure of 87% when assigning eight UMLS-based semantic groups to LFs. The system has been incorporated into a web interface which integrates SF knowledge from multiple SF knowledge bases. Web site: http://gauss.dbb.georgetown.edu/liblab/SFThesurus.

Adults' Autonomic and Subjective Emotional Responses to Infant Vocalizations: The Role of Secure Base Script Knowledge

ERIC Educational Resources Information Center

Groh, Ashley M.; Roisman, Glenn I.

2009-01-01

This article examines the extent to which secure base script knowledge--as reflected in an adult's ability to generate narratives in which attachment-related threats are recognized, competent help is provided, and the problem is resolved--is associated with adults' autonomic and subjective emotional responses to infant distress and nondistress…

Validation of highly reliable, real-time knowledge-based systems

NASA Technical Reports Server (NTRS)

Johnson, Sally C.

1988-01-01

Knowledge-based systems have the potential to greatly increase the capabilities of future aircraft and spacecraft and to significantly reduce support manpower needed for the space station and other space missions. However, a credible validation methodology must be developed before knowledge-based systems can be used for life- or mission-critical applications. Experience with conventional software has shown that the use of good software engineering techniques and static analysis tools can greatly reduce the time needed for testing and simulation of a system. Since exhaustive testing is infeasible, reliability must be built into the software during the design and implementation phases. Unfortunately, many of the software engineering techniques and tools used for conventional software are of little use in the development of knowledge-based systems. Therefore, research at Langley is focused on developing a set of guidelines, methods, and prototype validation tools for building highly reliable, knowledge-based systems. The use of a comprehensive methodology for building highly reliable, knowledge-based systems should significantly decrease the time needed for testing and simulation. A proven record of delivering reliable systems at the beginning of the highly visible testing and simulation phases is crucial to the acceptance of knowledge-based systems in critical applications.

KAT: A Flexible XML-based Knowledge Authoring Environment

PubMed Central

Hulse, Nathan C.; Rocha, Roberto A.; Del Fiol, Guilherme; Bradshaw, Richard L.; Hanna, Timothy P.; Roemer, Lorrie K.

2005-01-01

As part of an enterprise effort to develop new clinical information systems at Intermountain Health Care, the authors have built a knowledge authoring tool that facilitates the development and refinement of medical knowledge content. At present, users of the application can compose order sets and an assortment of other structured clinical knowledge documents based on XML schemas. The flexible nature of the application allows the immediate authoring of new types of documents once an appropriate XML schema and accompanying Web form have been developed and stored in a shared repository. The need for a knowledge acquisition tool stems largely from the desire for medical practitioners to be able to write their own content for use within clinical applications. We hypothesize that medical knowledge content for clinical use can be successfully created and maintained through XML-based document frameworks containing structured and coded knowledge. PMID:15802477

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.

TARGET: Rapid Capture of Process Knowledge

NASA Technical Reports Server (NTRS)

Ortiz, C. J.; Ly, H. V.; Saito, T.; Loftin, R. B.

1993-01-01

TARGET (Task Analysis/Rule Generation Tool) represents a new breed of tool that blends graphical process flow modeling capabilities with the function of a top-down reporting facility. Since NASA personnel frequently perform tasks that are primarily procedural in nature, TARGET models mission or task procedures and generates hierarchical reports as part of the process capture and analysis effort. Historically, capturing knowledge 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 the expert's 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 types of knowledge, procedural knowledge has received relatively little attention. In essence, TARGET is one of the first tools of its kind, commercial or institutional, that is designed to support this type of knowledge capture undertaking. This paper will describe the design and development of TARGET for the acquisition and representation of procedural knowledge. The strategies employed by TARGET to support use by knowledge engineers, subject matter experts, programmers and managers will be discussed. This discussion includes the method by which the tool employs its graphical user interface to generate a task hierarchy report. Next, the approach to generate production rules for incorporation in and development of a CLIPS based expert system will be elaborated. TARGET also permits experts to visually describe procedural tasks as a common medium for knowledge refinement by the expert community and knowledge engineer making knowledge consensus possible. The paper briefly touches on the verification and validation issues facing the CLIPS rule generation aspects of TARGET. A description of

Novel nonlinear knowledge-based mean force potentials based on machine learning.

PubMed

Dong, Qiwen; Zhou, Shuigeng

2011-01-01

The prediction of 3D structures of proteins from amino acid sequences is one of the most challenging problems in molecular biology. An essential task for solving this problem with coarse-grained models is to deduce effective interaction potentials. The development and evaluation of new energy functions is critical to accurately modeling the properties of biological macromolecules. Knowledge-based mean force potentials are derived from statistical analysis of proteins of known structures. Current knowledge-based potentials are almost in the form of weighted linear sum of interaction pairs. In this study, a class of novel nonlinear knowledge-based mean force potentials is presented. The potential parameters are obtained by nonlinear classifiers, instead of relative frequencies of interaction pairs against a reference state or linear classifiers. The support vector machine is used to derive the potential parameters on data sets that contain both native structures and decoy structures. Five knowledge-based mean force Boltzmann-based or linear potentials are introduced and their corresponding nonlinear potentials are implemented. They are the DIH potential (single-body residue-level Boltzmann-based potential), the DFIRE-SCM potential (two-body residue-level Boltzmann-based potential), the FS potential (two-body atom-level Boltzmann-based potential), the HR potential (two-body residue-level linear potential), and the T32S3 potential (two-body atom-level linear potential). Experiments are performed on well-established decoy sets, including the LKF data set, the CASP7 data set, and the Decoys “R”Us data set. The evaluation metrics include the energy Z score and the ability of each potential to discriminate native structures from a set of decoy structures. Experimental results show that all nonlinear potentials significantly outperform the corresponding Boltzmann-based or linear potentials, and the proposed discriminative framework is effective in developing knowledge-based

The effects of question-generation training on metacognitive knowledge, self regulation and learning approaches in science.

PubMed

Cano García, Francisco; García, Ángela; Berbén, A B G; Pichardo, M C; Justicia, Fernando

2014-01-01

Although much research has examined the impact of question generation on students' reading comprehension and learning from lectures, far less research has analysed its influence on how students learn and study science. The present study aims to bridge this knowledge gap. Using a quasi-experimental design, three complete ninth-grade science classes, with a total of 72 students, were randomly assigned to three conditions (groups): (G1) questioning-training by providing prompts; (G2) question-generation without any explicit instruction; and (G3) no question control. Participants' pre-test and post-test self-reported measures of metacognitive knowledge, self-regulation and learning approaches were collected and data analysed with multivariate and univariate analyses of covariance. (a) MANCOVA revealed a significant effect for group; (b) ANCOVAs showed the highest average gains for G1 and statistically significant between-group differences in the two components of metacognition: metacognitive knowledge and self-regulation; and (c) the direction of these differences seemed to vary in each of these components. Question-generation training influenced how students learned and studied, specifically their metacognition, and it had a medium to large effect size, which was somewhat related to the prompts used.

Leveraging Event Reporting Through Knowledge Support: A Knowledge-Based Approach to Promoting Patient Fall Prevention.

PubMed

Yao, Bin; Kang, Hong; Miao, Qi; Zhou, Sicheng; Liang, Chen; Gong, Yang

2017-01-01

Patient falls are a common safety event type that impairs the healthcare quality. Strategies including solution tools and reporting systems for preventing patient falls have been developed and implemented in the U.S. However, the current strategies do not include timely knowledge support, which is in great need in bridging the gap between reporting and learning. In this study, we constructed a knowledge base of fall events by combining expert-reviewed fall prevention solutions and then integrating them into a reporting system. The knowledge base enables timely and tailored knowledge support and thus will serve as a prevailing fall prevention tool. This effort holds promise in making knowledge acquisition and management a routine process for enhancing the reporting and understanding of patient safety events.

A knowledge-based approach to improving and homogenizing intensity modulated radiation therapy planning quality among treatment centers: an example application to prostate cancer planning.

PubMed

Good, David; Lo, Joseph; Lee, W Robert; Wu, Q Jackie; Yin, Fang-Fang; Das, Shiva K

2013-09-01

Intensity modulated radiation therapy (IMRT) treatment planning can have wide variation among different treatment centers. We propose a system to leverage the IMRT planning experience of larger institutions to automatically create high-quality plans for outside clinics. We explore feasibility by generating plans for patient datasets from an outside institution by adapting plans from our institution. A knowledge database was created from 132 IMRT treatment plans for prostate cancer at our institution. The outside institution, a community hospital, provided the datasets for 55 prostate cancer cases, including their original treatment plans. For each "query" case from the outside institution, a similar "match" case was identified in the knowledge database, and the match case's plan parameters were then adapted and optimized to the query case by use of a semiautomated approach that required no expert planning knowledge. The plans generated with this knowledge-based approach were compared with the original treatment plans at several dose cutpoints. Compared with the original plan, the knowledge-based plan had a significantly more homogeneous dose to the planning target volume and a significantly lower maximum dose. The volumes of the rectum, bladder, and femoral heads above all cutpoints were nominally lower for the knowledge-based plan; the reductions were significantly lower for the rectum. In 40% of cases, the knowledge-based plan had overall superior (lower) dose-volume histograms for rectum and bladder; in 54% of cases, the comparison was equivocal; in 6% of cases, the knowledge-based plan was inferior for both bladder and rectum. Knowledge-based planning was superior or equivalent to the original plan in 95% of cases. The knowledge-based approach shows promise for homogenizing plan quality by transferring planning expertise from more experienced to less experienced institutions. Copyright © 2013 Elsevier Inc. All rights reserved.

A rule-based software test data generator

NASA Technical Reports Server (NTRS)

Deason, William H.; Brown, David B.; Chang, Kai-Hsiung; Cross, James H., II

1991-01-01

Rule-based software test data generation is proposed as an alternative to either path/predicate analysis or random data generation. A prototype rule-based test data generator for Ada programs is constructed and compared to a random test data generator. Four Ada procedures are used in the comparison. Approximately 2000 rule-based test cases and 100,000 randomly generated test cases are automatically generated and executed. The success of the two methods is compared using standard coverage metrics. Simple statistical tests showing that even the primitive rule-based test data generation prototype is significantly better than random data generation are performed. This result demonstrates that rule-based test data generation is feasible and shows great promise in assisting test engineers, especially when the rule base is developed further.

Computer Assisted Multi-Center Creation of Medical Knowledge Bases

PubMed Central

Giuse, Nunzia Bettinsoli; Giuse, Dario A.; Miller, Randolph A.

1988-01-01

Computer programs which support different aspects of medical care have been developed in recent years. Their capabilities range from diagnosis to medical imaging, and include hospital management systems and therapy prescription. In spite of their diversity these systems have one commonality: their reliance on a large body of medical knowledge in computer-readable form. This knowledge enables such programs to draw inferences, validate hypotheses, and in general to perform their intended task. As has been clear to developers of such systems, however, the creation and maintenance of medical knowledge bases are very expensive. Practical and economical difficulties encountered during this long-term process have discouraged most attempts. This paper discusses knowledge base creation and maintenance, with special emphasis on medical applications. We first describe the methods currently used and their limitations. We then present our recent work on developing tools and methodologies which will assist in the process of creating a medical knowledge base. We focus, in particular, on the possibility of multi-center creation of the knowledge base.

Translating three states of knowledge--discovery, invention, and innovation

PubMed Central

2010-01-01

Background Knowledge Translation (KT) has historically focused on the proper use of knowledge in healthcare delivery. A knowledge base has been created through empirical research and resides in scholarly literature. Some knowledge is amenable to direct application by stakeholders who are engaged during or after the research process, as shown by the Knowledge to Action (KTA) model. Other knowledge requires multiple transformations before achieving utility for end users. For example, conceptual knowledge generated through science or engineering may become embodied as a technology-based invention through development methods. The invention may then be integrated within an innovative device or service through production methods. To what extent is KT relevant to these transformations? How might the KTA model accommodate these additional development and production activities while preserving the KT concepts? Discussion Stakeholders adopt and use knowledge that has perceived utility, such as a solution to a problem. Achieving a technology-based solution involves three methods that generate knowledge in three states, analogous to the three classic states of matter. Research activity generates discoveries that are intangible and highly malleable like a gas; development activity transforms discoveries into inventions that are moderately tangible yet still malleable like a liquid; and production activity transforms inventions into innovations that are tangible and immutable like a solid. The paper demonstrates how the KTA model can accommodate all three types of activity and address all three states of knowledge. Linking the three activities in one model also illustrates the importance of engaging the relevant stakeholders prior to initiating any knowledge-related activities. Summary Science and engineering focused on technology-based devices or services change the state of knowledge through three successive activities. Achieving knowledge implementation requires methods that

Knowledge management impact of information technology Web 2.0/3.0. The case study of agent software technology usability in knowledge management system

NASA Astrophysics Data System (ADS)

Sołtysik-Piorunkiewicz, Anna

2015-02-01

How we can measure the impact of internet technology Web 2.0/3.0 for knowledge management? How we can use the Web 2.0/3.0 technologies for generating, evaluating, sharing, organizing knowledge in knowledge-based organization? How we can evaluate it from user-centered perspective? Article aims to provide a method for evaluate the usability of web technologies to support knowledge management in knowledge-based organizations of the various stages of the cycle knowledge management, taking into account: generating knowledge, evaluating knowledge, sharing knowledge, etc. for the modern Internet technologies based on the example of agent technologies. The method focuses on five areas of evaluation: GUI, functional structure, the way of content publication, organizational aspect, technological aspect. The method is based on the proposed indicators relating respectively to assess specific areas of evaluation, taking into account the individual characteristics of the scoring. Each of the features identified in the evaluation is judged first point wise, then this score is subject to verification and clarification by means of appropriate indicators of a given feature. The article proposes appropriate indicators to measure the impact of Web 2.0/3.0 technologies for knowledge management and verification them in an example of agent technology usability in knowledge management system.

KBGIS-2: A knowledge-based geographic information system

NASA Technical Reports Server (NTRS)

Smith, T.; Peuquet, D.; Menon, S.; Agarwal, P.

1986-01-01

The architecture and working of a recently implemented knowledge-based geographic information system (KBGIS-2) that was designed to satisfy several general criteria for the geographic information system are described. The system has four major functions that include query-answering, learning, and editing. The main query finds constrained locations for spatial objects that are describable in a predicate-calculus based spatial objects language. The main search procedures include a family of constraint-satisfaction procedures that use a spatial object knowledge base to search efficiently for complex spatial objects in large, multilayered spatial data bases. These data bases are represented in quadtree form. The search strategy is designed to reduce the computational cost of search in the average case. The learning capabilities of the system include the addition of new locations of complex spatial objects to the knowledge base as queries are answered, and the ability to learn inductively definitions of new spatial objects from examples. The new definitions are added to the knowledge base by the system. The system is currently performing all its designated tasks successfully, although currently implemented on inadequate hardware. Future reports will detail the performance characteristics of the system, and various new extensions are planned in order to enhance the power of KBGIS-2.

Computer game-based and traditional learning method: a comparison regarding students' knowledge retention.

PubMed

Rondon, Silmara; Sassi, Fernanda Chiarion; Furquim de Andrade, Claudia Regina

2013-02-25

Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students' prior knowledge (i.e. before undergoing the learning method), short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method) were assessed with a multiple choice questionnaire. Students' performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students' short and long-term knowledge retention.

Generating Knowledge in a Learning Study--From the Perspective of a Teacher Researcher

ERIC Educational Resources Information Center

Thorsten, Anja

2017-01-01

The purpose of this article is to discuss and describe how a clinical research method can be used to generate knowledge about teaching and learning. This will be addressed from a teacher researcher's perspective, taking a conducted Learning Study as the departure. Learning Study is an interventionist, iterative and collaborative research approach,…

Knowledge management: An abstraction of knowledge base and database management systems

NASA Technical Reports Server (NTRS)

Riedesel, Joel D.

1990-01-01

Artificial intelligence application requirements demand powerful representation capabilities as well as efficiency for real-time domains. Many tools exist, the most prevalent being expert systems tools such as ART, KEE, OPS5, and CLIPS. Other tools just emerging from the research environment are truth maintenance systems for representing non-monotonic knowledge, constraint systems, object oriented programming, and qualitative reasoning. Unfortunately, as many knowledge engineers have experienced, simply applying a tool to an application requires a large amount of effort to bend the application to fit. Much work goes into supporting work to make the tool integrate effectively. A Knowledge Management Design System (KNOMAD), is described which is a collection of tools built in layers. The layered architecture provides two major benefits; the ability to flexibly apply only those tools that are necessary for an application, and the ability to keep overhead, and thus inefficiency, to a minimum. KNOMAD is designed to manage many knowledge bases in a distributed environment providing maximum flexibility and expressivity to the knowledge engineer while also providing support for efficiency.

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.

Cooperating knowledge-based systems

NASA Technical Reports Server (NTRS)

Feigenbaum, Edward A.; Buchanan, Bruce G.

1988-01-01

This final report covers work performed under Contract NCC2-220 between NASA Ames Research Center and the Knowledge Systems Laboratory, Stanford University. The period of research was from March 1, 1987 to February 29, 1988. Topics covered were as follows: (1) concurrent architectures for knowledge-based systems; (2) methods for the solution of geometric constraint satisfaction problems, and (3) reasoning under uncertainty. The research in concurrent architectures was co-funded by DARPA, as part of that agency's Strategic Computing Program. The research has been in progress since 1985, under DARPA and NASA sponsorship. The research in geometric constraint satisfaction has been done in the context of a particular application, that of determining the 3-D structure of complex protein molecules, using the constraints inferred from NMR measurements.

Ambulatory orthopaedic surgery patients' knowledge with internet-based education.

PubMed

Heikkinen, Katja; Leino-Kilpi, H; Salanterä, S

2012-01-01

There is a growing need for patient education and an evaluation of its outcomes. The aim of this study was to compare ambulatory orthopaedic surgery patients' knowledge with Internet-based education and face-to-face education with a nurse. The following hypothesis was proposed: Internet-based patient education (experiment) is as effective as face-to-face education with a nurse (control) in increasing patients' level of knowledge and sufficiency of knowledge. In addition, the correlations of demographic variables were tested. The patients were randomized to either an experiment group (n = 72) or a control group (n = 75). Empirical data were collected with two instruments. Patients in both groups showed improvement in their knowledge during their care. Patients in the experiment group improved their knowledge level significantly more in total than those patients in the control group. There were no differences in patients' sufficiency of knowledge between the groups. Knowledge was correlated especially with patients' age, gender and earlier ambulatory surgeries. As a conclusion, positive results concerning patients' knowledge could be achieved with the Internet-based education. The Internet is a viable method in ambulatory care.

Developing Learning Progression-Based Teacher Knowledge Measures

ERIC Educational Resources Information Center

Jin, Hui; Shin, HyoJeong; Johnson, Michele E.; Kim, JinHo; Anderson, Charles W.

2015-01-01

This study developed learning progression-based measures of science teachers' content knowledge (CK) and pedagogical content knowledge (PCK). The measures focus on an important topic in secondary science curriculum using scientific reasoning (i.e., tracing matter, tracing energy, and connecting scales) to explain plants gaining weight and…

Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development

NASA Astrophysics Data System (ADS)

Buytaert, Wouter; Zulkafli, Zed; Grainger, Sam; Acosta, Luis; Bastiaensen, Johan; De Bièvre, Bert; Bhusal, Jagat; Chanie, Tilashwork; Clark, Julian; Dewulf, Art; Foggin, Marc; Hannah, David; Hergarten, Christian; Isaeva, Aiganysh; Karpouzoglou, Timos; Pandey, Bhopal; Paudel, Deepak; Sharma, Keshav; Steenhuis, Tammo; Tilahun, Seifu; Van Hecken, Gert; Zhumanova, Munavar

2014-10-01

The participation of the general public in the research design, data collection and interpretation process together with scientists is often referred to as citizen science. While citizen science itself has existed since the start of scientific practice, developments in sensing technology, data processing and visualisation, and communication of ideas and results, are creating a wide range of new opportunities for public participation in scientific research. This paper reviews the state of citizen science in a hydrological context and explores the potential of citizen science to complement more traditional ways of scientific data collection and knowledge generation for hydrological sciences and water resources management. Although hydrological data collection often involves advanced technology, the advent of robust, cheap and low-maintenance sensing equipment provides unprecedented opportunities for data collection in a citizen science context. These data have a significant potential to create new hydrological knowledge, especially in relation to the characterisation of process heterogeneity, remote regions, and human impacts on the water cycle. However, the nature and quality of data collected in citizen science experiments is potentially very different from those of traditional monitoring networks. This poses challenges in terms of their processing, interpretation, and use, especially with regard to assimilation of traditional knowledge, the quantification of uncertainties, and their role in decision support. It also requires care in designing citizen science projects such that the generated data complement optimally other available knowledge. Lastly, we reflect on the challenges and opportunities in the integration of hydrologically-oriented citizen science in water resources management, the role of scientific knowledge in the decision-making process, and the potential contestation to established community institutions posed by co-generation of new knowledge.

