Knowledge Representation

Abstract: Based on a geometrical interpretation of knowledge space, this work defines relationships between data, concepts and models, and establishes a framework for their integration. Concepts encapsulate our knowledge and provide a basis for data acquisition. They change as we learn more. Every discipline operates with a specific set of concepts organized in models. In order to co-process data collected against different concepts, we need to map the underlying concepts. Modal Intentional Actual (MIA) structure, derived from knowledge representation theory, enables the separation of data from hypotheses, and provides a consistent approach for building data models, concept mapping and defining complex relationships, which are represented by morphisms in category theory. Essential data elements from enterprise modeling techniques provide specifications for storing concepts and morphisms in a database.

 ontology;  knowledge acquisition;  Knowledge Representation;  data models;  concept mapping;  data integration;  

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Abstract: Based on a geometrical interpretation of knowledge space, this work defines relationships between data, concepts and models, and establishes a framework for their integration. Concepts encapsulate our knowledge and provide a basis for data acquisition. They change as we learn more. Every discipline operates with a specific set of concepts organized in models. In order to co-process data collected against different concepts, we need to map the underlying concepts. Modal Intentional Actual (MIA) structure, derived from knowledge representation theory, enables the separation of data from hypotheses, and provides a consistent approach for building data models, concept mapping and defining complex relationships, which are represented by morphisms in category theory. Essential data elements from enterprise modeling techniques provide specifications for storing concepts and morphisms in a database.

 ontology;  knowledge acquisition;  Knowledge Representation;  data models;  concept mapping;  data integration;  

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 Knowledge Representation;  

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 Visualization;  data visualisation;  decision support systems;  Knowledge Representation;  knowledge domain mapping, semantics;  

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 ontology;  Knowledge Representation;  events;  qualitative reasoning;  

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 ontology;  Knowledge Representation;  semantic data models;  semantic search;  

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 Knowledge Representation;  

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 ontology;  knowledge discovery;  Knowledge Representation;  semantic data models;  semantic information spaces;  

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 Knowledge Representation;  agents;  

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 Visualization;  Knowledge Representation;  

Abstract: This chapter reviews visualization techniques that can not only be utilized to map the evergrowing domain structure of scientific disciplines but that also support information retrieval and classification. In contrast to the comprehensive surveys done in a traditional way by Howard White and Katherine McCain (1997; 1998), the current survey not only reviews emerging techniques in interactive data analysis and information visualization, but also visualizes bibliographical structures of the field as an integral part of our methodology. The chapter starts with a review of the history of knowledge domain visualizations. We then introduce a general process flow for the visualization of knowledge domains and explain commonly used techniques. In the interest of visualizing the domain this article reviews, we introduce a bibliographic data set of considerable size, which includes articles from the citation analysis, bibliometrics, semantics, and visualization literatures. Using a tutorial style, we then apply various algorithms to demonstrate the visualization effects produced by different approaches and compare the different visualization results. At the same time, the domain visualizations reveal the relationships within and between the four fields that together form the topic of this chapter, domain visualization. We conclude with a discussion of promising new avenues of research and a general discussion.

 Visualization;  Knowledge Representation;  knowledge domain mapping, semantics;  Survey;  

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Abstract: The National Science Digital Library (NSDL), launched in December 2002, is emerging as a center of innovation in digital libraries as applied to education. As a part of this extensive project, the GetSmart system was created to apply knowledge management techniques in a learning environment. The design of the system is based on an analysis of learning theory and the information search process. Its key notion is the integration of search tools and curriculum support with concept mapping. More than 100 students at the University of Arizona and Virginia Tech used the system in the fall of 2002. A database of more than one thousand student-prepared concept maps has been collected with more than forty thousand relationships expressed in semantic, graphical, node-link representations. Preliminary analysis of the collected data is revealing interesting knowledge representation patterns.

 Knowledge Representation;  digital libraries;  knowledge management;  concept mapping;  Distance Learning;  e-learning;  educational courses;  intelligent tutoring systems;  query formulation;  vocabulary;  GetSmart system;  NSDL;  National Science Digital Library;  curriculum support;  graphical representation;  information search process;  knowledge representation pattern;  learning theory analysis;  node-link representation;  search tool integration;  semantic representation;  student-prepared concept map database;  

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 Knowledge Representation;  spatial representation;  

Abstract: To make knowledge visualizations clear and easy to interpret, we have developed a method that extends and transforms traditional author co-citation analysis by extracting structural patterns from the scientific literature and representing them in a 3D knowledge landscape

 data visualisation;  citation analysis;  3D knowledge landscape;  author co-citation analysis;  knowledge domain intellectual structure visualization;  scientific literature;  structural pattern extraction;  Knowledge Representation;  

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 Knowledge Representation;  

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 Knowledge Representation;  

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 Knowledge Representation;  

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 Knowledge Representation;  knowledge domain mapping, semantics;  clustering methods;  

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 Knowledge Representation;  

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 Knowledge Representation;  AI;  

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 Knowledge Representation;  

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 Knowledge Representation;  chap 3;  spatial cognition;  

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 Knowledge Representation;  

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 Knowledge Representation;  chap 4;  chap 5;  

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 Knowledge Representation;  

Abstract: Knowledge representation is at the heart of every artificial intelligence system. The issues involved in the design of a representation are not restricted to technical aspects of the representation formalism (storage, access, inference, assimilation, consistency), but include the modeling process (relevance and granularity issues). We propose an extended knowledge representation model, characterized by making the role of the observer explicit. We use the representation model to look into the various modalities of representation such as declarative, procedural, prepositional, analogical, etc. Generally only some aspects of a representation correspond to a particular modality, depending on the level of abstraction considered. The concept of qualitativeness is found to be orthogonal to the modalities discussed.

