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2006
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| |  | Brazhnik, Olga | Databases and the geometry of knowledge read moreAbstract: 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. | 2006 |
| | | Brazhnik, Olga | Databases and the geometry of knowledge read moreAbstract: 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. | 2006 |
2005
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| | | Amar, Robert A. | Knowledge Precepts for Design and Evaluation of Information Visualizations read moreAbstract: Sorry no abstract available for this article | 2005 |
2003
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| | | Borner, K. | Visualizing knowledge domains read moreAbstract: 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. | 2003 |
| | | Marshall, B. | Convergence of knowledge management and e-learning: the GetSmart experience read moreAbstract: 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. | 2003 |
2001
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| | | Chen, Chaomei | Visualizing a knowledge domain's intellectual structure read moreAbstract: 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 | 2001 |
1994
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| | | Baral, Chitta | Logic Programming and Knowledge Representation read moreAbstract: 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 : : : : : : : : :... | 1994 |
| | | | A cognitive perspective on knowledge representation read moreAbstract: 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. | 1994 |
1993
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| | | Davis, Randall | What is a Knowledge Representation read moreAbstract: 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. | 1993 |
1992
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| | | Brachman, Ronald | What is knowledge representation, and where is it going? read moreAbstract: 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. | 1992 |
| | | Way, Eileen C. | Conceptual Graph Overview read moreAbstract: 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 | 1992 |
1991
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| | | Reichgelt, Han | Knowledge Representation: An Ai Perspective read moreAbstract: Sorry no abstract available for this article | 1991 |
1988
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| | | Peuquet, Donna J. | Representations of Geographic Space: Toward a Conceptual Synthesis read moreAbstract: The need for a conceptually high level, unifying representational scheme for geographic phenomena was recognized long ago. A number of attempts to address this issue have been made in the past, and each has usually been centered around a specific theoretical point of view (Bartels 1982). This topic has reappeared recently within the context of geographical information systems. Recognizing that the representational scheme employed in large part determines the efficiency and ease of use withing a given application context, there has been much activity in developing better methods for representating geographic data in digital form. Nevertheless, porgress has been slow, with shuch activity usually focused on narrowly viewed implementational issues with no clear answers or overall insights toward solving the overall problem. This vaccum suggests that need for a more unified approach to both research in methods of representing geographic data and practical geographic database design based on a common and unified framework. Representational theories for spatial and non-spatial phenomena developed within other fields, particularly cognitive and perceptual psychology, computer vision and database management systems, are examined within the context of the geographic literature. Drawing on and combining these concepts with a top-down approach, I suggest a set of unifying principles and an overall framework for representing geographical phenomena based on these principles. This effor includes an enumeration of basic types of spatial relationships and their characteristics. Although the framework as presented is very general, it seems to represent a complete blending of previous geometric and preceptual appraoches, demonstrating that these appraoches are not only compatible but complementary. The theories developed in other disciplines provide insight into the functional relationship between the image-based and object-based views that have existed implicitly in Geography for many years | 1988 |
1986
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| | | Delgrande, J. P. | Knowledge representation: features of knowledge read moreAbstract: Sorry no abstract available for this article | 1986 |
1985
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| | | Haugeland, John | Artificial intelligence: the very idea read moreAbstract: Sorry no abstract available for this article | 1985 |
1980
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| | | Rumelhart, D. E. | Schemata: The Building Blocks of Cognition read moreAbstract: Sorry no abstract available for this article | 1980 |
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