Using a knowledge-based planning solution to select patients for proton therapy.

PubMed

Delaney, Alexander R; Dahele, Max; Tol, Jim P; Kuijper, Ingrid T; Slotman, Ben J; Verbakel, Wilko F A R

2017-08-01

Patient selection for proton therapy by comparing proton/photon treatment plans is time-consuming and prone to bias. RapidPlan™, a knowledge-based-planning solution, uses plan-libraries to model and predict organ-at-risk (OAR) dose-volume-histograms (DVHs). We investigated whether RapidPlan, utilizing an algorithm based only on photon beam characteristics, could generate proton DVH-predictions and whether these could correctly identify patients for proton therapy. Model PROT and Model PHOT comprised 30 head-and-neck cancer proton and photon plans, respectively. Proton and photon knowledge-based-plans (KBPs) were made for ten evaluation-patients. DVH-prediction accuracy was analyzed by comparing predicted-vs-achieved mean OAR doses. KBPs and manual plans were compared using salivary gland and swallowing muscle mean doses. For illustration, patients were selected for protons if predicted Model PHOT mean dose minus predicted Model PROT mean dose (ΔPrediction) for combined OARs was ≥6Gy, and benchmarked using achieved KBP doses. Achieved and predicted Model PROT /Model PHOT mean dose R 2 was 0.95/0.98. Generally, achieved mean dose for Model PHOT /Model PROT KBPs was respectively lower/higher than predicted. Comparing Model PROT /Model PHOT KBPs with manual plans, salivary and swallowing mean doses increased/decreased by <2Gy, on average. ΔPrediction≥6Gy correctly selected 4 of 5 patients for protons. Knowledge-based DVH-predictions can provide efficient, patient-specific selection for protons. A proton-specific RapidPlan-solution could improve results. Copyright © 2017 Elsevier B.V. All rights reserved.

Chemical name extraction based on automatic training data generation and rich feature set.

PubMed

Yan, Su; Spangler, W Scott; Chen, Ying

2013-01-01

The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.

Methodology for testing and validating knowledge bases

NASA Technical Reports Server (NTRS)

Krishnamurthy, C.; Padalkar, S.; Sztipanovits, J.; Purves, B. R.

1987-01-01

A test and validation toolset developed for artificial intelligence programs is described. The basic premises of this method are: (1) knowledge bases have a strongly declarative character and represent mostly structural information about different domains, (2) the conditions for integrity, consistency, and correctness can be transformed into structural properties of knowledge bases, and (3) structural information and structural properties can be uniformly represented by graphs and checked by graph algorithms. The interactive test and validation environment have been implemented on a SUN workstation.

Students' Refinement of Knowledge during the Development of Knowledge Bases for Expert Systems.

ERIC Educational Resources Information Center

Lippert, Renate; Finley, Fred

The refinement of the cognitive knowledge base was studied through exploration of the transition from novice to expert and the use of an instructional strategy called novice knowledge engineering. Six college freshmen, who were enrolled in an honors physics course, used an expert system to create questions, decisions, rules, and explanations…

Knowledge-based processing for aircraft flight control

NASA Technical Reports Server (NTRS)

Painter, John H.; Glass, Emily; Economides, Gregory; Russell, Paul

1994-01-01

This Contractor Report documents research in Intelligent Control using knowledge-based processing in a manner dual to methods found in the classic stochastic decision, estimation, and control discipline. Such knowledge-based control has also been called Declarative, and Hybid. Software architectures were sought, employing the parallelism inherent in modern object-oriented modeling and programming. The viewpoint adopted was that Intelligent Control employs a class of domain-specific software architectures having features common over a broad variety of implementations, such as management of aircraft flight, power distribution, etc. As much attention was paid to software engineering issues as to artificial intelligence and control issues. This research considered that particular processing methods from the stochastic and knowledge-based worlds are duals, that is, similar in a broad context. They provide architectural design concepts which serve as bridges between the disparate disciplines of decision, estimation, control, and artificial intelligence. This research was applied to the control of a subsonic transport aircraft in the airport terminal area.

Mothers' electrophysiological, subjective, and observed emotional responding to infant crying: The role of secure base script knowledge.

PubMed

Groh, Ashley M; Roisman, Glenn I; Haydon, Katherine C; Bost, Kelly; McElwain, Nancy; Garcia, Leanna; Hester, Colleen

2015-11-01

This study examined the extent to which secure base script knowledge-reflected in the ability to generate narratives in which attachment-relevant events are encountered, a clear need for assistance is communicated, competent help is provided and accepted, and the problem is resolved-is associated with mothers' electrophysiological, subjective, and observed emotional responses to an infant distress vocalization. While listening to an infant crying, mothers (N = 108, M age = 34 years) lower on secure base script knowledge exhibited smaller shifts in relative left (vs. right) frontal EEG activation from rest, reported smaller reductions in feelings of positive emotion from rest, and expressed greater levels of tension. Findings indicate that lower levels of secure base script knowledge are associated with an organization of emotional responding indicative of a less flexible and more emotionally restricted response to infant distress. Discussion focuses on the contribution of mothers' attachment representations to their ability to effectively manage emotional responding to infant distress in a manner expected to support sensitive caregiving.

Perspectives on knowledge in engineering design

NASA Technical Reports Server (NTRS)

Rasdorf, W. J.

1985-01-01

Various perspectives are given of the knowledge currently used in engineering design, specifically dealing with knowledge-based expert systems (KBES). Constructing an expert system often reveals inconsistencies in domain knowledge while formalizing it. The types of domain knowledge (facts, procedures, judgments, and control) differ from the classes of that knowledge (creative, innovative, and routine). The feasible tasks for expert systems can be determined based on these types and classes of knowledge. Interpretive tasks require reasoning about a task in light of the knowledge available, where generative tasks create potential solutions to be tested against constraints. Only after classifying the domain by type and level can the engineer select a knowledge-engineering tool for the domain being considered. The critical features to be weighed after classification are knowledge representation techniques, control strategies, interface requirements, compatibility with traditional systems, and economic considerations.

Proposing a Knowledge Base for Teaching Academic Content to English Language Learners: Disciplinary Linguistic Knowledge

ERIC Educational Resources Information Center

Turkan, Sultan; De Oliveira, Luciana C.; Lee, Okhee; Phelps, Geoffrey

2014-01-01

Background/Context: The current research on teacher knowledge and teacher accountability falls short on information about what teacher knowledge base could guide preparation and accountability of the mainstream teachers for meeting the academic needs of ELLs. Most recently, research on specialized knowledge for teaching has offered ways to…

Knowledge retrieval as one type of knowledge-based decision support in medicine: results of an evaluation study.

PubMed

Haux, R; Grothe, W; Runkel, M; Schackert, H K; Windeler, H J; Winter, A; Wirtz, R; Herfarth, C; Kunze, S

1996-04-01

We report on a prospective, prolective observational study, supplying information on how physicians and other health care professionals retrieve medical knowledge on-line within the Heidelberg University Hospital information system. Within this hospital information system, on-line access to medical knowledge has been realised by installing a medical knowledge server in the range of about 24 GB and by providing access to it by health care professional workstations in wards, physicians' rooms, etc. During the study, we observed about 96 accesses per working day. The main group of health care professionals retrieving medical knowledge were physicians and medical students. Primary reasons for its utilisation were identified as support for the users' scientific work (50%), own clinical cases (19%), general medical problems (14%) and current clinical problems (13%). Health care professionals had accesses to medical knowledge bases such as MEDLINE (79%), drug bases ('Rote Liste', 6%), and to electronic text books and knowledge base systems as well. Sixty-five percent of accesses to medical knowledge were judged to be successful. In our opinion, medical knowledge retrieval can serve as a first step towards knowledge processing in medicine. We point out the consequences for the management of hospital information systems in order to provide the prerequisites for such a type of knowledge retrieval.

Expert and Knowledge Based Systems.

ERIC Educational Resources Information Center

Demaid, Adrian; Edwards, Lyndon

1987-01-01

Discusses the nature and current state of knowledge-based systems and expert systems. Describes an expert system from the viewpoints of a computer programmer and an applications expert. Addresses concerns related to materials selection and forecasts future developments in the teaching of materials engineering. (ML)

Knowledge-Based Aid: A Four Agency Comparative Study

ERIC Educational Resources Information Center

McGrath, Simon; King, Kenneth

2004-01-01

Part of the response of many development cooperation agencies to the challenges of globalisation, ICTs and the knowledge economy is to emphasise the importance of knowledge for development. This paper looks at the discourses and practices of ''knowledge-based aid'' through an exploration of four agencies: the World Bank, DFID, Sida and JICA. It…

A Knowledge Base for FIA Data Uses

Treesearch

Victor A. Rudis

2005-01-01

Knowledge management provides a way to capture the collective wisdom of an organization, facilitate organizational learning, and foster opportunities for improvement. This paper describes a knowledge base compiled from uses of field observations made by the U.S. Department of Agriculture Forest Service, Forest Inventory and Analysis program and a citation database of...

Knowledge-Based Systems Research

DTIC Science & Technology

1990-08-24

P. S., Laird, J. E., Newell, A. and McCarl, R. 1991. A Preliminary Analysis of the SOAR Architecture as a Basis for General Intelligence . Artifcial ...on reverse of neceSSjr’y gnd identify by block nhmber) FIELD I GRO’= SUB-C.OROUC Artificial Intelligence , Blackboard Systems, U°nstraint Satisfaction...knowledge acquisition; symbolic simulation; logic-based systems with self-awareness; SOAR, an architecture for general intelligence and learning

Computer game-based and traditional learning method: a comparison regarding students’ knowledge retention

PubMed Central

2013-01-01

Background Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Methods Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students’ prior knowledge (i.e. before undergoing the learning method), short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method) were assessed with a multiple choice questionnaire. Students’ performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Results Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. Conclusions The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students’ short and long-term knowledge retention. PMID:23442203

Genetic counselors’ (GC) knowledge, awareness, and understanding of clinical next-generation sequencing (NGS) genomic testing

PubMed Central

Boland, PM; Ruth, K; Matro, JM; Rainey, KL; Fang, CY; Wong, YN; Daly, MB; Hall, MJ

2014-01-01

Genomic tests are increasingly complex, less expensive, and more widely available with the advent of next-generation sequencing (NGS). We assessed knowledge and perceptions among genetic counselors pertaining to NGS genomic testing via an online survey. Associations between selected characteristics and perceptions were examined. Recent education on NGS testing was common, but practical experience limited. Perceived understanding of clinical NGS was modest, specifically concerning tumor testing. Greater perceived understanding of clinical NGS testing correlated with more time spent in cancer-related counseling, exposure to NGS testing, and NGS-focused education. Substantial disagreement about the role of counseling for tumor-based testing was seen. Finally, a majority of counselors agreed with the need for more education about clinical NGS testing, supporting this approach to optimizing implementation. PMID:25523111

A Web-Based Earth-Systems Knowledge Portal and Collaboration Platform

NASA Astrophysics Data System (ADS)

D'Agnese, F. A.; Turner, A. K.

2010-12-01

In support of complex water-resource sustainability projects in the Great Basin region of the United States, Earth Knowledge, Inc. has developed several web-based data management and analysis platforms that have been used by its scientists, clients, and public to facilitate information exchanges, collaborations, and decision making. These platforms support accurate water-resource decision-making by combining second-generation internet (Web 2.0) technologies with traditional 2D GIS and web-based 2D and 3D mapping systems such as Google Maps, and Google Earth. Most data management and analysis systems use traditional software systems to address the data needs and usage behavior of the scientific community. In contrast, these platforms employ more accessible open-source and “off-the-shelf” consumer-oriented, hosted web-services. They exploit familiar software tools using industry standard protocols, formats, and APIs to discover, process, fuse, and visualize earth, engineering, and social science datasets. Thus, they respond to the information needs and web-interface expectations of both subject-matter experts and the public. Because the platforms continue to gather and store all the contributions of their broad-spectrum of users, each new assessment leverages the data, information, and expertise derived from previous investigations. In the last year, Earth Knowledge completed a conceptual system design and feasibility study for a platform, which has a Knowledge Portal providing access to users wishing to retrieve information or knowledge developed by the science enterprise and a Collaboration Environment Module, a framework that links the user-access functions to a Technical Core supporting technical and scientific analyses including Data Management, Analysis and Modeling, and Decision Management, and to essential system administrative functions within an Administrative Module. The over-riding technical challenge is the design and development of a single

Using concept mapping to evaluate knowledge structure in problem-based learning.

PubMed

Hung, Chia-Hui; Lin, Chen-Yung

2015-11-27

Many educational programs incorporate problem-based learning (PBL) to promote students' learning; however, the knowledge structure developed in PBL remains unclear. The aim of this study was to use concept mapping to generate an understanding of the use of PBL in the development of knowledge structures. Using a quasi-experimental study design, we employed concept mapping to illustrate the effects of PBL by examining the patterns of concepts and differences in the knowledge structures of students taught with and without a PBL approach. Fifty-two occupational therapy undergraduates were involved in the study and were randomly divided into PBL and control groups. The PBL group was given two case scenarios for small group discussion, while the control group continued with ordinary teaching and learning. Students were asked to make concept maps after being taught about knowledge structure. A descriptive analysis of the morphology of concept maps was conducted in order to compare the integration of the students' knowledge structures, and statistical analyses were done to understand the differences between groups. Three categories of concept maps were identified as follows: isolated, departmental, and integrated. The students in the control group constructed more isolated maps, while the students in the PBL group tended toward integrated mapping. Concept Relationships, Hierarchy Levels, and Cross Linkages in the concept maps were significantly greater in the PBL group; however, examples of concept maps did not differ significantly between the two groups. The data indicated that PBL had a strong effect on the acquisition and integration of knowledge. The important properties of PBL, including situational learning, problem spaces, and small group interactions, can help students to acquire more concepts, achieve an integrated knowledge structure, and enhance clinical reasoning.

Knowledge Discovery in Literature Data Bases

NASA Astrophysics Data System (ADS)

Albrecht, Rudolf; Merkl, Dieter

The concept of knowledge discovery as defined through ``establishing previously unknown and unsuspected relations of features in a data base'' is, cum grano salis, relatively easy to implement for data bases containing numerical data. Increasingly we find at our disposal data bases containing scientific literature. Computer assisted detection of unknown relations of features in such data bases would be extremely valuable and would lead to new scientific insights. However, the current representation of scientific knowledge in such data bases is not conducive to computer processing. Any correlation of features still has to be done by the human reader, a process which is plagued by ineffectiveness and incompleteness. On the other hand we note that considerable progress is being made in an area where reading all available material is totally prohibitive: the World Wide Web. Robots and Web crawlers mine the Web continuously and construct data bases which allow the identification of pages of interest in near real time. An obvious step is to categorize and classify the documents in the text data base. This can be used to identify papers worth reading, or which are of unexpected cross-relevance. We show the results of first experiments using unsupervised classification based on neural networks.

Population Education: A Knowledge Base.

ERIC Educational Resources Information Center

Jacobson, Willard J.

To aid junior high and high school educators and curriculum planners as they develop population education programs, the book provides an overview of the population education knowledge base. In addition, it suggests learning activities, discussion questions, and background information which can be integrated into courses dealing with population,…

The Coming of Knowledge-Based Business.

ERIC Educational Resources Information Center

Davis, Stan; Botkin, Jim

1994-01-01

Economic growth will come from knowledge-based businesses whose "smart" products filter and interpret information. Businesses will come to think of themselves as educators and their customers as learners. (SK)

Diagnosis by integrating model-based reasoning with knowledge-based reasoning

NASA Technical Reports Server (NTRS)

Bylander, Tom

1988-01-01

Our research investigates how observations can be categorized by integrating a qualitative physical model with experiential knowledge. Our domain is diagnosis of pathologic gait in humans, in which the observations are the gait motions, muscle activity during gait, and physical exam data, and the diagnostic hypotheses are the potential muscle weaknesses, muscle mistimings, and joint restrictions. Patients with underlying neurological disorders typically have several malfunctions. Among the problems that need to be faced are: the ambiguity of the observations, the ambiguity of the qualitative physical model, correspondence of the observations and hypotheses to the qualitative physical model, the inherent uncertainty of experiential knowledge, and the combinatorics involved in forming composite hypotheses. Our system divides the work so that the knowledge-based reasoning suggests which hypotheses appear more likely than others, the qualitative physical model is used to determine which hypotheses explain which observations, and another process combines these functionalities to construct a composite hypothesis based on explanatory power and plausibility. We speculate that the reasoning architecture of our system is generally applicable to complex domains in which a less-than-perfect physical model and less-than-perfect experiential knowledge need to be combined to perform diagnosis.

A knowledge-based system for prototypical reasoning

NASA Astrophysics Data System (ADS)

Lieto, Antonio; Minieri, Andrea; Piana, Alberto; Radicioni, Daniele P.

2015-04-01

In this work we present a knowledge-based system equipped with a hybrid, cognitively inspired architecture for the representation of conceptual information. The proposed system aims at extending the classical representational and reasoning capabilities of the ontology-based frameworks towards the realm of the prototype theory. It is based on a hybrid knowledge base, composed of a classical symbolic component (grounded on a formal ontology) with a typicality based one (grounded on the conceptual spaces framework). The resulting system attempts to reconcile the heterogeneous approach to the concepts in Cognitive Science with the dual process theories of reasoning and rationality. The system has been experimentally assessed in a conceptual categorisation task where common sense linguistic descriptions were given in input, and the corresponding target concepts had to be identified. The results show that the proposed solution substantially extends the representational and reasoning 'conceptual' capabilities of standard ontology-based systems.

Genetic counselors' (GC) knowledge, awareness, understanding of clinical next-generation sequencing (NGS) genomic testing.

PubMed

Boland, P M; Ruth, K; Matro, J M; Rainey, K L; Fang, C Y; Wong, Y N; Daly, M B; Hall, M J

2015-12-01

Genomic tests are increasingly complex, less expensive, and more widely available with the advent of next-generation sequencing (NGS). We assessed knowledge and perceptions among genetic counselors pertaining to NGS genomic testing via an online survey. Associations between selected characteristics and perceptions were examined. Recent education on NGS testing was common, but practical experience limited. Perceived understanding of clinical NGS was modest, specifically concerning tumor testing. Greater perceived understanding of clinical NGS testing correlated with more time spent in cancer-related counseling, exposure to NGS testing, and NGS-focused education. Substantial disagreement about the role of counseling for tumor-based testing was seen. Finally, a majority of counselors agreed with the need for more education about clinical NGS testing, supporting this approach to optimizing implementation. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The Knowledge-Based Software Assistant: Beyond CASE

NASA Technical Reports Server (NTRS)

Carozzoni, Joseph A.

1993-01-01

This paper will outline the similarities and differences between two paradigms of software development. Both support the whole software life cycle and provide automation for most of the software development process, but have different approaches. The CASE approach is based on a set of tools linked by a central data repository. This tool-based approach is data driven and views software development as a series of sequential steps, each resulting in a product. The Knowledge-Based Software Assistant (KBSA) approach, a radical departure from existing software development practices, is knowledge driven and centers around a formalized software development process. KBSA views software development as an incremental, iterative, and evolutionary process with development occurring at the specification level.

Development of a Knowledge Base for Enduser Consultation of AAL-Systems.

PubMed

Röll, Natalie; Stork, Wilhelm; Rosales, Bruno; Stephan, René; Knaup, Petra

2016-01-01

Manufacturer information, user experiences and product availability of assistive living technologies are usually not known to citizens or consultation centers. The different knowledge levels concerning the availability of technology shows the need for building up a knowledge base. The aim of this contribution is the definition of requirements in the development of knowledge bases for AAL consultations. The major requirements, such as a maintainable and easy to use structure were implemented into a web based knowledge base, which went productive in ~3700 consulting interviews of municipal technology information centers. Within this field phase the implementation of the requirements for a knowledge base in the field of AAL consulting was evaluated and further developed.