 Knowledge Representation;  

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 Knowledge Representation;  

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 Knowledge Representation;  chap 7;  chap 8;  

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 Knowledge Representation;  

Abstract: In this paper, we review recent work aimed at the application of declarative logic programming to knowledge representation in artificial intelligence. We consider extensions of the language of definite logic programs by classical (strong) negation, disjunction, and some modal operators and show how each of the added features extends the representational power of the language. We also discuss extensions of logic programming allowing abductive reasoning, meta-reasoning and reasoning in open domains. We investigate the methodology of using these languages for representing various forms of nonmonotonic reasoning and for describing knowledge in specific domains. We also address recent work on properties of programs needed for sucessful applications of this methodology such as consistency, categoricity and complexity. 1 Contents 1 Introduction 1 1.1 Historical Perspective : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 1.2 Structure of the Paper : : : : : : : : :...

 Knowledge Representation;  

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Abstract: Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it—What is it?—has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties that are important to the notion of representation in general. In this article, we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and, at times, conflicting demands on the properties a representation should have. We argue that keeping in mind all five of these roles provides a usefully broad perspective that sheds light on some long-standing disputes and can invigorate both research and practice in the field.

 contextual knowledge representation;  Knowledge Representation;  

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Abstract: This special issue of Jetai is devoted to papers concerning the knowledge representation language of Conceptual Graphs. The intent of this overview is to give readers a general understanding of what conceptual graphs are and how the formalism operates. Unfortunately, this overview is somewhat brief; however, it should provide a sufficient background to enable the reader to follow the papers in this issue. Interested readers should refer to John Sowa's book, Conceptual Structures, for a fuller account

 Knowledge Representation;  book;  data models;  

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 Knowledge Representation;  

Abstract: Since its very beginnings, Artificial Intelligence (AI) has rested on a foundation of formal representation of knowledge. To date, AI systems have almost universally relied on knowledge bases of symbolically encoded world knowledge and associated formal inference algorithms, which draw implicit conclusions from explicitly represented facts. While Knowledge Representation — the research area that directly addresses languages for representation and the inferences that go along with them — has always been important in AI, the 1980's saw a groundswell of new work in the area, and as we engage the '90's, the field continues to grow and evolve. In this brief overview, I introduce the area, outlining its goals and some of its key concerns. I offer some brief historical remarks and a short description of the evolution of the field over the last dozen years, and conclude with some directions that will carry the field into the mid-’90’s.

 Knowledge Representation;  

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 Knowledge Representation;  chap 3;  

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 Knowledge Representation;  data models;  

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 Knowledge Representation;  chap 3;  

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 Knowledge Representation;  chap5;  chap4;  

Abstract: Some general issues about the differences in considering time are considered, with examples. The way to consider time is strictly tied to the way of seeing it, as to the way with which the time shows itself.... The knowledge we have of the time is the knowledge we may get of the hidden meaning of those visible things. The processes of knowledge have developed in three main directions which have always been separated in the past: iconic, logical-conceptual and mathematical.

 Knowledge Representation;  time perception;  

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 Knowledge Representation;  

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 Knowledge Representation;  spatial data models;  

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 Knowledge Representation;  

Abstract: Analysis the philosophical assumptions and implications of current AI representation schemes, in particular, those dealing with the underlying cognitive processes of language. Many of the conceptions of knowledge representation have been dominated by a traditional view which sees language as based on logic. This view, known as ideal language, protrays language as essentially literal and truth-functional (proponents being called, the neats). Not only does the author vigorously attack such a picture of language and lnowledge representation, she argues that the cognitive mechanisms involved in metaphor provide a better model for structuring knowledge than that of first-order logic. The result is Dynamic Type Hierarchy theory, a theory which represents an entirely new approach to metaphor, language and knowledge representation. ---- Way rejects other solutions to selection which have a universal algorithm character. In particular, Way considers the structural constraints in these models to be inadequate for selection. (This may be a bit rash - Structural constraints, like systematicity, have proven to be very powerful selection mechanisms. Way fails to appreciate their power because the book was completed before these models were proposed. The book suffers from a long delay between its completion and publication.

 Knowledge Representation;  book;  

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 Knowledge Representation;  chap 3;  

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 Knowledge Representation;  artificial intelligence;  

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 Knowledge Representation;  shape;  

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 Knowledge Representation;  spatial perception;  

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 Knowledge Representation;  linguistics;  

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 Knowledge Representation;  

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 spatial relations;  Knowledge Representation;