"Comments on Greenhow, Robelia, and Hughes": Technologies that Facilitate Generating Knowledge and Possibly Wisdom

ERIC Educational Resources Information Center

Dede, Chris

2009-01-01

Greenhow, Robelia, and Hughes (2009) argue that Web 2.0 media are well suited to enhancing the education research community's purpose of generating and sharing knowledge. The author of this comment article first articulates how a research infrastructure with capabilities for communal bookmarking, photo and video sharing, social networking, wikis,…

Big data analytics in immunology: a knowledge-based approach.

PubMed

Zhang, Guang Lan; Sun, Jing; Chitkushev, Lou; Brusic, Vladimir

2014-01-01

With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow.

Knowledge-Based Instructional Gaming: GEO.

ERIC Educational Resources Information Center

Duchastel, Philip

1989-01-01

Describes the design and development of an instructional game, GEO, in which the user learns elements of Canadian geography. The use of knowledge-based artificial intelligence techniques is discussed, the use of HyperCard in the design of GEO is explained, and future directions are suggested. (15 references) (Author/LRW)

The HEP Software and Computing Knowledge Base

NASA Astrophysics Data System (ADS)

Wenaus, T.

2017-10-01

HEP software today is a rich and diverse domain in itself and exists within the mushrooming world of open source software. As HEP software developers and users we can be more productive and effective if our work and our choices are informed by a good knowledge of what others in our community have created or found useful. The HEP Software and Computing Knowledge Base, hepsoftware.org, was created to facilitate this by serving as a collection point and information exchange on software projects and products, services, training, computing facilities, and relating them to the projects, experiments, organizations and science domains that offer them or use them. It was created as a contribution to the HEP Software Foundation, for which a HEP S&C knowledge base was a much requested early deliverable. This contribution will motivate and describe the system, what it offers, its content and contributions both existing and needed, and its implementation (node.js based web service and javascript client app) which has emphasized ease of use for both users and contributors.

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

Caregiving Antecedents of Secure Base Script Knowledge: A Comparative Analysis of Young Adult Attachment Representations

PubMed Central

Steele, Ryan D.; Waters, Theodore E. A.; Bost, Kelly K.; Vaughn, Brian E.; Truitt, Warren; Waters, Harriet S.; Booth-LaForce, Cathryn; Roisman, Glenn I.

2015-01-01

Based on a sub-sample (N = 673) of the NICHD Study of Early Child Care and Youth Development (SECCYD) cohort, this paper reports data from a follow-up assessment at age 18 years on the antecedents of secure base script knowledge, as reflected in the ability to generate narratives in which attachment-related difficulties are recognized, competent help is provided, and the problem is resolved. Secure base script knowledge was (a) modestly to moderately correlated with more well established assessments of adult attachment, (b) associated with mother-child attachment in the first three years of life and with observations of maternal and paternal sensitivity from childhood to adolescence, and (c) partially accounted for associations previously documented in the SECCYD cohort between early caregiving experiences and Adult Attachment Interview states of mind (Booth-LaForce & Roisman, 2014) as well as self-reported attachment styles (Fraley, Roisman, Booth-LaForce, Owen, & Holland, 2013). PMID:25264703

The development of a novel knowledge-based weaning algorithm using pulmonary parameters: a simulation study.

PubMed

Guler, Hasan; Kilic, Ugur

2018-03-01

Weaning is important for patients and clinicians who have to determine correct weaning time so that patients do not become addicted to the ventilator. There are already some predictors developed, such as the rapid shallow breathing index (RSBI), the pressure time index (PTI), and Jabour weaning index. Many important dimensions of weaning are sometimes ignored by these predictors. This is an attempt to develop a knowledge-based weaning process via fuzzy logic that eliminates the disadvantages of the present predictors. Sixteen vital parameters listed in published literature have been used to determine the weaning decisions in the developed system. Since there are considered to be too many individual parameters in it, related parameters were grouped together to determine acid-base balance, adequate oxygenation, adequate pulmonary function, hemodynamic stability, and the psychological status of the patients. To test the performance of the developed algorithm, 20 clinical scenarios were generated using Monte Carlo simulations and the Gaussian distribution method. The developed knowledge-based algorithm and RSBI predictor were applied to the generated scenarios. Finally, a clinician evaluated each clinical scenario independently. The Student's t test was used to show the statistical differences between the developed weaning algorithm, RSBI, and the clinician's evaluation. According to the results obtained, there were no statistical differences between the proposed methods and the clinician evaluations.

Approximate Degrees of Similarity between a User's Knowledge and the Tutorial Systems' Knowledge Base

ERIC Educational Resources Information Center

Mogharreban, Namdar

2004-01-01

A typical tutorial system functions by means of interaction between four components: the expert knowledge base component, the inference engine component, the learner's knowledge component and the user interface component. In typical tutorial systems the interaction and the sequence of presentation as well as the mode of evaluation are…

Translating knowledge from Pakistan's second generation surveillance system to other global contexts.

PubMed

Adrien, Alix; Thompson, Laura H; Archibald, Chris P; Sandstrom, Paul A; Munro, Michelle; Emmanuel, Faran; Blanchard, James F

2013-09-01

From 2004 to 2011, a collaborative project was undertaken to enhance the capacity of the Government of Pakistan to implement an effective second-generation surveillance system for HIV/AIDS, known as the HIV/AIDS Surveillance Project (HASP). In four separate rounds, behavioural questionnaires were administered among injection drug users, and female, male and hijra (transgender) sex workers. Dried blood spots were collected for HIV testing. Through interviews with project staff in Pakistan and Canada, we have undertaken a critical review of the role of HASP in generating, using and translating knowledge, with an emphasis on capacity building within both the donor and recipient countries. We also documented ongoing and future opportunities for the translation of knowledge produced through HASP. Knowledge translation activities have included educational workshops and consultations held in places as diverse as Colombia and Cairo, and the implementation of HASP methodologies in Asia, the Middle East and sub-Saharan Africa. HASP methodologies have been incorporated in multiple WHO reports. Importantly, the donor country, Canada, has benefited in significant ways from this partnership. Operational and logistical lessons from HASP have, in turn, improved how surveillance is performed in Canada. Through this project, significant capacity was built among the staff of HASP, non-governmental organisations which were engaged as implementation partners, data coordination units which were established in each province, and in the laboratory. As is to be expected, different organisations have different agendas and priorities, requiring negotiation, at times, to ensure the success of collaborative activities. Overall, there has been considerable interest in and opportunities made for learning about the methodologies and approaches employed by HASP. Generally, the recognition of the strengths of the approaches and methodologies used by HASP has ensured an appetite for opportunities of

A Voronoi interior adjacency-based approach for generating a contour tree

NASA Astrophysics Data System (ADS)

Chen, Jun; Qiao, Chaofei; Zhao, Renliang

2004-05-01

A contour tree is a good graphical tool for representing the spatial relations of contour lines and has found many applications in map generalization, map annotation, terrain analysis, etc. A new approach for generating contour trees by introducing a Voronoi-based interior adjacency set concept is proposed in this paper. The immediate interior adjacency set is employed to identify all of the children contours of each contour without contour elevations. It has advantages over existing methods such as the point-in-polygon method and the region growing-based method. This new approach can be used for spatial data mining and knowledge discovering, such as the automatic extraction of terrain features and construction of multi-resolution digital elevation model.

Knowledge-based simulation using object-oriented programming

NASA Technical Reports Server (NTRS)

Sidoran, Karen M.

1993-01-01

Simulations have become a powerful mechanism for understanding and modeling complex phenomena. Their results have had substantial impact on a broad range of decisions in the military, government, and industry. Because of this, new techniques are continually being explored and developed to make them even more useful, understandable, extendable, and efficient. One such area of research is the application of the knowledge-based methods of artificial intelligence (AI) to the computer simulation field. The goal of knowledge-based simulation is to facilitate building simulations of greatly increased power and comprehensibility by making use of deeper knowledge about the behavior of the simulated world. One technique for representing and manipulating knowledge that has been enhanced by the AI community is object-oriented programming. Using this technique, the entities of a discrete-event simulation can be viewed as objects in an object-oriented formulation. Knowledge can be factual (i.e., attributes of an entity) or behavioral (i.e., how the entity is to behave in certain circumstances). Rome Laboratory's Advanced Simulation Environment (RASE) was developed as a research vehicle to provide an enhanced simulation development environment for building more intelligent, interactive, flexible, and realistic simulations. This capability will support current and future battle management research and provide a test of the object-oriented paradigm for use in large scale military applications.

Presentation planning using an integrated knowledge base

NASA Technical Reports Server (NTRS)

Arens, Yigal; Miller, Lawrence; Sondheimer, Norman

1988-01-01

A description is given of user interface research aimed at bringing together multiple input and output modes in a way that handles mixed mode input (commands, menus, forms, natural language), interacts with a diverse collection of underlying software utilities in a uniform way, and presents the results through a combination of output modes including natural language text, maps, charts and graphs. The system, Integrated Interfaces, derives much of its ability to interact uniformly with the user and the underlying services and to build its presentations, from the information present in a central knowledge base. This knowledge base integrates models of the application domain (Navy ships in the Pacific region, in the current demonstration version); the structure of visual displays and their graphical features; the underlying services (data bases and expert systems); and interface functions. The emphasis is on a presentation planner that uses the knowledge base to produce multi-modal output. There has been a flurry of recent work in user interface management systems. (Several recent examples are listed in the references). Existing work is characterized by an attempt to relieve the software designer of the burden of handcrafting an interface for each application. The work has generally focused on intelligently handling input. This paper deals with the other end of the pipeline - presentations.

Application of Knowledge-Based Techniques to Tracking Function

DTIC Science & Technology

2006-09-01

38394041 42434445 46474849 505152 53545556 57585960 616263 646566 676869 707172 737475 7677 7879 8081 8283 8485 8687 8889 9091 9293 9495 969798 99100...Knowledge-based applications to adaptive space-time processing. Volume I: Summary”, AFRL-SN-TR-2001-146 Vol. I (of Vol. VI ), Final Technical Report, July...2001-146 Vol. IV (of Vol. VI ), Final Technical Report, July 2001. [53] C. Morgan, L. Moyer, “Knowledge-based applications to adaptive space-time

Knowledge-Based Reinforcement Learning for Data Mining

NASA Astrophysics Data System (ADS)

Kudenko, Daniel; Grzes, Marek

experts have developed heuristics that help them in planning and scheduling resources in their work place. However, this domain knowledge is often rough and incomplete. When the domain knowledge is used directly by an automated expert system, the solutions are often sub-optimal, due to the incompleteness of the knowledge, the uncertainty of environments, and the possibility to encounter unexpected situations. RL, on the other hand, can overcome the weaknesses of the heuristic domain knowledge and produce optimal solutions. In the talk we propose two techniques, which represent first steps in the area of knowledge-based RL (KBRL). The first technique [1] uses high-level STRIPS operator knowledge in reward shaping to focus the search for the optimal policy. Empirical results show that the plan-based reward shaping approach outperforms other RL techniques, including alternative manual and MDP-based reward shaping when it is used in its basic form. We showed that MDP-based reward shaping may fail and successful experiments with STRIPS-based shaping suggest modifications which can overcome encountered problems. The STRIPSbased method we propose allows expressing the same domain knowledge in a different way and the domain expert can choose whether to define an MDP or STRIPS planning task. We also evaluated the robustness of the proposed STRIPS-based technique to errors in the plan knowledge. In case that STRIPS knowledge is not available, we propose a second technique [2] that shapes the reward with hierarchical tile coding. Where the Q-function is represented with low-level tile coding, a V-function with coarser tile coding can be learned in parallel and used to approximate the potential for ground states. In the context of data mining, our KBRL approaches can also be used for any data collection task where the acquisition of data may incur considerable cost. In addition, observing the data collection agent in specific scenarios may lead to new insights into optimal data

Neural processing of overt word generation in healthy individuals: the effect of age and word knowledge.

PubMed

Nagels, Arne; Kircher, Tilo; Dietsche, Bruno; Backes, Heidelore; Marquetand, Justus; Krug, Axel

2012-07-16

Verbal fluency is a classical and widely used neuropsychological instrument to assess cognitive abilities. Results of previous studies indicate an influence on verbal fluency performance of both, age and word knowledge. So far, no imaging study has investigated the neural mechanisms underlying an age and word knowledge related decline on the quantitative verbal output in a highly demanding overt and continuous semantic fluency task. Fifty healthy volunteers (age 22-56 years, verbal IQ 95-143) overtly and continuously articulated words in response to ten visually presented semantic categories while BOLD signal was measured with fMRI. Verbal responses were recorded with an MRI compatible microphone and transcribed after the scanning session. The number of produced words as well as age, word knowledge and level of education was implemented in the design matrix enabling a separate analysis of these factors on both, neural responses and behavioral differences. There was a significant correlation of level of education and number of generated words, but no significant correlations of generated words and age or word knowledge were observed. On the neural level, a widespread network was found for the word production task as contrasted with the resting condition, encompassing the bilateral superior temporal gyri, the cerebellum and the SMA. An age related positive correlation was found in the bilateral inferior and middle frontal gyri, the anterior cingulate gyrus, the left precentral gyrus and the right insula. A lower word knowledge resulted in enhanced BOLD responses in the right superior temporal gyrus and the left superior frontal gyrus. Results are interpreted in terms of compensation mechanisms countervailing potential age and word knowledge related effects. Copyright © 2012 Elsevier Inc. All rights reserved.

The Effects of Embedded Generative Learning Strategies and Collaboration on Knowledge Acquisition in a Cognitive Flexibility-Based Computer Learning Environment

DTIC Science & Technology

1998-08-07

cognitive flexibility theory and generative learning theory which focus primarily on the individual student’s cognitive development , collaborative... develop "Handling Transfusion Hazards," a computer program based upon cognitive flexibility theory principles. The Program: Handling Transfusion Hazards...computer program was developed according to cognitive flexibility theory principles. A generative version was then developed by embedding

Field-based generation and social validation managers and staff competencies for small community residences.

PubMed

Thousand, J S; Burchard, S N; Hasazi, J E

1986-01-01

Characteristics and competencies for four staff positions in community residences for individuals with mental retardation were identified utilizing multiple empirical and deductive methods with field-based practitioners and field-based experts. The more commonly used competency generation methods of expert opinion and job performance analysis generated a high degree of knowledge and skill-based competencies similar to course curricula. Competencies generated by incumbent practitioners through open-ended methods of personal structured interview and critical incident analysis were ones which related to personal style, interpersonal interaction, and humanistic orientation. Although seldom included in staff, paraprofessional, or professional training curricula, these latter competencies include those identified by Carl Rogers as essential for developing an effective helping relationship in a therapeutic situation (i.e., showing liking, interest, and respect for the clients; being able to communicate positive regard to the client). Of 21 core competency statements selected as prerequisites to employment for all four staff positions, the majority (17 of 21) represented interpersonal skills important to working with others, including responsiveness to resident needs, personal valuation of persons with mental retardation, and normalization principles.

Games for Learning: Which Template Generates Social Construction of Knowledge?

ERIC Educational Resources Information Center

Garcia, Francisco A.

2015-01-01

The purpose of this study was to discover how three person teams use game templates (trivia, role-play, or scavenger hunt) to socially construct knowledge. The researcher designed an experimental Internet-based database to facilitate teams creating each game. Teams consisted of teachers, students, hobbyist, and business owners who shared similar…

Knowledge representation to support reasoning based on multiple models

NASA Technical Reports Server (NTRS)

Gillam, April; Seidel, Jorge P.; Parker, Alice C.

1990-01-01

Model Based Reasoning is a powerful tool used to design and analyze systems, which are often composed of numerous interactive, interrelated subsystems. Models of the subsystems are written independently and may be used together while they are still under development. Thus the models are not static. They evolve as information becomes obsolete, as improved artifact descriptions are developed, and as system capabilities change. Researchers are using three methods to support knowledge/data base growth, to track the model evolution, and to handle knowledge from diverse domains. First, the representation methodology is based on having pools, or types, of knowledge from which each model is constructed. In addition information is explicit. This includes the interactions between components, the description of the artifact structure, and the constraints and limitations of the models. The third principle we have followed is the separation of the data and knowledge from the inferencing and equation solving mechanisms. This methodology is used in two distinct knowledge-based systems: one for the design of space systems and another for the synthesis of VLSI circuits. It has facilitated the growth and evolution of our models, made accountability of results explicit, and provided credibility for the user community. These capabilities have been implemented and are being used in actual design projects.

Towards a Reconceptualisation of "Word" for High Frequency Word Generation in Word Knowledge Studies

ERIC Educational Resources Information Center

Sibanda, Jabulani; Baxen, Jean

2014-01-01

The present paper derives from a PhD study investigating the nexus between Grade 4 textbook vocabulary demands and Grade 3 isiXhosa-speaking learners' knowledge of that vocabulary to enable them to read to learn in Grade 4. The paper challenges the efficacy of the four current definitions of "word" for generating high frequency words…

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

Knowledge-based engineering of a PLC controlled telescope

NASA Astrophysics Data System (ADS)

Pessemier, Wim; Raskin, Gert; Saey, Philippe; Van Winckel, Hans; Deconinck, Geert

2016-08-01

As the new control system of the Mercator Telescope is being finalized, we can review some technologies and design methodologies that are advantageous, despite their relative uncommonness in astronomical instrumentation. Particular for the Mercator Telescope is that it is controlled by a single high-end soft-PLC (Programmable Logic Controller). Using off-the-shelf components only, our distributed embedded system controls all subsystems of the telescope such as the pneumatic primary mirror support, the hydrostatic bearing, the telescope axes, the dome, the safety system, and so on. We show how real-time application logic can be written conveniently in typical PLC languages (IEC 61131-3) and in C++ (to implement the pointing kernel) using the commercial TwinCAT 3 programming environment. This software processes the inputs and outputs of the distributed system in real-time via an observatory-wide EtherCAT network, which is synchronized with high precision to an IEEE 1588 (PTP, Precision Time Protocol) time reference clock. Taking full advantage of the ability of soft-PLCs to run both real-time and non real-time software, the same device also hosts the most important user interfaces (HMIs or Human Machine Interfaces) and communication servers (OPC UA for process data, FTP for XML configuration data, and VNC for remote control). To manage the complexity of the system and to streamline the development process, we show how most of the software, electronics and systems engineering aspects of the control system have been modeled as a set of scripts written in a Domain Specific Language (DSL). When executed, these scripts populate a Knowledge Base (KB) which can be queried to retrieve specific information. By feeding the results of those queries to a template system, we were able to generate very detailed "browsable" web-based documentation about the system, but also PLC software code, Python client code, model verification reports, etc. The aim of this paper is to

Assessing an AI knowledge-base for asymptomatic liver diseases.

PubMed

Babic, A; Mathiesen, U; Hedin, K; Bodemar, G; Wigertz, O

1998-01-01

Discovering not yet seen knowledge from clinical data is of importance in the field of asymptomatic liver diseases. Avoidance of liver biopsy which is used as the ultimate confirmation of diagnosis by making the decision based on relevant laboratory findings only, would be considered an essential support. The system based on Quinlan's ID3 algorithm was simple and efficient in extracting the sought knowledge. Basic principles of applying the AI systems are therefore described and complemented with medical evaluation. Some of the diagnostic rules were found to be useful as decision algorithms i.e. they could be directly applied in clinical work and made a part of the knowledge-base of the Liver Guide, an automated decision support system.

Knowledge based systems for intelligent robotics

NASA Technical Reports Server (NTRS)

Rajaram, N. S.

1982-01-01

It is pointed out that the construction of large space platforms, such as space stations, has to be carried out in the outer space environment. As it is extremely expensive to support human workers in space for large periods, the only feasible solution appears to be related to the development and deployment of highly capable robots for most of the tasks. Robots for space applications will have to possess characteristics which are very different from those needed by robots in industry. The present investigation is concerned with the needs of space robotics and the technologies which can be of assistance to meet these needs, giving particular attention to knowledge bases. 'Intelligent' robots are required for the solution of arising problems. The collection of facts and rules needed for accomplishing such solutions form the 'knowledge base' of the system.

An Ebola virus-centered knowledge base

PubMed Central

Kamdar, Maulik R.; Dumontier, Michel

2015-01-01

Ebola virus (EBOV), of the family Filoviridae viruses, is a NIAID category A, lethal human pathogen. It is responsible for causing Ebola virus disease (EVD) that is a severe hemorrhagic fever and has a cumulative death rate of 41% in the ongoing epidemic in West Africa. There is an ever-increasing need to consolidate and make available all the knowledge that we possess on EBOV, even if it is conflicting or incomplete. This would enable biomedical researchers to understand the molecular mechanisms underlying this disease and help develop tools for efficient diagnosis and effective treatment. In this article, we present our approach for the development of an Ebola virus-centered Knowledge Base (Ebola-KB) using Linked Data and Semantic Web Technologies. We retrieve and aggregate knowledge from several open data sources, web services and biomedical ontologies. This knowledge is transformed to RDF, linked to the Bio2RDF datasets and made available through a SPARQL 1.1 Endpoint. Ebola-KB can also be explored using an interactive Dashboard visualizing the different perspectives of this integrated knowledge. We showcase how different competency questions, asked by domain users researching the druggability of EBOV, can be formulated as SPARQL Queries or answered using the Ebola-KB Dashboard. Database URL: http://ebola.semanticscience.org. PMID:26055098

An Ebola virus-centered knowledge base.

PubMed

Kamdar, Maulik R; Dumontier, Michel

2015-01-01

Ebola virus (EBOV), of the family Filoviridae viruses, is a NIAID category A, lethal human pathogen. It is responsible for causing Ebola virus disease (EVD) that is a severe hemorrhagic fever and has a cumulative death rate of 41% in the ongoing epidemic in West Africa. There is an ever-increasing need to consolidate and make available all the knowledge that we possess on EBOV, even if it is conflicting or incomplete. This would enable biomedical researchers to understand the molecular mechanisms underlying this disease and help develop tools for efficient diagnosis and effective treatment. In this article, we present our approach for the development of an Ebola virus-centered Knowledge Base (Ebola-KB) using Linked Data and Semantic Web Technologies. We retrieve and aggregate knowledge from several open data sources, web services and biomedical ontologies. This knowledge is transformed to RDF, linked to the Bio2RDF datasets and made available through a SPARQL 1.1 Endpoint. Ebola-KB can also be explored using an interactive Dashboard visualizing the different perspectives of this integrated knowledge. We showcase how different competency questions, asked by domain users researching the druggability of EBOV, can be formulated as SPARQL Queries or answered using the Ebola-KB Dashboard. © The Author(s) 2015. Published by Oxford University Press.

PDA: A coupling of knowledge and memory for case-based reasoning

NASA Technical Reports Server (NTRS)

Bharwani, S.; Walls, J.; Blevins, E.

1988-01-01

Problem solving in most domains requires reference to past knowledge and experience whether such knowledge is represented as rules, decision trees, networks or any variant of attributed graphs. Regardless of the representational form employed, designers of expert systems rarely make a distinction between the static and dynamic aspects of the system's knowledge base. The current paper clearly distinguishes between knowledge-based and memory-based reasoning where the former in its most pure sense is characterized by a static knowledge based resulting in a relatively brittle expert system while the latter is dynamic and analogous to the functions of human memory which learns from experience. The paper discusses the design of an advisory system which combines a knowledge base consisting of domain vocabulary and default dependencies between concepts with a dynamic conceptual memory which stores experimental knowledge in the form of cases. The case memory organizes past experience in the form of MOPs (memory organization packets) and sub-MOPs. Each MOP consists of a context frame and a set of indices. The context frame contains information about the features (norms) common to all the events and sub-MOPs indexed under it.

Knowledge-based systems in Japan

NASA Technical Reports Server (NTRS)

Feigenbaum, Edward; Engelmore, Robert S.; Friedland, Peter E.; Johnson, Bruce B.; Nii, H. Penny; Schorr, Herbert; Shrobe, Howard

1994-01-01

This report summarizes a study of the state-of-the-art in knowledge-based systems technology in Japan, organized by the Japanese Technology Evaluation Center (JTEC) under the sponsorship of the National Science Foundation and the Advanced Research Projects Agency. The panel visited 19 Japanese sites in March 1992. Based on these site visits plus other interactions with Japanese organizations, both before and after the site visits, the panel prepared a draft final report. JTEC sent the draft to the host organizations for their review. The final report was published in May 1993.

Knowledge modeling tool for evidence-based design.

PubMed

Durmisevic, Sanja; Ciftcioglu, Ozer

2010-01-01

The aim of this study is to take evidence-based design (EBD) to the next level by activating available knowledge, integrating new knowledge, and combining them for more efficient use by the planning and design community. This article outlines a framework for a performance-based measurement tool that can provide the necessary decision support during the design or evaluation of a healthcare environment by estimating the overall design performance of multiple variables. New knowledge in EBD adds continuously to complexity (the "information explosion"), and it becomes impossible to consider all aspects (design features) at the same time, much less their impact on final building performance. How can existing knowledge and the information explosion in healthcare-specifically the domain of EBD-be rendered manageable? Is it feasible to create a computational model that considers many design features and deals with them in an integrated way, rather than one at a time? The found evidence is structured and readied for computation through a "fuzzification" process. The weights are calculated using an analytical hierarchy process. Actual knowledge modeling is accomplished through a fuzzy neural tree structure. The impact of all inputs on the outcome-in this case, patient recovery-is calculated using sensitivity analysis. Finally, the added value of the model is discussed using a hypothetical case study of a patient room. The proposed model can deal with the complexities of various aspects and the relationships among variables in a coordinated way, allowing existing and new pieces of evidence to be integrated in a knowledge tree structure that facilitates understanding of the effects of various design interventions on overall design performance.

A collaborative filtering-based approach to biomedical knowledge discovery.

PubMed

Lever, Jake; Gakkhar, Sitanshu; Gottlieb, Michael; Rashnavadi, Tahereh; Lin, Santina; Siu, Celia; Smith, Maia; Jones, Martin R; Krzywinski, Martin; Jones, Steven J M; Wren, Jonathan

2018-02-15

The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers. We propose an approach based on the singular value decomposition (SVD) that is able to combine data from across the knowledge graph through a reduced representation. Using cooccurrence data extracted from published literature, we show that SVD performs better than the leading methods for scoring discoveries. We also show the diminishing predictive power of knowledge discovery as we compare our predictions with real associations that appear further into the future. Finally, we examine the strengths and weaknesses of the SVD approach against another well-performing system using several predicted associations. All code and results files for this analysis can be accessed at https://github.com/jakelever/knowledgediscovery. sjones@bcgsc.ca. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

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.

A Text Knowledge Base from the AI Handbook.

ERIC Educational Resources Information Center

Simmons, Robert F.

1987-01-01

Describes a prototype natural language text knowledge system (TKS) that was used to organize 50 pages of a handbook on artificial intelligence as an inferential knowledge base with natural language query and command capabilities. Representation of text, database navigation, query systems, discourse structuring, and future research needs are…

Satellite Contamination and Materials Outgassing Knowledge base

NASA Technical Reports Server (NTRS)

Minor, Jody L.; Kauffman, William J. (Technical Monitor)

2001-01-01

Satellite contamination continues to be a design problem that engineers must take into account when developing new satellites. To help with this issue, NASA's Space Environments and Effects (SEE) Program funded the development of the Satellite Contamination and Materials Outgassing Knowledge base. This engineering tool brings together in one location information about the outgassing properties of aerospace materials based upon ground-testing data, the effects of outgassing that has been observed during flight and measurements of the contamination environment by on-orbit instruments. The knowledge base contains information using the ASTM Standard E- 1559 and also consolidates data from missions using quartz-crystal microbalances (QCM's). The data contained in the knowledge base was shared with NASA by government agencies and industry in the US and international space agencies as well. The term 'knowledgebase' was used because so much information and capability was brought together in one comprehensive engineering design tool. It is the SEE Program's intent to continually add additional material contamination data as it becomes available - creating a dynamic tool whose value to the user is ever increasing. The SEE Program firmly believes that NASA, and ultimately the entire contamination user community, will greatly benefit from this new engineering tool and highly encourages the community to not only use the tool but add data to it as well.

Knowledge-based imaging-sensor fusion system

NASA Technical Reports Server (NTRS)

Westrom, George

1989-01-01

An imaging system which applies knowledge-based technology to supervise and control both sensor hardware and computation in the imaging system is described. It includes the development of an imaging system breadboard which brings together into one system work that we and others have pursued for LaRC for several years. The goal is to combine Digital Signal Processing (DSP) with Knowledge-Based Processing and also include Neural Net processing. The system is considered a smart camera. Imagine that there is a microgravity experiment on-board Space Station Freedom with a high frame rate, high resolution camera. All the data cannot possibly be acquired from a laboratory on Earth. In fact, only a small fraction of the data will be received. Again, imagine being responsible for some experiments on Mars with the Mars Rover: the data rate is a few kilobits per second for data from several sensors and instruments. Would it not be preferable to have a smart system which would have some human knowledge and yet follow some instructions and attempt to make the best use of the limited bandwidth for transmission. The system concept, current status of the breadboard system and some recent experiments at the Mars-like Amboy Lava Fields in California are discussed.

Semantic attributes based texture generation

NASA Astrophysics Data System (ADS)

Chi, Huifang; Gan, Yanhai; Qi, Lin; Dong, Junyu; Madessa, Amanuel Hirpa

2018-04-01

Semantic attributes are commonly used for texture description. They can be used to describe the information of a texture, such as patterns, textons, distributions, brightness, and so on. Generally speaking, semantic attributes are more concrete descriptors than perceptual features. Therefore, it is practical to generate texture images from semantic attributes. In this paper, we propose to generate high-quality texture images from semantic attributes. Over the last two decades, several works have been done on texture synthesis and generation. Most of them focusing on example-based texture synthesis and procedural texture generation. Semantic attributes based texture generation still deserves more devotion. Gan et al. proposed a useful joint model for perception driven texture generation. However, perceptual features are nonobjective spatial statistics used by humans to distinguish different textures in pre-attentive situations. To give more describing information about texture appearance, semantic attributes which are more in line with human description habits are desired. In this paper, we use sigmoid cross entropy loss in an auxiliary model to provide enough information for a generator. Consequently, the discriminator is released from the relatively intractable mission of figuring out the joint distribution of condition vectors and samples. To demonstrate the validity of our method, we compare our method to Gan et al.'s method on generating textures by designing experiments on PTD and DTD. All experimental results show that our model can generate textures from semantic attributes.

An Analysis of Three Different Approaches to Student Teacher Mentoring and Their Impact on Knowledge Generation in Practicum Settings

ERIC Educational Resources Information Center

Mena, Juanjo; García, Marisa; Clarke, Anthony; Barkatsas, Anastasios

2016-01-01

Mentoring in Teacher Education is a key component in the professional development of student teachers. However, little research focuses on the knowledge shared and generated in mentoring conversations. In this paper, we explore the knowledge student teachers articulate in mentoring conversations under three different post-lesson approaches to…

The adverse outcome pathway knowledge base

EPA Science Inventory

The rapid advancement of the Adverse Outcome Pathway (AOP) framework has been paralleled by the development of tools to store, analyse, and explore AOPs. The AOP Knowledge Base (AOP-KB) project has brought three independently developed platforms (Effectopedia, AOP-Wiki, and AOP-X...

Online Knowledge-Based Model for Big Data Topic Extraction.

PubMed

Khan, Muhammad Taimoor; Durrani, Mehr; Khalid, Shehzad; Aziz, Furqan

2016-01-01

Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half.

Formalization of the engineering science discipline - knowledge engineering

NASA Astrophysics Data System (ADS)

Peng, Xiao

Knowledge is the most precious ingredient facilitating aerospace engineering research and product development activities. Currently, the most common knowledge retention methods are paper-based documents, such as reports, books and journals. However, those media have innate weaknesses. For example, four generations of flying wing aircraft (Horten, Northrop XB-35/YB-49, Boeing BWB and many others) were mostly developed in isolation. The subsequent engineers were not aware of the previous developments, because these projects were documented such which prevented the next generation of engineers to benefit from the previous lessons learned. In this manner, inefficient knowledge retention methods have become a primary obstacle for knowledge transfer from the experienced to the next generation of engineers. In addition, the quality of knowledge itself is a vital criterion; thus, an accurate measure of the quality of 'knowledge' is required. Although qualitative knowledge evaluation criteria have been researched in other disciplines, such as the AAA criterion by Ernest Sosa stemming from the field of philosophy, a quantitative knowledge evaluation criterion needs to be developed which is capable to numerically determine the qualities of knowledge for aerospace engineering research and product development activities. To provide engineers with a high-quality knowledge management tool, the engineering science discipline Knowledge Engineering has been formalized to systematically address knowledge retention issues. This research undertaking formalizes Knowledge Engineering as follows: 1. Categorize knowledge according to its formats and representations for the first time, which serves as the foundation for the subsequent knowledge management function development. 2. Develop an efficiency evaluation criterion for knowledge management by analyzing the characteristics of both knowledge and the parties involved in the knowledge management processes. 3. Propose and develop an

Arranging ISO 13606 archetypes into a knowledge base.

PubMed

Kopanitsa, Georgy

2014-01-01

To enable the efficient reuse of standard based medical data we propose to develop a higher level information model that will complement the archetype model of ISO 13606. This model will make use of the relationships that are specified in UML to connect medical archetypes into a knowledge base within a repository. UML connectors were analyzed for their ability to be applied in the implementation of a higher level model that will establish relationships between archetypes. An information model was developed using XML Schema notation. The model allows linking different archetypes of one repository into a knowledge base. Presently it supports several relationships and will be advanced in future.

Knowledge acquisition for case-based reasoning systems

NASA Technical Reports Server (NTRS)

Riesbeck, Christopher K.

1988-01-01

Case-based reasoning (CBR) is a simple idea: solve new problems by adapting old solutions to similar problems. The CBR approach offers several potential advantages over rule-based reasoning: rules are not combined blindly in a search for solutions, solutions can be explained in terms of concrete examples, and performance can improve automatically as new problems are solved and added to the case library. Moving CBR for the university research environment to the real world requires smooth interfaces for getting knowledge from experts. Described are the basic elements of an interface for acquiring three basic bodies of knowledge that any case-based reasoner requires: the case library of problems and their solutions, the analysis rules that flesh out input problem specifications so that relevant cases can be retrieved, and the adaptation rules that adjust old solutions to fit new problems.

The BioHub Knowledge Base: Ontology and Repository for Sustainable Biosourcing.

PubMed

Read, Warren J; Demetriou, George; Nenadic, Goran; Ruddock, Noel; Stevens, Robert; Winter, Jerry

2016-06-01

The motivation for the BioHub project is to create an Integrated Knowledge Management System (IKMS) that will enable chemists to source ingredients from bio-renewables, rather than from non-sustainable sources such as fossil oil and its derivatives. The BioHubKB is the data repository of the IKMS; it employs Semantic Web technologies, especially OWL, to host data about chemical transformations, bio-renewable feedstocks, co-product streams and their chemical components. Access to this knowledge base is provided to other modules within the IKMS through a set of RESTful web services, driven by SPARQL queries to a Sesame back-end. The BioHubKB re-uses several bio-ontologies and bespoke extensions, primarily for chemical feedstocks and products, to form its knowledge organisation schema. Parts of plants form feedstocks, while various processes generate co-product streams that contain certain chemicals. Both chemicals and transformations are associated with certain qualities, which the BioHubKB also attempts to capture. Of immediate commercial and industrial importance is to estimate the cost of particular sets of chemical transformations (leading to candidate surfactants) performed in sequence, and these costs too are captured. Data are sourced from companies' internal knowledge and document stores, and from the publicly available literature. Both text analytics and manual curation play their part in populating the ontology. We describe the prototype IKMS, the BioHubKB and the services that it supports for the IKMS. The BioHubKB can be found via http://biohub.cs.manchester.ac.uk/ontology/biohub-kb.owl .

Mathematics/Arithmetic Knowledge-Based Way of Thinking and Its Maintenance Needed for Engineers

NASA Astrophysics Data System (ADS)

Harada, Shoji

Examining curriculum among universities revealed that no significant difference in math class or related subjects can be seen. However, amount and depth of those studies, in general, differed depending on content of curriculum and the level of achievement at entrance to individual university. Universalization of higher education shows that students have many problems in learning higher level of traditional math and that the memory of math they learned quickly fades away after passing in exam. It means that further development of higher math knowledgebased engineer after graduation from universities. Under these circumstances, the present author, as one of fun of math, propose how to maintain way of thinking generated by math knowledge. What necessary for engineer is to pay attention to common books, dealing with elementary mathematics or arithmetic- related matters. This surely leads engineer to nourish math/arithmetic knowledge-based way of thinking.

English Learners' Knowledge of Prepositions: Collocational Knowledge or Knowledge Based on Meaning?

ERIC Educational Resources Information Center

Mueller, Charles M.

2011-01-01

Second language (L2) learners' successful performance in an L2 can be partly attributed to their knowledge of collocations. In some cases, this knowledge is accompanied by knowledge of the semantic and/or grammatical patterns that motivate the collocation. At other times, collocational knowledge may serve a compensatory role. To determine the…

Knowledge-based computer systems for radiotherapy planning.

PubMed

Kalet, I J; Paluszynski, W

1990-08-01

Radiation therapy is one of the first areas of clinical medicine to utilize computers in support of routine clinical decision making. The role of the computer has evolved from simple dose calculations to elaborate interactive graphic three-dimensional simulations. These simulations can combine external irradiation from megavoltage photons, electrons, and particle beams with interstitial and intracavitary sources. With the flexibility and power of modern radiotherapy equipment and the ability of computer programs that simulate anything the machinery can do, we now face a challenge to utilize this capability to design more effective radiation treatments. How can we manage the increased complexity of sophisticated treatment planning? A promising approach will be to use artificial intelligence techniques to systematize our present knowledge about design of treatment plans, and to provide a framework for developing new treatment strategies. Far from replacing the physician, physicist, or dosimetrist, artificial intelligence-based software tools can assist the treatment planning team in producing more powerful and effective treatment plans. Research in progress using knowledge-based (AI) programming in treatment planning already has indicated the usefulness of such concepts as rule-based reasoning, hierarchical organization of knowledge, and reasoning from prototypes. Problems to be solved include how to handle continuously varying parameters and how to evaluate plans in order to direct improvements.

Model-based reasoning for system and software engineering: The Knowledge From Pictures (KFP) environment

NASA Technical Reports Server (NTRS)

Bailin, Sydney; Paterra, Frank; Henderson, Scott; Truszkowski, Walt

1993-01-01

This paper presents a discussion of current work in the area of graphical modeling and model-based reasoning being undertaken by the Automation Technology Section, Code 522.3, at Goddard. The work was initially motivated by the growing realization that the knowledge acquisition process was a major bottleneck in the generation of fault detection, isolation, and repair (FDIR) systems for application in automated Mission Operations. As with most research activities this work started out with a simple objective: to develop a proof-of-concept system demonstrating that a draft rule-base for a FDIR system could be automatically realized by reasoning from a graphical representation of the system to be monitored. This work was called Knowledge From Pictures (KFP) (Truszkowski et. al. 1992). As the work has successfully progressed the KFP tool has become an environment populated by a set of tools that support a more comprehensive approach to model-based reasoning. This paper continues by giving an overview of the graphical modeling objectives of the work, describing the three tools that now populate the KFP environment, briefly presenting a discussion of related work in the field, and by indicating future directions for the KFP environment.

The Knowledge-Based Economy and E-Learning: Critical Considerations for Workplace Democracy

ERIC Educational Resources Information Center

Remtulla, Karim A.

2007-01-01

The ideological shift by nation-states to "a knowledge-based economy" (also referred to as "knowledge-based society") is causing changes in the workplace. Brought about by the forces of globalisation and technological innovation, the ideologies of the "knowledge-based economy" are not limited to influencing the…

Employee knowledge of value-based insurance design benefits.

PubMed

Henrikson, Nora B; Anderson, Melissa L; Hubbard, Rebecca A; Fishman, Paul; Grossman, David C

2014-08-01

Value-based insurance designs (VBD) incorporate evidence-based medicine into health benefit design. Consumer knowledge of new VBD benefits is important to assessing their impact on health care use. To assess knowledge of features of a VBD. The eligible study population was employees receiving healthcare benefits in an integrated care system in the U.S. Pacific Northwest. In 2010, participants completed a web-based survey 2 months after rollout of the plan, including three true/false questions about benefit design features including copays for preventive care visits and chronic disease medications and premium costs. Analysis was completed in 2012. Knowledgeable was defined as correct response to all three questions; self-reported knowledge was also assessed. A total of 3,463 people completed the survey (response rate=71.7%). The majority of respondents were female (80.1%) Caucasians (79.6%) aged 35-64 years (79.0%), reflecting the overall employee population. A total of 45.7% had at least a 4-year college education, and 69.1% were married. About three quarters of respondents correctly answered each individual question; half (52.1%) of respondents answered all three questions correctly. On multivariate analysis, knowledge was independently associated with female gender (OR=1.80, 95% CI=1.40, 2.31); Caucasian race (OR=1.72, 95% CI=1.28, 2.32); increasing household income (OR for ≥$100,000=1.86, 95% CI=1.29, 2.68); nonunion job status (OR compared to union status=1.63, 95% CI=1.17, 2.26); and high satisfaction with the health plan (OR compared to low satisfaction=1.26; 95% CI=1.00, 1.57). Incomplete knowledge of benefits is prevalent in an employee population soon after VBD rollout. Copyright © 2014 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Relating GTE and Knowledge-Based Courseware Engineering: Some Epistemological Issues.

ERIC Educational Resources Information Center

De Diana, Italo P. F.; Ladhani, Al-Noor

1998-01-01

Discusses GTE (Generic Tutoring Environment) and knowledge-based courseware engineering from an epistemological point of view and suggests some combination of the two approaches. Topics include intelligent tutoring; courseware authoring; application versus acquisition of knowledge; and domain knowledge. (LRW)

An American knowledge base in England - Alternate implementations of an expert system flight status monitor

NASA Technical Reports Server (NTRS)

Butler, G. F.; Graves, A. T.; Disbrow, J. D.; Duke, E. L.

1989-01-01

A joint activity between the Dryden Flight Research Facility of the NASA Ames Research Center (Ames-Dryden) and the Royal Aerospace Establishment (RAE) on knowledge-based systems has been agreed. Under the agreement, a flight status monitor knowledge base developed at Ames-Dryden has been implemented using the real-time AI (artificial intelligence) toolkit MUSE, which was developed in the UK. Here, the background to the cooperation is described and the details of the flight status monitor and a prototype MUSE implementation are presented. It is noted that the capabilities of the expert-system flight status monitor to monitor data downlinked from the flight test aircraft and to generate information on the state and health of the system for the test engineers provides increased safety during flight testing of new systems. Furthermore, the expert-system flight status monitor provides the systems engineers with ready access to the large amount of information required to describe a complex aircraft system.

A Map-Based Service Supporting Different Types of Geographic Knowledge for the Public

PubMed Central

Zhou, Mengjie; Wang, Rui; Tian, Jing; Ye, Ning; Mai, Shumin

2016-01-01

The internet enables the rapid and easy creation, storage, and transfer of knowledge; however, services that transfer geographic knowledge and facilitate the public understanding of geographic knowledge are still underdeveloped to date. Existing online maps (or atlases) can support limited types of geographic knowledge. In this study, we propose a framework for map-based services to represent and transfer different types of geographic knowledge to the public. A map-based service provides tools to ensure the effective transfer of geographic knowledge. We discuss the types of geographic knowledge that should be represented and transferred to the public, and we propose guidelines and a method to represent various types of knowledge through a map-based service. To facilitate the effective transfer of geographic knowledge, tools such as auxiliary background knowledge and auxiliary map-reading tools are provided through interactions with maps. An experiment conducted to illustrate our idea and to evaluate the usefulness of the map-based service is described; the results demonstrate that the map-based service is useful for transferring different types of geographic knowledge. PMID:27045314

A Map-Based Service Supporting Different Types of Geographic Knowledge for the Public.

PubMed

Zhou, Mengjie; Wang, Rui; Tian, Jing; Ye, Ning; Mai, Shumin

2016-01-01

The internet enables the rapid and easy creation, storage, and transfer of knowledge; however, services that transfer geographic knowledge and facilitate the public understanding of geographic knowledge are still underdeveloped to date. Existing online maps (or atlases) can support limited types of geographic knowledge. In this study, we propose a framework for map-based services to represent and transfer different types of geographic knowledge to the public. A map-based service provides tools to ensure the effective transfer of geographic knowledge. We discuss the types of geographic knowledge that should be represented and transferred to the public, and we propose guidelines and a method to represent various types of knowledge through a map-based service. To facilitate the effective transfer of geographic knowledge, tools such as auxiliary background knowledge and auxiliary map-reading tools are provided through interactions with maps. An experiment conducted to illustrate our idea and to evaluate the usefulness of the map-based service is described; the results demonstrate that the map-based service is useful for transferring different types of geographic knowledge.

The Influence of Self-Regulated Learning and Prior Knowledge on Knowledge Acquisition in Computer-Based Learning Environments

ERIC Educational Resources Information Center

Bernacki, Matthew

2010-01-01

This study examined how learners construct textbase and situation model knowledge in hypertext computer-based learning environments (CBLEs) and documented the influence of specific self-regulated learning (SRL) tactics, prior knowledge, and characteristics of the learner on posttest knowledge scores from exposure to a hypertext. A sample of 160…

Malaysia Transitions toward a Knowledge-Based Economy

ERIC Educational Resources Information Center

Mustapha, Ramlee; Abdullah, Abu

2004-01-01

The emergence of a knowledge-based economy (k-economy) has spawned a "new" notion of workplace literacy, changing the relationship between employers and employees. The traditional covenant where employees expect a stable or lifelong employment will no longer apply. The retention of employees will most probably be based on their skills…

A knowledge-driven approach to biomedical document conceptualization.

PubMed

Zheng, Hai-Tao; Borchert, Charles; Jiang, Yong

2010-06-01

Biomedical document conceptualization is the process of clustering biomedical documents based on ontology-represented domain knowledge. The result of this process is the representation of the biomedical documents by a set of key concepts and their relationships. Most of clustering methods cluster documents based on invariant domain knowledge. The objective of this work is to develop an effective method to cluster biomedical documents based on various user-specified ontologies, so that users can exploit the concept structures of documents more effectively. We develop a flexible framework to allow users to specify the knowledge bases, in the form of ontologies. Based on the user-specified ontologies, we develop a key concept induction algorithm, which uses latent semantic analysis to identify key concepts and cluster documents. A corpus-related ontology generation algorithm is developed to generate the concept structures of documents. Based on two biomedical datasets, we evaluate the proposed method and five other clustering algorithms. The clustering results of the proposed method outperform the five other algorithms, in terms of key concept identification. With respect to the first biomedical dataset, our method has the F-measure values 0.7294 and 0.5294 based on the MeSH ontology and gene ontology (GO), respectively. With respect to the second biomedical dataset, our method has the F-measure values 0.6751 and 0.6746 based on the MeSH ontology and GO, respectively. Both results outperforms the five other algorithms in terms of F-measure. Based on the MeSH ontology and GO, the generated corpus-related ontologies show informative conceptual structures. The proposed method enables users to specify the domain knowledge to exploit the conceptual structures of biomedical document collections. In addition, the proposed method is able to extract the key concepts and cluster the documents with a relatively high precision. Copyright 2010 Elsevier B.V. All rights reserved.

Improved knowledge diffusion model based on the collaboration hypernetwork

NASA Astrophysics Data System (ADS)

Wang, Jiang-Pan; Guo, Qiang; Yang, Guang-Yong; Liu, Jian-Guo

2015-06-01

The process for absorbing knowledge becomes an essential element for innovation in firms and in adapting to changes in the competitive environment. In this paper, we present an improved knowledge diffusion hypernetwork (IKDH) model based on the idea that knowledge will spread from the target node to all its neighbors in terms of the hyperedge and knowledge stock. We apply the average knowledge stock V(t) , the variable σ2(t) , and the variance coefficient c(t) to evaluate the performance of knowledge diffusion. By analyzing different knowledge diffusion ways, selection ways of the highly knowledgeable nodes, hypernetwork sizes and hypernetwork structures for the performance of knowledge diffusion, results show that the diffusion speed of IKDH model is 3.64 times faster than that of traditional knowledge diffusion (TKDH) model. Besides, it is three times faster to diffuse knowledge by randomly selecting "expert" nodes than that by selecting large-hyperdegree nodes as "expert" nodes. Furthermore, either the closer network structure or smaller network size results in the faster knowledge diffusion.

A protein relational database and protein family knowledge bases to facilitate structure-based design analyses.

PubMed

Mobilio, Dominick; Walker, Gary; Brooijmans, Natasja; Nilakantan, Ramaswamy; Denny, R Aldrin; Dejoannis, Jason; Feyfant, Eric; Kowticwar, Rupesh K; Mankala, Jyoti; Palli, Satish; Punyamantula, Sairam; Tatipally, Maneesh; John, Reji K; Humblet, Christine

2010-08-01

The Protein Data Bank is the most comprehensive source of experimental macromolecular structures. It can, however, be difficult at times to locate relevant structures with the Protein Data Bank search interface. This is particularly true when searching for complexes containing specific interactions between protein and ligand atoms. Moreover, searching within a family of proteins can be tedious. For example, one cannot search for some conserved residue as residue numbers vary across structures. We describe herein three databases, Protein Relational Database, Kinase Knowledge Base, and Matrix Metalloproteinase Knowledge Base, containing protein structures from the Protein Data Bank. In Protein Relational Database, atom-atom distances between protein and ligand have been precalculated allowing for millisecond retrieval based on atom identity and distance constraints. Ring centroids, centroid-centroid and centroid-atom distances and angles have also been included permitting queries for pi-stacking interactions and other structural motifs involving rings. Other geometric features can be searched through the inclusion of residue pair and triplet distances. In Kinase Knowledge Base and Matrix Metalloproteinase Knowledge Base, the catalytic domains have been aligned into common residue numbering schemes. Thus, by searching across Protein Relational Database and Kinase Knowledge Base, one can easily retrieve structures wherein, for example, a ligand of interest is making contact with the gatekeeper residue.

Using Knowledge-Based Systems to Support Learning of Organizational Knowledge: A Case Study

NASA Technical Reports Server (NTRS)

Cooper, Lynne P.; Nash, Rebecca L.; Phan, Tu-Anh T.; Bailey, Teresa R.

2003-01-01

This paper describes the deployment of a knowledge system to support learning of organizational knowledge at the Jet Propulsion Laboratory (JPL), a US national research laboratory whose mission is planetary exploration and to 'do what no one has done before.' Data collected over 19 weeks of operation were used to assess system performance with respect to design considerations, participation, effectiveness of communication mechanisms, and individual-based learning. These results are discussed in the context of organizational learning research and implications for practice.

Predicting links based on knowledge dissemination in complex network

NASA Astrophysics Data System (ADS)

Zhou, Wen; Jia, Yifan

2017-04-01

Link prediction is the task of mining the missing links in networks or predicting the next vertex pair to be connected by a link. A lot of link prediction methods were inspired by evolutionary processes of networks. In this paper, a new mechanism for the formation of complex networks called knowledge dissemination (KD) is proposed with the assumption of knowledge disseminating through the paths of a network. Accordingly, a new link prediction method-knowledge dissemination based link prediction (KDLP)-is proposed to test KD. KDLP characterizes vertex similarity based on knowledge quantity (KQ) which measures the importance of a vertex through H-index. Extensive numerical simulations on six real-world networks demonstrate that KDLP is a strong link prediction method which performs at a higher prediction accuracy than four well-known similarity measures including common neighbors, local path index, average commute time and matrix forest index. Furthermore, based on the common conclusion that an excellent link prediction method reveals a good evolving mechanism, the experiment results suggest that KD is a considerable network evolving mechanism for the formation of complex networks.

Knowledge modeling of coal mining equipments based on ontology

NASA Astrophysics Data System (ADS)

Zhang, Baolong; Wang, Xiangqian; Li, Huizong; Jiang, Miaomiao

2017-06-01

The problems of information redundancy and sharing are universe in coal mining equipment management. In order to improve the using efficiency of knowledge of coal mining equipments, this paper proposed a new method of knowledge modeling based on ontology. On the basis of analyzing the structures and internal relations of coal mining equipment knowledge, taking OWL as ontology construct language, the ontology model of coal mining equipment knowledge is built with the help of Protégé 4.3 software tools. The knowledge description method will lay the foundation for the high effective knowledge management and sharing, which is very significant for improving the production management level of coal mining enterprises.

Developing a kidney and urinary pathway knowledge base

PubMed Central

2011-01-01

Background Chronic renal disease is a global health problem. The identification of suitable biomarkers could facilitate early detection and diagnosis and allow better understanding of the underlying pathology. One of the challenges in meeting this goal is the necessary integration of experimental results from multiple biological levels for further analysis by data mining. Data integration in the life science is still a struggle, and many groups are looking to the benefits promised by the Semantic Web for data integration. Results We present a Semantic Web approach to developing a knowledge base that integrates data from high-throughput experiments on kidney and urine. A specialised KUP ontology is used to tie the various layers together, whilst background knowledge from external databases is incorporated by conversion into RDF. Using SPARQL as a query mechanism, we are able to query for proteins expressed in urine and place these back into the context of genes expressed in regions of the kidney. Conclusions The KUPKB gives KUP biologists the means to ask queries across many resources in order to aggregate knowledge that is necessary for answering biological questions. The Semantic Web technologies we use, together with the background knowledge from the domain’s ontologies, allows both rapid conversion and integration of this knowledge base. The KUPKB is still relatively small, but questions remain about scalability, maintenance and availability of the knowledge itself. Availability The KUPKB may be accessed via http://www.e-lico.eu/kupkb. PMID:21624162

Online Knowledge-Based Model for Big Data Topic Extraction

PubMed Central

Khan, Muhammad Taimoor; Durrani, Mehr; Khalid, Shehzad; Aziz, Furqan

2016-01-01

Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half. PMID:27195004

Irrelevance Reasoning in Knowledge Based Systems

NASA Technical Reports Server (NTRS)

Levy, A. Y.

1993-01-01

This dissertation considers the problem of reasoning about irrelevance of knowledge in a principled and efficient manner. Specifically, it is concerned with two key problems: (1) developing algorithms for automatically deciding what parts of a knowledge base are irrelevant to a query and (2) the utility of relevance reasoning. The dissertation describes a novel tool, the query-tree, for reasoning about irrelevance. Based on the query-tree, we develop several algorithms for deciding what formulas are irrelevant to a query. Our general framework sheds new light on the problem of detecting independence of queries from updates. We present new results that significantly extend previous work in this area. The framework also provides a setting in which to investigate the connection between the notion of irrelevance and the creation of abstractions. We propose a new approach to research on reasoning with abstractions, in which we investigate the properties of an abstraction by considering the irrelevance claims on which it is based. We demonstrate the potential of the approach for the cases of abstraction of predicates and projection of predicate arguments. Finally, we describe an application of relevance reasoning to the domain of modeling physical devices.

Autonomously Generating Operations Sequences for a Mars Rover Using Artificial Intelligence-Based Planning

NASA Astrophysics Data System (ADS)

Sherwood, R.; Mutz, D.; Estlin, T.; Chien, S.; Backes, P.; Norris, J.; Tran, D.; Cooper, B.; Rabideau, G.; Mishkin, A.; Maxwell, S.

2001-07-01

This article discusses a proof-of-concept prototype for ground-based automatic generation of validated rover command sequences from high-level science and engineering activities. This prototype is based on ASPEN, the Automated Scheduling and Planning Environment. This artificial intelligence (AI)-based planning and scheduling system will automatically generate a command sequence that will execute within resource constraints and satisfy flight rules. An automated planning and scheduling system encodes rover design knowledge and uses search and reasoning techniques to automatically generate low-level command sequences while respecting rover operability constraints, science and engineering preferences, environmental predictions, and also adhering to hard temporal constraints. This prototype planning system has been field-tested using the Rocky 7 rover at JPL and will be field-tested on more complex rovers to prove its effectiveness before transferring the technology to flight operations for an upcoming NASA mission. Enabling goal-driven commanding of planetary rovers greatly reduces the requirements for highly skilled rover engineering personnel. This in turn greatly reduces mission operations costs. In addition, goal-driven commanding permits a faster response to changes in rover state (e.g., faults) or science discoveries by removing the time-consuming manual sequence validation process, allowing rapid "what-if" analyses, and thus reducing overall cycle times.

Knowledge-Based Software Development Tools

DTIC Science & Technology

1993-09-01

GREEN, C., AND WESTFOLD, S. Knowledge-based programming self-applied. In Machine Intelligence 10, J. E. Hayes, D. Mitchie, and Y. Pao, Eds., Wiley...Technical Report KES.U.84.2, Kestrel Institute, April 1984. [181 KORF, R. E. Toward a model of representation changes. Artificial Intelligence 14, 1...Artificial Intelligence 27, 1 (February 1985), 43-96. Replinted in Readings in Artificial Intelligence and Software Engineering, C. Rich •ad R. Waters

Mapping and Managing Knowledge and Information in Resource-Based Learning

ERIC Educational Resources Information Center

Tergan, Sigmar-Olaf; Graber, Wolfgang; Neumann, Anja

2006-01-01

In resource-based learning scenarios, students are often overwhelmed by the complexity of task-relevant knowledge and information. Techniques for the external interactive representation of individual knowledge in graphical format may help them to cope with complex problem situations. Advanced computer-based concept-mapping tools have the potential…

Viewing Knowledge Bases as Qualitative Models.

ERIC Educational Resources Information Center

Clancey, William J.

The concept of a qualitative model provides a unifying perspective for understanding how expert systems differ from conventional programs. Knowledge bases contain qualitative models of systems in the world, that is, primarily non-numeric descriptions that provide a basis for explaining and predicting behavior and formulating action plans. The…

ISPE: A knowledge-based system for fluidization studies

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

Reddy, S.

1991-01-01

Chemical engineers use mathematical simulators to design, model, optimize and refine various engineering plants/processes. This procedure requires the following steps: (1) preparation of an input data file according to the format required by the target simulator; (2) excecuting the simulation; and (3) analyzing the results of the simulation to determine if all specified goals'' are satisfied. If the goals are not met, the input data file must be modified and the simulation repeated. This multistep process is continued until satisfactory results are obtained. This research was undertaken to develop a knowledge based system, IPSE (Intelligent Process Simulation Environment), that canmore » enhance the productivity of chemical engineers/modelers by serving as an intelligent assistant to perform a variety tasks related to process simulation. ASPEN, a widely used simulator by the US Department of Energy (DOE) at Morgantown Energy Technology Center (METC) was selected as the target process simulator in the project. IPSE, written in the C language, was developed using a number of knowledge-based programming paradigms: object-oriented knowledge representation that uses inheritance and methods, rulebased inferencing (includes processing and propagation of probabilistic information) and data-driven programming using demons. It was implemented using the knowledge based environment LASER. The relationship of IPSE with the user, ASPEN, LASER and the C language is shown in Figure 1.« less

PRAIS: Distributed, real-time knowledge-based systems made easy

NASA Technical Reports Server (NTRS)

Goldstein, David G.

1990-01-01

This paper discusses an architecture for real-time, distributed (parallel) knowledge-based systems called the Parallel Real-time Artificial Intelligence System (PRAIS). PRAIS strives for transparently parallelizing production (rule-based) systems, even when under real-time constraints. PRAIS accomplishes these goals by incorporating a dynamic task scheduler, operating system extensions for fact handling, and message-passing among multiple copies of CLIPS executing on a virtual blackboard. This distributed knowledge-based system tool uses the portability of CLIPS and common message-passing protocols to operate over a heterogeneous network of processors.

Using CLIPS in the domain of knowledge-based massively parallel programming

NASA Technical Reports Server (NTRS)

Dvorak, Jiri J.

1994-01-01

The Program Development Environment (PDE) is a tool for massively parallel programming of distributed-memory architectures. Adopting a knowledge-based approach, the PDE eliminates the complexity introduced by parallel hardware with distributed memory and offers complete transparency in respect of parallelism exploitation. The knowledge-based part of the PDE is realized in CLIPS. Its principal task is to find an efficient parallel realization of the application specified by the user in a comfortable, abstract, domain-oriented formalism. A large collection of fine-grain parallel algorithmic skeletons, represented as COOL objects in a tree hierarchy, contains the algorithmic knowledge. A hybrid knowledge base with rule modules and procedural parts, encoding expertise about application domain, parallel programming, software engineering, and parallel hardware, enables a high degree of automation in the software development process. In this paper, important aspects of the implementation of the PDE using CLIPS and COOL are shown, including the embedding of CLIPS with C++-based parts of the PDE. The appropriateness of the chosen approach and of the CLIPS language for knowledge-based software engineering are discussed.

Dynamic reasoning in a knowledge-based system

NASA Technical Reports Server (NTRS)

Rao, Anand S.; Foo, Norman Y.

1988-01-01

Any space based system, whether it is a robot arm assembling parts in space or an onboard system monitoring the space station, has to react to changes which cannot be foreseen. As a result, apart from having domain-specific knowledge as in current expert systems, a space based AI system should also have general principles of change. This paper presents a modal logic which can not only represent change but also reason with it. Three primitive operations, expansion, contraction and revision are introduced and axioms which specify how the knowledge base should change when the external world changes are also specified. Accordingly the notion of dynamic reasoning is introduced, which unlike the existing forms of reasoning, provide general principles of change. Dynamic reasoning is based on two main principles, namely minimize change and maximize coherence. A possible-world semantics which incorporates the above two principles is also discussed. The paper concludes by discussing how the dynamic reasoning system can be used to specify actions and hence form an integral part of an autonomous reasoning and planning system.

Construction of Expert Knowledge Monitoring and Assessment System Based on Integral Method of Knowledge Evaluation

ERIC Educational Resources Information Center

Golovachyova, Viktoriya N.; Menlibekova, Gulbakhyt Zh.; Abayeva, Nella F.; Ten, Tatyana L.; Kogaya, Galina D.

2016-01-01

Using computer-based monitoring systems that rely on tests could be the most effective way of knowledge evaluation. The problem of objective knowledge assessment by means of testing takes on a new dimension in the context of new paradigms in education. The analysis of the existing test methods enabled us to conclude that tests with selected…

Using open data in near real time disaster analysis and knowledge generation

NASA Astrophysics Data System (ADS)

She, Jun

2017-04-01

This presentation will address the value of using open operational geo data in near real time disaster analysis and knowledge generation. In the past, mechanism analysis of a meteo-hyrological extreme event may take month and years with lots of resources since there exist many kinds of restrictions on the model and observation data, e.g., in availability, accessibility, adequacy in resolution, quality and delivery time etc. In recent years, thanks to the open data and open service programs such as Copernicus, EMODnet (European Marine Observation Data Network) and data sharing activities in ROOSs (Regional Operational Oceanography Systems) and national agencies, the disaster analysis become a much faster and efficient procedure. The study will present such a case study for analyzing a hundred-year storm event in January 2017 which affects Danish and German coasts in western Baltic Sea. The event and its forecasts have caused lots of attention in Danish and German media. However, the explanations on how the storm surge is formed and why the prediction is good or bad in this or that country are still largely absent in the media reports. All the data and plots used in the analysis are from open sources. It is found that with the open data, the spatiotemporal variation and the internal links between weather, sea level and water mass movements can be well understood. New knowledge on key factors for the unusual high waters in the western Baltic is obtained from this analysis. Finally, recommendations for using open operational data in generating open science are given.

A knowledge management-based intranet: asset or EBM liability?

PubMed

Mimnagh, Christopher

2005-01-01

This paper summarises the presentation given at the British Computer Society Primary Health Care Specialist Group annual conference 2004. It outlines the four years of experience gained in implementing a knowledge management-based intranet across a local health community. Consideration is given to definitions of knowledge management and evidence-based medicine. The paper outlines the potential impacts and actual results over the four-year period, with reference to the wider issues involved.

Development of theory-based knowledge translation interventions to facilitate the implementation of evidence-based guidelines on the early management of adults with traumatic spinal cord injury.

PubMed

Bérubé, Mélanie; Albert, Martin; Chauny, Jean-Marc; Contandriopoulos, Damien; DuSablon, Anne; Lacroix, Sébastien; Gagné, Annick; Laflamme, Élise; Boutin, Nathalie; Delisle, Stéphane; Pauzé, Anne-Marie; MacThiong, Jean-Marc

2015-12-01

Optimal, early management following a spinal cord injury (SCI) can limit individuals' disabilities and costs related to their care. Several knowledge syntheses were recently published to guide health care professionals with regard to early interventions in SCI patients. However, no knowledge translation (KT) intervention, selected according to a behaviour change theory, has been proposed to facilitate the use of SCI guidelines in an acute care setting. To develop theory-informed KT interventions to promote the application of evidence-based recommendations on the acute care management of SCI patients. The first four phases of the knowledge-to-action model were used to establish the study design. Knowledge selection was based on the Grading of Recommendations Assessment, Development and Evaluation system. Knowledge adaptation to the local context was sourced from the ADAPTE process. The theoretical domains framework oriented the selection and development of the interventions based on an assessment of barriers and enablers to knowledge application. Twenty-nine recommendations were chosen and operationalized in measurable clinical indicators. Barriers related to knowledge, skills, perceived capacities, beliefs about consequences, social influences, and the environmental context and resources theoretical domains were identified. The mapping of behaviour change techniques associated with those barriers led to the development of an online educational curriculum, interdisciplinary clinical pathways as well as policies and procedures. This research project allowed us developing KT interventions according to a thorough behavioural change methodology. Exposure to the generated interventions will support health care professionals in providing the best care to SCI patients. © 2015 John Wiley & Sons, Ltd.

Generating structure from experience: A retrieval-based model of language processing.

PubMed

Johns, Brendan T; Jones, Michael N

2015-09-01

Standard theories of language generally assume that some abstraction of linguistic input is necessary to create higher level representations of linguistic structures (e.g., a grammar). However, the importance of individual experiences with language has recently been emphasized by both usage-based theories (Tomasello, 2003) and grounded and situated theories (e.g., Zwaan & Madden, 2005). Following the usage-based approach, we present a formal exemplar model that stores instances of sentences across a natural language corpus, applying recent advances from models of semantic memory. In this model, an exemplar memory is used to generate expectations about the future structure of sentences, using a mechanism for prediction in language processing (Altmann & Mirković, 2009). The model successfully captures a broad range of behavioral effects-reduced relative clause processing (Reali & Christiansen, 2007), the role of contextual constraint (Rayner & Well, 1996), and event knowledge activation (Ferretti, Kutas, & McRae, 2007), among others. We further demonstrate how perceptual knowledge could be integrated into this exemplar-based framework, with the goal of grounding language processing in perception. Finally, we illustrate how an exemplar memory system could have been used in the cultural evolution of language. The model provides evidence that an impressive amount of language processing may be bottom-up in nature, built on the storage and retrieval of individual linguistic experiences. (c) 2015 APA, all rights reserved).

The Latent Structure of Secure Base Script Knowledge

ERIC Educational Resources Information Center

Waters, Theodore E. A.; Fraley, R. Chris; Groh, Ashley M.; Steele, Ryan D.; Vaughn, Brian E.; Bost, Kelly K.; Veríssimo, Manuela; Coppola, Gabrielle; Roisman, Glenn I.

2015-01-01

There is increasing evidence that attachment representations abstracted from childhood experiences with primary caregivers are organized as a cognitive script describing secure base use and support (i.e., the "secure base script"). To date, however, the latent structure of secure base script knowledge has gone unexamined--this despite…

A Knowledge Based Approach to VLSI CAD

DTIC Science & Technology

1983-09-01

Avail-and/or Dist ISpecial L| OI. SEICURITY CLASIIrCATION OP THIS IPA.lErllm S Daene." A KNOwLEDE BASED APPROACH TO VLSI CAD’ Louis L Steinberg and...major issues lies in building up and managing the knowledge base of oesign expertise. We expect that, as with many recent expert systems, in order to

Knowledge/geometry-based Mobile Autonomous Robot Simulator (KMARS)

NASA Technical Reports Server (NTRS)

Cheng, Linfu; Mckendrick, John D.; Liu, Jeffrey

1990-01-01

Ongoing applied research is focused on developing guidance system for robot vehicles. Problems facing the basic research needed to support this development (e.g., scene understanding, real-time vision processing, etc.) are major impediments to progress. Due to the complexity and the unpredictable nature of a vehicle's area of operation, more advanced vehicle control systems must be able to learn about obstacles within the range of its sensor(s). A better understanding of the basic exploration process is needed to provide critical support to developers of both sensor systems and intelligent control systems which can be used in a wide spectrum of autonomous vehicles. Elcee Computek, Inc. has been working under contract to the Flight Dynamics Laboratory, Wright Research and Development Center, Wright-Patterson AFB, Ohio to develop a Knowledge/Geometry-based Mobile Autonomous Robot Simulator (KMARS). KMARS has two parts: a geometry base and a knowledge base. The knowledge base part of the system employs the expert-system shell CLIPS ('C' Language Integrated Production System) and necessary rules that control both the vehicle's use of an obstacle detecting sensor and the overall exploration process. The initial phase project has focused on the simulation of a point robot vehicle operating in a 2D environment.

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.

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...

A combined park management framework based on regulatory and behavioral strategies: use of visitors' knowledge to assess effectiveness.

PubMed

Papageorgiou, K

2001-07-01

In light of the increasing mandate for greater efficiency in conservation of natural reserves such as national parks, the present study suggests educational approaches as a tool to achieve conservation purposes. Currently, the management of human-wildlife interactions is dominated by regulatory strategies, but considerable potential exists for environmental education to enhance knowledge in the short run and to prompt attitude change in the long run. A framework for conservation based on both traditional regulatory- and behavior-oriented strategies was proposed whereby the level of knowledge that park visitors have acquired comprises an obvious outcome and establishes a basis upon which the effectiveness of regulatory- and behavior-based regimes could be assessed. The perceptions regarding park-related issues of two distinct visitor groups (locals and nonlocals) are summarized from a survey undertaken in Vikos-Aoos national park. The findings suggest a superficial knowledge for certain concepts but little profound understanding of the content of such concepts, indicating that knowledge-raising efforts should go a long way towards establishing a positive attitude for the resource. Visitors' poor knowledge of the park's operation regulation contest the efficiency of the presently dominant regulatory management regime. While geographical distances did not appear to significantly differentiate knowledge between the two groups, wilderness experience (as certified by visits to other parks) was proved to be an impetus for generating substantial learner interest in critical park issues among nonlocal visitors. School education and media were found to be significant knowledge providers.

Knowledge Sharing in an American Multinational Company Based in Malaysia

ERIC Educational Resources Information Center

Ling, Chen Wai; Sandhu, Manjit S.; Jain, Kamal Kishore

2009-01-01

Purpose: This paper seeks to examine the views of executives working in an American based multinational company (MNC) about knowledge sharing, barriers to knowledge sharing, and strategies to promote knowledge sharing. Design/methodology/approach: This study was carried out in phases. In the first phase, a topology of organizational mechanisms for…

Integrating knowledge generation with knowledge diffusion and utilization: a case study analysis of the Consortium for Applied Research and Evaluation in Mental Health.

PubMed

Vingilis, Evelyn; Hartford, Kathleen; Schrecker, Ted; Mitchell, Beth; Lent, Barbara; Bishop, Joan

2003-01-01

Knowledge diffusion and utilization (KDU) have become a key focus in the health research community because of the limited success to date of research findings to inform health policies, programs and services. Yet, evidence indicates that successful KDU is often predicated on the early involvement of potential knowledge users in the conceptualization and conduct of the research and on the development of a "partnership culture". This study describes the integration of KDU theory with practice via a case study analysis of the Consortium for Applied Research and Evaluation in Mental Health (CAREMH). This qualitative study, using a single-case design, included a number of data sources: proposals, meeting minutes, presentations, publications, reports and curricula vitae of CAREMH members. CAREMH has adopted the following operational strategies to increase KDU capacity: 1) viewing research as a means and not as an end; 2) bringing the university and researcher to the community; 3) using participatory research methods; 4) embracing transdisciplinary research and interactions; and 5) using connectors. Examples of the iterative process between researchers and potential knowledge users in their contribution to knowledge generation, diffusion and utilization are provided. This case study supports the importance of early and ongoing involvement of relevant potential knowledge users in research to enhance its utilization potential. It also highlights the need for re-thinking research funding approaches.

Research on Knowledge-Based Optimization Method of Indoor Location Based on Low Energy Bluetooth

NASA Astrophysics Data System (ADS)

Li, C.; Li, G.; Deng, Y.; Wang, T.; Kang, Z.

2017-09-01

With the rapid development of LBS (Location-based Service), the demand for commercialization of indoor location has been increasing, but its technology is not perfect. Currently, the accuracy of indoor location, the complexity of the algorithm, and the cost of positioning are hard to be simultaneously considered and it is still restricting the determination and application of mainstream positioning technology. Therefore, this paper proposes a method of knowledge-based optimization of indoor location based on low energy Bluetooth. The main steps include: 1) The establishment and application of a priori and posterior knowledge base. 2) Primary selection of signal source. 3) Elimination of positioning gross error. 4) Accumulation of positioning knowledge. The experimental results show that the proposed algorithm can eliminate the signal source of outliers and improve the accuracy of single point positioning in the simulation data. The proposed scheme is a dynamic knowledge accumulation rather than a single positioning process. The scheme adopts cheap equipment and provides a new idea for the theory and method of indoor positioning. Moreover, the performance of the high accuracy positioning results in the simulation data shows that the scheme has a certain application value in the commercial promotion.

Examining Collaborative Knowledge Construction in Microblogging-Based Learning Environments

ERIC Educational Resources Information Center

Luo, Tian; Clifton, Lacey

2017-01-01

Aim/Purpose: The purpose of the study is to provide foundational research to exemplify how knowledge construction takes place in microblogging-based learning environments, to understand learner interaction representing the knowledge construction process, and to analyze learner perception, thereby suggesting a model of delivery for microblogging.…

A Model of Knowledge Based Information Retrieval with Hierarchical Concept Graph.

ERIC Educational Resources Information Center

Kim, Young Whan; Kim, Jin H.

1990-01-01

Proposes a model of knowledge-based information retrieval (KBIR) that is based on a hierarchical concept graph (HCG) which shows relationships between index terms and constitutes a hierarchical thesaurus as a knowledge base. Conceptual distance between a query and an object is discussed and the use of Boolean operators is described. (25…

KoBaMIN: a knowledge-based minimization web server for protein structure refinement.

PubMed

Rodrigues, João P G L M; Levitt, Michael; Chopra, Gaurav

2012-07-01

The KoBaMIN web server provides an online interface to a simple, consistent and computationally efficient protein structure refinement protocol based on minimization of a knowledge-based potential of mean force. The server can be used to refine either a single protein structure or an ensemble of proteins starting from their unrefined coordinates in PDB format. The refinement method is particularly fast and accurate due to the underlying knowledge-based potential derived from structures deposited in the PDB; as such, the energy function implicitly includes the effects of solvent and the crystal environment. Our server allows for an optional but recommended step that optimizes stereochemistry using the MESHI software. The KoBaMIN server also allows comparison of the refined structures with a provided reference structure to assess the changes brought about by the refinement protocol. The performance of KoBaMIN has been benchmarked widely on a large set of decoys, all models generated at the seventh worldwide experiments on critical assessment of techniques for protein structure prediction (CASP7) and it was also shown to produce top-ranking predictions in the refinement category at both CASP8 and CASP9, yielding consistently good results across a broad range of model quality values. The web server is fully functional and freely available at http://csb.stanford.edu/kobamin.

System diagnostic builder: a rule-generation tool for expert systems that do intelligent data evaluation

NASA Astrophysics Data System (ADS)

Nieten, Joseph L.; Burke, Roger

1993-03-01

The system diagnostic builder (SDB) is an automated knowledge acquisition tool using state- of-the-art artificial intelligence (AI) technologies. The SDB uses an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert (SME). Thus, data is captured from the subject system, classified by an expert, and used to drive the rule generation process. These rule-bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The rule-bases can be used in any knowledge based system which monitors or controls a physical system or simulation. The SDB has demonstrated the utility of using inductive machine learning technology to generate reliable knowledge bases. In fact, we have discovered that the knowledge captured by the SDB can be used in any number of applications. For example, the knowledge bases captured from the SMS can be used as black box simulations by intelligent computer aided training devices. We can also use the SDB to construct knowledge bases for the process control industry, such as chemical production, or oil and gas production. These knowledge bases can be used in automated advisory systems to ensure safety, productivity, and consistency.

The development of a classification schema for arts-based approaches to knowledge translation.

PubMed

Archibald, Mandy M; Caine, Vera; Scott, Shannon D

2014-10-01

Arts-based approaches to knowledge translation are emerging as powerful interprofessional strategies with potential to facilitate evidence uptake, communication, knowledge, attitude, and behavior change across healthcare provider and consumer groups. These strategies are in the early stages of development. To date, no classification system for arts-based knowledge translation exists, which limits development and understandings of effectiveness in evidence syntheses. We developed a classification schema of arts-based knowledge translation strategies based on two mechanisms by which these approaches function: (a) the degree of precision in key message delivery, and (b) the degree of end-user participation. We demonstrate how this classification is necessary to explore how context, time, and location shape arts-based knowledge translation strategies. Classifying arts-based knowledge translation strategies according to their core attributes extends understandings of the appropriateness of these approaches for various healthcare settings and provider groups. The classification schema developed may enhance understanding of how, where, and for whom arts-based knowledge translation approaches are effective, and enable theorizing of essential knowledge translation constructs, such as the influence of context, time, and location on utilization strategies. The classification schema developed may encourage systematic inquiry into the effectiveness of these approaches in diverse interprofessional contexts. © 2014 Sigma Theta Tau International.

Non-Linear Effects in Knowledge Production

NASA Astrophysics Data System (ADS)

Purica, Ionut

2007-04-01

The generation of technological knowledge is paramount to our present development; the production of technological knowledge is governed by the same Cobb Douglas type model, with the means of research and the intelligence level replacing capital, respectively labor. We are exploring the basic behavior of present days' economies that are producing technological knowledge, along with the `usual' industrial production and determine a basic behavior that turns out to be a `Henon attractor'. Measures are introduced for the gain of technological knowledge and for the information of technological sequences that are based respectively on the underlying multi-valued modal logic of the technological research and on nonlinear thermodynamic considerations.

Knowledge-Driven Event Extraction in Russian: Corpus-Based Linguistic Resources

PubMed Central

Solovyev, Valery; Ivanov, Vladimir

2016-01-01

Automatic event extraction form text is an important step in knowledge acquisition and knowledge base population. Manual work in development of extraction system is indispensable either in corpus annotation or in vocabularies and pattern creation for a knowledge-based system. Recent works have been focused on adaptation of existing system (for extraction from English texts) to new domains. Event extraction in other languages was not studied due to the lack of resources and algorithms necessary for natural language processing. In this paper we define a set of linguistic resources that are necessary in development of a knowledge-based event extraction system in Russian: a vocabulary of subordination models, a vocabulary of event triggers, and a vocabulary of Frame Elements that are basic building blocks for semantic patterns. We propose a set of methods for creation of such vocabularies in Russian and other languages using Google Books NGram Corpus. The methods are evaluated in development of event extraction system for Russian. PMID:26955386

Collaborative Learning and Knowledge-Construction through a Knowledge-Based WWW Authoring Tool.

ERIC Educational Resources Information Center

Haugsjaa, Erik

This paper outlines hurdles to using the World Wide Web for learning, specifically in a collaborative knowledge-construction environment. Theoretical solutions based directly on existing Web environments, as well as on research and system prototypes in the areas of Intelligent Tutoring Systems (ITS) and ITS authoring systems, are suggested. Topics…

The use and generation of illustrative examples in computer-based instructional systems

NASA Technical Reports Server (NTRS)

Selig, William John; Johannes, James D.

1987-01-01

A method is proposed whereby the underlying domain knowledge is represented such that illustrative examples may be generated on demand. This method has the advantage that the generated example can follow changes in the domain in addition to allowing automatic customization of the example to the individual.

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.

Learning Science-Based Fitness Knowledge in Constructivist Physical Education

ERIC Educational Resources Information Center

Sun, Haichun; Chen, Ang; Zhu, Xihe; Ennis, Catherine D.

2012-01-01

Teaching fitness-related knowledge has become critical in developing children's healthful living behavior. The purpose of this study was to examine the effects of a science-based, constructivist physical education curriculum on learning fitness knowledge critical to healthful living in elementary school students. The schools (N = 30) were randomly…

Knowledge service decision making in business incubators based on the supernetwork model

NASA Astrophysics Data System (ADS)

Zhao, Liming; Zhang, Haihong; Wu, Wenqing

2017-08-01

As valuable resources for incubating firms, knowledge resources have received gradually increasing attention from all types of business incubators, and business incubators use a variety of knowledge services to stimulate rapid growth in incubating firms. Based on previous research, we generalize the knowledge transfer and knowledge networking services of two main forms of knowledge services and further divide knowledge transfer services into knowledge depth services and knowledge breadth services. Then, we construct the business incubators' knowledge supernetwork model, describe the evolution mechanism among heterogeneous agents and utilize a simulation to explore the performance variance of different business incubators' knowledge services. The simulation results show that knowledge stock increases faster when business incubators are able to provide knowledge services to more incubating firms and that the degree of discrepancy in the knowledge stock increases during the process of knowledge growth. Further, knowledge transfer services lead to greater differences in the knowledge structure, while knowledge networking services lead to smaller differences. Regarding the two types of knowledge transfer services, knowledge depth services are more conducive to knowledge growth than knowledge breadth services, but knowledge depth services lead to greater gaps in knowledge stocks and greater differences in knowledge structures. Overall, it is optimal for business incubators to select a single knowledge service or portfolio strategy based on the amount of time and energy expended on the two types of knowledge services.

An Ontology-Based Approach to Incorporate User-Generated Geo-Content Into Sdi

NASA Astrophysics Data System (ADS)

Deng, D.-P.; Lemmens, R.

2011-08-01

The Web is changing the way people share and communicate information because of emergence of various Web technologies, which enable people to contribute information on the Web. User-Generated Geo-Content (UGGC) is a potential resource of geographic information. Due to the different production methods, UGGC often cannot fit in geographic information model. There is a semantic gap between UGGC and formal geographic information. To integrate UGGC into geographic information, this study conducts an ontology-based process to bridge this semantic gap. This ontology-based process includes five steps: Collection, Extraction, Formalization, Mapping, and Deployment. In addition, this study implements this process on Twitter messages, which is relevant to Japan Earthquake disaster. By using this process, we extract disaster relief information from Twitter messages, and develop a knowledge base for GeoSPARQL queries in disaster relief information.

PLAN-IT: Knowledge-Based Mission Sequencing

NASA Astrophysics Data System (ADS)

Biefeld, Eric W.

1987-02-01

Mission sequencing consumes a large amount of time and manpower during a space exploration effort. Plan-It is a knowledge-based approach to assist in mission sequencing. Plan-It uses a combined frame and blackboard architecture. This paper reports on the approach implemented by Plan-It and the current applications of Plan-It for sequencing at NASA.

A knowledge-based object recognition system for applications in the space station

NASA Technical Reports Server (NTRS)

Dhawan, Atam P.

1988-01-01

A knowledge-based three-dimensional (3D) object recognition system is being developed. The system uses primitive-based hierarchical relational and structural matching for the recognition of 3D objects in the two-dimensional (2D) image for interpretation of the 3D scene. At present, the pre-processing, low-level preliminary segmentation, rule-based segmentation, and the feature extraction are completed. The data structure of the primitive viewing knowledge-base (PVKB) is also completed. Algorithms and programs based on attribute-trees matching for decomposing the segmented data into valid primitives were developed. The frame-based structural and relational descriptions of some objects were created and stored in a knowledge-base. This knowledge-base of the frame-based descriptions were developed on the MICROVAX-AI microcomputer in LISP environment. The simulated 3D scene of simple non-overlapping objects as well as real camera data of images of 3D objects of low-complexity have been successfully interpreted.

MO-D-BRC-00: In Memoriam of Jan Van De Geijn: Knowledge-Based Planning

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

NONE

Treatment planning is a central part of radiation therapy, including delineation in tumor volumes and critical organs, setting treatment goals of prescription doses to the tumor targets and tolerance doses to the critical organs, and finally generation of treatment plans to meet the treatment goals. National groups like RTOG have led the effort to standardize treatment goals of the prescription doses to the tumor targets and tolerance doses to the critical organs based on accumulated knowledge from decades of abundant clinical trial experience. The challenge for each clinical department is how to achieve or surpass these set goals within themore » time constraints of clinical practice. Using fifteen testing cases from different treatment sites such as head and neck, prostate with and without pelvic lymph nodes, SBRT spine, we will present clinically utility of advanced planning tools, including knowledge based, automatic based, and multiple criteria based tools that are clinically implemented. The objectives of this session are: Understand differences among these three advanced planning tools Provide clinical assessments on the utility of the advanced planning tools Discuss clinical challenges of treatment planning with large variations in tumor volumes and their relationships with adjacent critical organs. Ping Xia received research grant from Philips. Jackie Wu received research grant from Varian; P. Xia, Research support by Philips and Varian; Q. Wu, NIH, Varian Medical.« less

Development of an Inquiry-Based Learning Support System Based on an Intelligent Knowledge Exploration Approach

ERIC Educational Resources Information Center

Wu, Ji-Wei; Tseng, Judy C. R.; Hwang, Gwo-Jen

2015-01-01

Inquiry-Based Learning (IBL) is an effective approach for promoting active learning. When inquiry-based learning is incorporated into instruction, teachers provide guiding questions for students to actively explore the required knowledge in order to solve the problems. Although the World Wide Web (WWW) is a rich knowledge resource for students to…

Sustaining Knowledge Building as a Principle-Based Innovation at an Elementary School

ERIC Educational Resources Information Center

Zhang, Jianwei; Hong, Huang-Yao; Scardamalia, Marlene; Teo, Chew Lee; Morley, Elizabeth A.

2011-01-01

This study explores Knowledge Building as a principle-based innovation at an elementary school and makes a case for a principle- versus procedure-based approach to educational innovation, supported by new knowledge media. Thirty-nine Knowledge Building initiatives, each focused on a curriculum theme and facilitated by nine teachers over eight…

'Medical Knowledge' and 'Tradition' of Colonial Korea: Focused on Kudo's "Gynecology"-based Knowledge.

PubMed

Hong, Yang Hee

2013-08-01

This article attempts to illuminate the ways in which Kudo's medical knowledge based on 'gynecological science' constructed the cultural 'traditions' of colonial Korea. Kudo appears to have been quite an influential figure in colonial Korea in that his writings on the relationship between women's crime, gynecological science and the Chosŏn society granted a significant amount of intellectual authority. Here, I examine Kudo's position within colonial Korea as a producer and propagator of medical knowledge, and then see how women's bodies were understood according to his gynecological knowledge. It also traces the ways in which Kudo's gynecological knowledge represents Chosŏn society and in turn invents the 'traditions' of Chosŏn. Kudo's knowledge of "gynecology" which had been formed while it traveled the states such as Japan, Germany and France served as an important reference for his representation of colonial Korean society. Kudo was a proponent of biological evolution, particularly the rules of 'atavism' put forth by the criminal anthropologist Cesare Lombroso, and argued that an unique social environment caused 'alteration of sexual urges' and primitive cruelty in Chosŏn women. According to Kudo, The social environment was none other than the practice of 'early marriage,' which went against the physiology of women. To Kudo, 'early marriage' was an old 'tradition' of Chosŏn and the cause of heinous crimes, as well as an unmistakable indicator of both the primitiveness and savageness of Chosŏn. While Lombroso considered personal factors such as stress as the cause of women's crimes, Kudo saw Chosŏn women's crimes as a national characteristic. Moreover, he compared the occurrence rate of husband murders by provinces, based on which he categorized the northern population of Chosŏn as barbaric Manchurian and the southern population as the superior Japanese, a combination of racism and scientific knowledge. Kudo's writings provide an insight into the

Knowledge-Based Environmental Context Modeling

NASA Astrophysics Data System (ADS)

Pukite, P. R.; Challou, D. J.

2017-12-01

As we move from the oil-age to an energy infrastructure based on renewables, the need arises for new educational tools to support the analysis of geophysical phenomena and their behavior and properties. Our objective is to present models of these phenomena to make them amenable for incorporation into more comprehensive analysis contexts. Starting at the level of a college-level computer science course, the intent is to keep the models tractable and therefore practical for student use. Based on research performed via an open-source investigation managed by DARPA and funded by the Department of Interior [1], we have adapted a variety of physics-based environmental models for a computer-science curriculum. The original research described a semantic web architecture based on patterns and logical archetypal building-blocks (see figure) well suited for a comprehensive environmental modeling framework. The patterns span a range of features that cover specific land, atmospheric and aquatic domains intended for engineering modeling within a virtual environment. The modeling engine contained within the server relied on knowledge-based inferencing capable of supporting formal terminology (through NASA JPL's Semantic Web for Earth and Environmental Technology (SWEET) ontology and a domain-specific language) and levels of abstraction via integrated reasoning modules. One of the key goals of the research was to simplify models that were ordinarily computationally intensive to keep them lightweight enough for interactive or virtual environment contexts. The breadth of the elements incorporated is well-suited for learning as the trend toward ontologies and applying semantic information is vital for advancing an open knowledge infrastructure. As examples of modeling, we have covered such geophysics topics as fossil-fuel depletion, wind statistics, tidal analysis, and terrain modeling, among others. Techniques from the world of computer science will be necessary to promote efficient

Finding gene regulatory network candidates using the gene expression knowledge base.

PubMed

Venkatesan, Aravind; Tripathi, Sushil; Sanz de Galdeano, Alejandro; Blondé, Ward; Lægreid, Astrid; Mironov, Vladimir; Kuiper, Martin

2014-12-10

Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of 'omics' data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.

Finding ways to grow skills, knowledge, and voice in the next generation of interdisciplinary sustainability scientists

NASA Astrophysics Data System (ADS)

Roy, S. G.; de Souza, S. P.; McGreavy, B.; Gardner, K.; Hart, D.; Druschke, C. G.

2017-12-01

The need to train early-career interdisciplinary, solutions-driven sustainability researchers has never been more apparent than today. To meet this challenge, educators at the Universities of Maine, New Hampshire, and Rhode Island have collaborated with their students to design and assess an interdisciplinary, multi-university course meant to develop the skills, content knowledge, and voice that are seen as critical for training the next generation of interdisciplinary sustainability researchers. We developed a rubric and conducted a mixed methods analysis of sustainability science learning outcomes identified as central to successful sustainability research. We used these targeted outcomes as a guide to design and implement several activities that build these skills and competencies and advance the identified outcomes. These course learning outcomes focus on three major sustainability science competencies: (1) systems thinking, which focuses on improving students' abilities to build a deep understanding of dynamic social-ecological systems; (2) problem definition, which focuses on the skills necessary to identify and communicate sustainability problems by combining systems knowledge with multiple stakeholder perspectives; and (3) decision making, which focuses on the abilities required to create and communicate adaptable decisions to mitigate sustainability problems. Students were frequently asked to help co-create class meetings based on their own educational experiences and objectives.Based on a quantitative assessment of survey results taken before and after the course, several students tended to initially overestimate their capacity for undertaking interdisciplinary sustainability research, possibly because of a previously narrow exposure to these concepts from the perspective of a single discipline. Qualitative results indicate that students gained substantial experience and confidence in communication, and especially in collaboration, stakeholder engagement

Knowledge-based approach to video content classification

NASA Astrophysics Data System (ADS)

Chen, Yu; Wong, Edward K.

2001-01-01

A framework for video content classification using a knowledge-based approach is herein proposed. This approach is motivated by the fact that videos are rich in semantic contents, which can best be interpreted and analyzed by human experts. We demonstrate the concept by implementing a prototype video classification system using the rule-based programming language CLIPS 6.05. Knowledge for video classification is encoded as a set of rules in the rule base. The left-hand-sides of rules contain high level and low level features, while the right-hand-sides of rules contain intermediate results or conclusions. Our current implementation includes features computed from motion, color, and text extracted from video frames. Our current rule set allows us to classify input video into one of five classes: news, weather, reporting, commercial, basketball and football. We use MYCIN's inexact reasoning method for combining evidences, and to handle the uncertainties in the features and in the classification results. We obtained good results in a preliminary experiment, and it demonstrated the validity of the proposed approach.

Knowledge-based approach to video content classification

NASA Astrophysics Data System (ADS)

Chen, Yu; Wong, Edward K.

2000-12-01

A framework for video content classification using a knowledge-based approach is herein proposed. This approach is motivated by the fact that videos are rich in semantic contents, which can best be interpreted and analyzed by human experts. We demonstrate the concept by implementing a prototype video classification system using the rule-based programming language CLIPS 6.05. Knowledge for video classification is encoded as a set of rules in the rule base. The left-hand-sides of rules contain high level and low level features, while the right-hand-sides of rules contain intermediate results or conclusions. Our current implementation includes features computed from motion, color, and text extracted from video frames. Our current rule set allows us to classify input video into one of five classes: news, weather, reporting, commercial, basketball and football. We use MYCIN's inexact reasoning method for combining evidences, and to handle the uncertainties in the features and in the classification results. We obtained good results in a preliminary experiment, and it demonstrated the validity of the proposed approach.

Knowledge network model of the energy consumption in discrete manufacturing system

NASA Astrophysics Data System (ADS)

Xu, Binzi; Wang, Yan; Ji, Zhicheng

2017-07-01

Discrete manufacturing system generates a large amount of data and information because of the development of information technology. Hence, a management mechanism is urgently required. In order to incorporate knowledge generated from manufacturing data and production experience, a knowledge network model of the energy consumption in the discrete manufacturing system was put forward based on knowledge network theory and multi-granularity modular ontology technology. This model could provide a standard representation for concepts, terms and their relationships, which could be understood by both human and computer. Besides, the formal description of energy consumption knowledge elements (ECKEs) in the knowledge network was also given. Finally, an application example was used to verify the feasibility of the proposed method.

Knowledge engineering for adverse drug event prevention: on the design and development of a uniform, contextualized and sustainable knowledge-based framework.

PubMed

Koutkias, Vassilis; Kilintzis, Vassilis; Stalidis, George; Lazou, Katerina; Niès, Julie; Durand-Texte, Ludovic; McNair, Peter; Beuscart, Régis; Maglaveras, Nicos

2012-06-01

The primary aim of this work was the development of a uniform, contextualized and sustainable knowledge-based framework to support adverse drug event (ADE) prevention via Clinical Decision Support Systems (CDSSs). In this regard, the employed methodology involved first the systematic analysis and formalization of the knowledge sources elaborated in the scope of this work, through which an application-specific knowledge model has been defined. The entire framework architecture has been then specified and implemented by adopting Computer Interpretable Guidelines (CIGs) as the knowledge engineering formalism for its construction. The framework integrates diverse and dynamic knowledge sources in the form of rule-based ADE signals, all under a uniform Knowledge Base (KB) structure, according to the defined knowledge model. Equally important, it employs the means to contextualize the encapsulated knowledge, in order to provide appropriate support considering the specific local environment (hospital, medical department, language, etc.), as well as the mechanisms for knowledge querying, inference, sharing, and management. In this paper, we present thoroughly the establishment of the proposed knowledge framework by presenting the employed methodology and the results obtained as regards implementation, performance and validation aspects that highlight its applicability and virtue in medication safety. Copyright © 2012 Elsevier Inc. All rights reserved.

The Meta-Ontology Model of the Fishdisease Diagnostic Knowledge Based on Owl

NASA Astrophysics Data System (ADS)

Shi, Yongchang; Gao, Wen; Hu, Liang; Fu, Zetian

For improving available and reusable of knowledge in fish disease diagnosis (FDD) domain and facilitating knowledge acquisition, an ontology model of FDD knowledge was developed based on owl according to FDD knowledge model. It includes terminology of terms in FDD knowledge and hierarchies of their class.

Evaluation of an automated knowledge-based textual summarization system for longitudinal clinical data, in the intensive care domain.

PubMed

Goldstein, Ayelet; Shahar, Yuval; Orenbuch, Efrat; Cohen, Matan J

2017-10-01

To examine the feasibility of the automated creation of meaningful free-text summaries of longitudinal clinical records, using a new general methodology that we had recently developed; and to assess the potential benefits to the clinical decision-making process of using such a method to generate draft letters that can be further manually enhanced by clinicians. We had previously developed a system, CliniText (CTXT), for automated summarization in free text of longitudinal medical records, using a clinical knowledge base. In the current study, we created an Intensive Care Unit (ICU) clinical knowledge base, assisted by two ICU clinical experts in an academic tertiary hospital. The CTXT system generated free-text summary letters from the data of 31 different patients, which were compared to the respective original physician-composed discharge letters. The main evaluation measures were (1) relative completeness, quantifying the data items missed by one of the letters but included by the other, and their importance; (2) quality parameters, such as readability; (3) functional performance, assessed by the time needed, by three clinicians reading each of the summaries, to answer five key questions, based on the discharge letter (e.g., "What are the patient's current respiratory requirements?"), and by the correctness of the clinicians' answers. Completeness: In 13/31 (42%) of the letters the number of important items missed in the CTXT-generated letter was actually less than or equal to the number of important items missed by the MD-composed letter. In each of the MD-composed letters, at least two important items that were mentioned by the CTXT system were missed (a mean of 7.2±5.74). In addition, the standard deviation in the number of missed items in the MD letters (STD=15.4) was much higher than the standard deviation in the CTXT-generated letters (STD=5.3). Quality: The MD-composed letters obtained a significantly better grade in three out of four measured parameters

SAFOD Brittle Microstructure and Mechanics Knowledge Base (BM2KB)

NASA Astrophysics Data System (ADS)

Babaie, Hassan A.; Broda Cindi, M.; Hadizadeh, Jafar; Kumar, Anuj

2013-07-01

Scientific drilling near Parkfield, California has established the San Andreas Fault Observatory at Depth (SAFOD), which provides the solid earth community with short range geophysical and fault zone material data. The BM2KB ontology was developed in order to formalize the knowledge about brittle microstructures in the fault rocks sampled from the SAFOD cores. A knowledge base, instantiated from this domain ontology, stores and presents the observed microstructural and analytical data with respect to implications for brittle deformation and mechanics of faulting. These data can be searched on the knowledge base‧s Web interface by selecting a set of terms (classes, properties) from different drop-down lists that are dynamically populated from the ontology. In addition to this general search, a query can also be conducted to view data contributed by a specific investigator. A search by sample is done using the EarthScope SAFOD Core Viewer that allows a user to locate samples on high resolution images of core sections belonging to different runs and holes. The class hierarchy of the BM2KB ontology was initially designed using the Unified Modeling Language (UML), which was used as a visual guide to develop the ontology in OWL applying the Protégé ontology editor. Various Semantic Web technologies such as the RDF, RDFS, and OWL ontology languages, SPARQL query language, and Pellet reasoning engine, were used to develop the ontology. An interactive Web application interface was developed through Jena, a java based framework, with AJAX technology, jsp pages, and java servlets, and deployed via an Apache tomcat server. The interface allows the registered user to submit data related to their research on a sample of the SAFOD core. The submitted data, after initial review by the knowledge base administrator, are added to the extensible knowledge base and become available in subsequent queries to all types of users. The interface facilitates inference capabilities in the

Value Creation in the Knowledge-Based Economy

ERIC Educational Resources Information Center

Liu, Fang-Chun

2013-01-01

Effective investment strategies help companies form dynamic core organizational capabilities allowing them to adapt and survive in today's rapidly changing knowledge-based economy. This dissertation investigates three valuation issues that challenge managers with respect to developing business-critical investment strategies that can have…

Knowledge-based personalized search engine for the Web-based Human Musculoskeletal System Resources (HMSR) in biomechanics.

PubMed

Dao, Tien Tuan; Hoang, Tuan Nha; Ta, Xuan Hien; Tho, Marie Christine Ho Ba

2013-02-01

Human musculoskeletal system resources of the human body are valuable for the learning and medical purposes. Internet-based information from conventional search engines such as Google or Yahoo cannot response to the need of useful, accurate, reliable and good-quality human musculoskeletal resources related to medical processes, pathological knowledge and practical expertise. In this present work, an advanced knowledge-based personalized search engine was developed. Our search engine was based on a client-server multi-layer multi-agent architecture and the principle of semantic web services to acquire dynamically accurate and reliable HMSR information by a semantic processing and visualization approach. A security-enhanced mechanism was applied to protect the medical information. A multi-agent crawler was implemented to develop a content-based database of HMSR information. A new semantic-based PageRank score with related mathematical formulas were also defined and implemented. As the results, semantic web service descriptions were presented in OWL, WSDL and OWL-S formats. Operational scenarios with related web-based interfaces for personal computers and mobile devices were presented and analyzed. Functional comparison between our knowledge-based search engine, a conventional search engine and a semantic search engine showed the originality and the robustness of our knowledge-based personalized search engine. In fact, our knowledge-based personalized search engine allows different users such as orthopedic patient and experts or healthcare system managers or medical students to access remotely into useful, accurate, reliable and good-quality HMSR information for their learning and medical purposes. Copyright © 2012 Elsevier Inc. All rights reserved.

Knowledge-Based Hierarchies: Using Organizations to Understand the Economy

ERIC Educational Resources Information Center

Garicano, Luis; Rossi-Hansberg, Esteban

2015-01-01

Incorporating the decision of how to organize the acquisition, use, and communication of knowledge into economic models is essential to understand a wide variety of economic phenomena. We survey the literature that has used knowledge-based hierarchies to study issues such as the evolution of wage inequality, the growth and productivity of firms,…

Health-Related Fitness Knowledge Development through Project-Based Learning

ERIC Educational Resources Information Center

Hastle, Peter A.; Chen, Senlin; Guarino, Anthony J.

2017-01-01

Purpose: The purpose of this study was to examine the process and outcome of an intervention using the project-based learning (PBL) model to increase students' health-related fitness (HRF) knowledge. Method: The participants were 185 fifth-grade students from three schools in Alabama (PBL group: n = 109; control group: n = 76). HRF knowledge was…

Advances in knowledge-based software engineering

NASA Technical Reports Server (NTRS)

Truszkowski, Walt

1991-01-01

The underlying hypothesis of this work is that a rigorous and comprehensive software reuse methodology can bring about a more effective and efficient utilization of constrained resources in the development of large-scale software systems by both government and industry. It is also believed that correct use of this type of software engineering methodology can significantly contribute to the higher levels of reliability that will be required of future operational systems. An overview and discussion of current research in the development and application of two systems that support a rigorous reuse paradigm are presented: the Knowledge-Based Software Engineering Environment (KBSEE) and the Knowledge Acquisition fo the Preservation of Tradeoffs and Underlying Rationales (KAPTUR) systems. Emphasis is on a presentation of operational scenarios which highlight the major functional capabilities of the two systems.

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…

Comparison of long-term knowledge retention in lecture-based versus flipped team-based learning course delivery.

PubMed

Taglieri, Catherine; Schnee, David; Dvorkin Camiel, Lana; Zaiken, Kathy; Mistry, Amee; Nigro, Stefanie; Tataronis, Gary; Patel, Dhiren; Jacobson, Susan; Goldman, Jennifer

2017-05-01

To determine whether team based learning (TBL) is superior to traditional lecture -based learning in confidence and knowledge retention one year later. A survey was administered 17 months after a completion of a required over-the-counter /self-care (OTC) course to two different cohorts of students. The survey assessed confidence and knowledge related to OTC topics. The lecture group had a traditional lecture based classroom experience; the intervention group experienced a TBL format throughout the entire course. One hundred forty-seven students of 283 enrolled (51.9%) in the lecture group and 222 of 305 (72.8%) students in the TBL group participated in the knowledge assessment and survey. Demographic data including student grade point averages (GPA) and confidence were similar in both groups. Mean assessment scores (±SD) on OTC knowledge was significantly higher in the traditional lecture based group versus the TBL group; 62.9±19.3 vs. 54.9±15.7 (p=0.001). Although TBL is thought to improve student engagement and mastery of material, after an initial implementation of TBL, knowledge retention in the long term appears to be lower than lecture based learning. Copyright © 2017 Elsevier Inc. All rights reserved.

A knowledge-based machine vision system for space station automation

NASA Technical Reports Server (NTRS)

Chipman, Laure J.; Ranganath, H. S.

1989-01-01

A simple knowledge-based approach to the recognition of objects in man-made scenes is being developed. Specifically, the system under development is a proposed enhancement to a robot arm for use in the space station laboratory module. The system will take a request from a user to find a specific object, and locate that object by using its camera input and information from a knowledge base describing the scene layout and attributes of the object types included in the scene. In order to use realistic test images in developing the system, researchers are using photographs of actual NASA simulator panels, which provide similar types of scenes to those expected in the space station environment. Figure 1 shows one of these photographs. In traditional approaches to image analysis, the image is transformed step by step into a symbolic representation of the scene. Often the first steps of the transformation are done without any reference to knowledge of the scene or objects. Segmentation of an image into regions generally produces a counterintuitive result in which regions do not correspond to objects in the image. After segmentation, a merging procedure attempts to group regions into meaningful units that will more nearly correspond to objects. Here, researchers avoid segmenting the image as a whole, and instead use a knowledge-directed approach to locate objects in the scene. The knowledge-based approach to scene analysis is described and the categories of knowledge used in the system are discussed.

Fortuitous phenomena: on complexity, pragmatic randomised controlled trials, and knowledge for evidence-based practice.

PubMed

Thompson, Carl

2004-01-01

Many of the interventions that nurses develop and implement are in themselves complex and have to operate in situations of irreducible complexity and uncertainty. This article argues that the primary means of generating knowledge for the evidence-based deployment of complex interventions should be the pragmatic randomised controlled trial. Randomised controlled trials represent the only research design to adequately deal with that which we know and (far more importantly) that which we do not. Using the example of practice development as an exemplar for complexity, and drawing on the objections often voiced as a response to calls to make use of randomised controlled trials in nursing and nursing research, the article presents a developmental framework and some methodological solutions to problems often encountered. Randomised controlled trials, whilst undoubtedly methodologically and strategically challenging, offer the most robust basis for developing primary research knowledge on the effects of complex interventions in nursing and their active components.

Predicate Oriented Pattern Analysis for Biomedical Knowledge Discovery

PubMed Central

Shen, Feichen; Liu, Hongfang; Sohn, Sunghwan; Larson, David W.; Lee, Yugyung

2017-01-01

In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF, OWL). With the increasing number of semi-structured data coming into the biomedical community, data integration and knowledge discovery from heterogeneous domains become important research problem. In the application level, detection of related concepts among medical ontologies is an important goal of life science research. It is more crucial to figure out how different concepts are related within a single ontology or across multiple ontologies by analysing predicates in different knowledge bases. However, the world today is one of information explosion, and it is extremely difficult for biomedical researchers to find existing or potential predicates to perform linking among cross domain concepts without any support from schema pattern analysis. Therefore, there is a need for a mechanism to do predicate oriented pattern analysis to partition heterogeneous ontologies into closer small topics and do query generation to discover cross domain knowledge from each topic. In this paper, we present such a model that predicates oriented pattern analysis based on their close relationship and generates a similarity matrix. Based on this similarity matrix, we apply an innovated unsupervised learning algorithm to partition large data sets into smaller and closer topics and generate meaningful queries to fully discover knowledge over a set of interlinked data sources. We have implemented a prototype system named BmQGen and evaluate the proposed model with colorectal surgical cohort from the Mayo Clinic. PMID:28983419

The Knowledge Base as an Extension of Distance Learning Reference Service

ERIC Educational Resources Information Center

Casey, Anne Marie

2012-01-01

This study explores knowledge bases as extension of reference services for distance learners. Through a survey and follow-up interviews with distance learning librarians, this paper discusses their interest in creating and maintaining a knowledge base as a resource for reference services to distance learners. It also investigates their perceptions…

Critical and post-critical behaviour of two-degree-of-freedom flutter-based generators

NASA Astrophysics Data System (ADS)

Pigolotti, Luca; Mannini, Claudio; Bartoli, Gianni; Thiele, Klaus

2017-09-01

Energy harvesting from flow-induced vibrations is a recent research field, which considers a diverse range of systems, among which two-degree-of-freedom flutter-based solutions were individuated as good candidates to obtain high energy performance. In the present work, numerical linear analyses and wind-tunnel tests were conducted on a flat-plate sectional model. The aim is to identify some design guidelines for generators exploiting the classical-flutter instability, through the investigation of the critical condition and the response during the post-critical regime. Many sets of governing parameters of interest from the energy-harvesting point of view were considered, including high levels of heaving damping to simulate the operation of a conversion apparatus. In particular, eccentricity of the elastic centre and small downstream mass unbalance can be introduced as solutions aiming at optimal operative ranges. The collected results suggest the high potentiality of flutter-based generators, and a significant enhancement of performance can be envisaged. Moreover, they contribute to improve the knowledge of the flutter excitation mechanism and to widen the dataset of measurements in the post-critical regime.

Community-based participatory research and integrated knowledge translation: advancing the co-creation of knowledge.

PubMed

Jull, Janet; Giles, Audrey; Graham, Ian D

2017-12-19

Better use of research evidence (one form of "knowledge") in health systems requires partnerships between researchers and those who contend with the real-world needs and constraints of health systems. Community-based participatory research (CBPR) and integrated knowledge translation (IKT) are research approaches that emphasize the importance of creating partnerships between researchers and the people for whom the research is ultimately meant to be of use ("knowledge users"). There exist poor understandings of the ways in which these approaches converge and diverge. Better understanding of the similarities and differences between CBPR and IKT will enable researchers to use these approaches appropriately and to leverage best practices and knowledge from each. The co-creation of knowledge conveys promise of significant social impacts, and further understandings of how to engage and involve knowledge users in research are needed. We examine the histories and traditions of CBPR and IKT, as well as their points of convergence and divergence. We critically evaluate the ways in which both have the potential to contribute to the development and integration of knowledge in health systems. As distinct research traditions, the underlying drivers and rationale for CBPR and IKT have similarities and differences across the areas of motivation, social location, and ethics; nevertheless, the practices of CBPR and IKT converge upon a common aim: the co-creation of knowledge that is the result of knowledge user and researcher expertise. We argue that while CBPR and IKT both have the potential to contribute evidence to implementation science and practices for collaborative research, clarity for the purpose of the research-social change or application-is a critical feature in the selection of an appropriate collaborative approach to build knowledge. CBPR and IKT bring distinct strengths to a common aim: to foster democratic processes in the co-creation of knowledge. As research

Analysis, Simulation, and Verification of Knowledge-Based, Rule-Based, and Expert Systems

NASA Technical Reports Server (NTRS)

Hinchey, Mike; Rash, James; Erickson, John; Gracanin, Denis; Rouff, Chris

2010-01-01

Mathematically sound techniques are used to view a knowledge-based system (KBS) as a set of processes executing in parallel and being enabled in response to specific rules being fired. The set of processes can be manipulated, examined, analyzed, and used in a simulation. The tool that embodies this technology may warn developers of errors in their rules, but may also highlight rules (or sets of rules) in the system that are underspecified (or overspecified) and need to be corrected for the KBS to operate as intended. The rules embodied in a KBS specify the allowed situations, events, and/or results of the system they describe. In that sense, they provide a very abstract specification of a system. The system is implemented through the combination of the system specification together with an appropriate inference engine, independent of the algorithm used in that inference engine. Viewing the rule base as a major component of the specification, and choosing an appropriate specification notation to represent it, reveals how additional power can be derived from an approach to the knowledge-base system that involves analysis, simulation, and verification. This innovative approach requires no special knowledge of the rules, and allows a general approach where standardized analysis, verification, simulation, and model checking techniques can be applied to the KBS.

BEAT: A Web-Based Boolean Expression Fault-Based Test Case Generation Tool

ERIC Educational Resources Information Center

Chen, T. Y.; Grant, D. D.; Lau, M. F.; Ng, S. P.; Vasa, V. R.

2006-01-01

BEAT is a Web-based system that generates fault-based test cases from Boolean expressions. It is based on the integration of our several fault-based test case selection strategies. The generated test cases are considered to be fault-based, because they are aiming at the detection of particular faults. For example, when the Boolean expression is in…

Developing an ontological explosion knowledge base for business continuity planning purposes.

PubMed

Mohammadfam, Iraj; Kalatpour, Omid; Golmohammadi, Rostam; Khotanlou, Hasan

2013-01-01

Industrial accidents are among the most known challenges to business continuity. Many organisations have lost their reputation following devastating accidents. To manage the risks of such accidents, it is necessary to accumulate sufficient knowledge regarding their roots, causes and preventive techniques. The required knowledge might be obtained through various approaches, including databases. Unfortunately, many databases are hampered by (among other things) static data presentations, a lack of semantic features, and the inability to present accident knowledge as discrete domains. This paper proposes the use of Protégé software to develop a knowledge base for the domain of explosion accidents. Such a structure has a higher capability to improve information retrieval compared with common accident databases. To accomplish this goal, a knowledge management process model was followed. The ontological explosion knowledge base (EKB) was built for further applications, including process accident knowledge retrieval and risk management. The paper will show how the EKB has a semantic feature that enables users to overcome some of the search constraints of existing accident databases.

Clips as a knowledge based language

NASA Technical Reports Server (NTRS)

Harrington, James B.

1987-01-01

CLIPS is a language for writing expert systems applications on a personal or small computer. Here, the CLIPS programming language is described and compared to three other artificial intelligence (AI) languages (LISP, Prolog, and OPS5) with regard to the processing they provide for the implementation of a knowledge based system (KBS). A discussion is given on how CLIPS would be used in a control system.

Consulting as a Strategy for Knowledge Transfer

PubMed Central

Jacobson, Nora; Butterill, Dale; Goering, Paula

2005-01-01

Academic researchers who work on health policy and health services are expected to transfer knowledge to decision makers. Decision makers often do not, however, regard academics’ traditional ways of doing research and disseminating their findings as relevant or useful. This article argues that consulting can be a strategy for transferring knowledge between researchers and decision makers and is effective at promoting the “enlightenment” and “interactive” models of knowledge use. Based on three case studies, it develops a model of knowledge transfer–focused consulting that consists of six stages and four types of work. Finally, the article explores how knowledge is generated in consulting and identifies several classes of factors facilitating its use by decision makers. PMID:15960773

A Comparison of Books and Hypermedia for Knowledge-based Sports Coaching.

ERIC Educational Resources Information Center

Vickers, Joan N.; Gaines, Brian R.

1988-01-01

Summarizes and illustrates the knowledge-based approach to instructional material design. A series of sports coaching handbooks and hypermedia presentations of the same material are described and the different instantiations of the knowledge and training structures are compared. Figures show knowledge structures for badminton and the architecture…

A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations

PubMed Central

2017-01-01

Evidence-based dietary information represented as unstructured text is a crucial information that needs to be accessed in order to help dietitians follow the new knowledge arrives daily with newly published scientific reports. Different named-entity recognition (NER) methods have been introduced previously to extract useful information from the biomedical literature. They are focused on, for example extracting gene mentions, proteins mentions, relationships between genes and proteins, chemical concepts and relationships between drugs and diseases. In this paper, we present a novel NER method, called drNER, for knowledge extraction of evidence-based dietary information. To the best of our knowledge this is the first attempt at extracting dietary concepts. DrNER is a rule-based NER that consists of two phases. The first one involves the detection and determination of the entities mention, and the second one involves the selection and extraction of the entities. We evaluate the method by using text corpora from heterogeneous sources, including text from several scientifically validated web sites and text from scientific publications. Evaluation of the method showed that drNER gives good results and can be used for knowledge extraction of evidence-based dietary recommendations. PMID:28644863

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.

Introduction: The Growing Importance of Traditional Forest-Related Knowledge

Treesearch

Ronald L. Trosper; John A. Parrotta

2012-01-01

The knowledge, innovations, and practices of local and indigenous communities have supported their forest-based livelihoods for countless generations. The role of traditional knowledge—and the bio-cultural diversity it sustains—is increasingly recognized as important by decision makers, conservation and development organizations, and the scientifi c community. However...

Coal and Coal/Biomass-Based Power Generation

EPA Science Inventory

For Frank Princiotta's book, Global Climate Change--The Technology Challenge Coal is a key, growing component in power generation globally. It generates 50% of U.S. electricity, and criteria emissions from coal-based power generation are being reduced. However, CO2 emissions m...

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

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).

Increasing home dialysis knowledge through a web-based e-learning program.

PubMed

Bennett, Paul N; Jaeschke, Sadie; Sinclair, Peter M; Kerr, Peter G; Holt, Steve; Schoch, Monica; Fortnum, Debbie; Ockerby, Cherene; Kent, Bridie

2014-06-01

There has been a global decline in the uptake of home-based dialysis therapies in the past 20 years. The ability to provide appropriate information to potential patients in this area may be confounded by a lack of knowledge of home dialysis options. The aim of this study was to develop a web-based education package for health professionals to increase knowledge and positive perceptions of home-based dialysis options. A three-module e-learning package concerning home dialysis was developed under the auspices of the home dialysis first project. These modules were tested on 88 undergraduate health professionals. Changes in attitudes and knowledge of home dialysis were measured using custom designed surveys administered electronically to students who completed the modules. Matched pre and post responses to the survey items were compared using Wilcoxon signed rank tests. The pre survey indicated clear deficits in existing knowledge of home dialysis options. In particular, when asked if haemodialysis could be performed at home, 22% of participants responded 'definitely no' and a further 24% responded 'probably no'. Upon completion of the e-learning, post survey responses indicated statistically significant improvements (P < 0.001) in eight of the nine items. When asked if the e-learning had increased their knowledge about home dialysis, 99% of participants responded 'definitely yes'. A suite of web-based education modules can successfully deliver significant improvements in awareness and knowledge around home dialysis therapies. © 2014 Asian Pacific Society of Nephrology.

Defaults, context, and knowledge: alternatives for OWL-indexed knowledge bases.

PubMed

Rector, A

2004-01-01

The new Web Ontology Language (OWL) and its Description Logic compatible sublanguage (OWL-DL) explicitly exclude defaults and exceptions, as do all logic based formalisms for ontologies. However, many biomedical applications appear to require default reasoning, at least if they are to be engineered in a maintainable way. Default reasoning has always been one of the great strengths of Frame systems such as Protégé. Resolving this conflict requires analysis of the different uses for defaults and exceptions. In some cases, alternatives can be provided within the OWL framework; in others, it appears that hybrid reasoning about a knowledge base of contingent facts built around the core ontology is necessary. Trade-offs include both human factors and the scaling of computational performance. The analysis presented here is based on the OpenGALEN experience with large scale ontologies using a formalism, GRAIL, which explicitly incorporates constructs for hybrid reasoning, numerous experiments with OWL, and initial work on combining OWL and Protégé.

Rangeland degradation assessment: a new strategy based on the ecological knowledge of indigenous pastoralists

NASA Astrophysics Data System (ADS)

Behmanesh, Bahareh; Barani, Hossein; Abedi Sarvestani, Ahmad; Shahraki, Mohammad Reza; Sharafatmandrad, Mohsen

2016-04-01

In a changing world, the prevalence of land degradation is becoming a serious problem, especially in countries with arid and semi-arid rangelands. There are many techniques to assess rangeland degradation that rely on scientific knowledge but ignore indigenous people. Indigenous people have accumulated precious knowledge about land management through generations of experience. Therefore, a study was conducted to find out how indigenous people assess rangeland degradation and how their ecological knowledge can be used for rangeland degradation assessment. Interviews were conducted with the pastoralists of two sites (Dasht and Mirza Baylu), where part of both areas is located in Golestan National Park (north-eastern Iran). A structured questionnaire was designed based on 17 indicators taken from literature and also primary discussions with pastoralists in order to evaluate land degradation. A qualitative Likert five-point scale was used for scoring rangeland degradation indicators. The results revealed that pastoralists pay more attention to edaphic indicators than to vegetative and other indicators. There were significant differences between the inside and outside of the park in terms of rangeland degradation indicators for both sites. The results show that the rangelands outside of the park in both sites were degraded compared to those inside of the park, especially in the areas close to villages. It can be concluded that pastoralists have a wealth of knowledge about the vegetation and grazing animal habits that can be used in rangeland degradation assessment. It is therefore necessary to document their ecological indigenous knowledge and involve them in the process of rangeland-degradation assessment.

Constructing Knowledge Bases: A Promising Instructional Tool.

ERIC Educational Resources Information Center

Trollip, Stanley R.; Lippert, Renate C.

1987-01-01

Argues that construction of knowledge bases is an instructional tool that encourages students' critical thinking in problem solving situations through metacognitive experiences. A study is described in which college students created expert systems to test the effectiveness of this method of instruction, and benefits for students and teachers are…