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Journal ArticleDOI

Frame-based representation of philosophical systems using a knowledge engineering tool

01 Jul 1993-Computers and The Humanities (Kluwer Academic Publishers)-Vol. 27, Iss: 4, pp 261-275
TL;DR: It is argued that computer aided text analysis can apply knowledge representation tools and techniques developed in artificial intelligence and it is estimated how philosophers as well as knowledge engineers could gain from this cross-fertilization.
Abstract: This article addresses the methodological problem of the non-linear representation of philosophical systems in a computerized knowledge base. It is a problem of knowledge representation as defined in the field of artificial intelligence. Instead of a purely theoretical discussion of the issue, we present selected results of a practical experiment which has in itself some theoretical significance. We show how one can represent different philosophies using CODE, a knowledge engineering system developed by artificial intelligence researchers. The hypothesis is that such a computer based representation of philosophical systems can give insight into their conceptual structure. We argue that computer aided text analysis can apply knowledge representation tools and techniques developed in artificial intelligence and we estimate how philosophers as well as knowledge engineers could gain from this cross-fertilization. This paper should be considered as an experiment report on the use of knowledge representation techniques in computer aided text analysis. It is part of a much broader project on the representation of conceptual structures in an expert system. However, we intentionally avoided technical issues related to either Computer Science or History of Philosophy to focus on the benefit to enhance traditional humanistic studies with tools and methods developed in AI on the one hand and the need to develop more appropriate tools on the other.
Citations
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Journal ArticleDOI
TL;DR: This paper adapts the GQM (Goals-Questions-Metrics) methodology that is used to select and develop software metrics to develop a series of metrics that measure the size and complexity of concept-oriented knowledge bases.
Abstract: Metrics are widely researched and used in software engineering; however there is little analogous work in the field of knowledge engineering. In other words, there are no widely-known metrics that the developers of knowledge bases can use to monitor and improve their work. In this paper we adapt the GQM (Goals-Questions-Metrics) methodology that is used to select and develop software metrics. We use the methodology to develop a series of metrics that measure the size and complexity of concept-oriented knowledge bases. Two of the metrics measure raw size; seven measure various aspects of complexity on scales of 0 to 1, and are shown to be largely independent of each other. The remaining three are compound metrics that combine aspects of the other nine in an attempt to measure the overall 'difficulty' or 'complexity' of a knowledge base. The metrics have been implemented and tested in the context of a knowledge management system called CODE4.

24 citations

DissertationDOI
01 Jan 1994
TL;DR: An idealized KNOWLEDGE REPRESENTATION SCHEMA describing representation principles, but containing simplify- ing assumptions or constructs that may not be finitely realizable.
Abstract: representation schema, abstract schema: (*) An idealized KNOWLEDGE REPRESENTATION SCHEMA describing representation principles, but containing simplify- ing assumptions or constructs that may not be finitely realizable. An abstract schema

20 citations

References
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Book ChapterDOI
01 Jan 1979
TL;DR: In this paper, the authors examine the history of semantic networks and propose a new type of network formalism called Structured Inheritance Networks (SINN), which allows the explicit expression of epistemological level relationships as network links.
Abstract: This chapter examines in detail the history of a set of network-structured formalisms for knowledge representation—the so-called “semantic networks.” Semantic nets were introduced around 1966 as a representation for the concepts underlying English words, and since then have become an increasingly popular type of language for representing concepts of a widely varying sort. While these nets have for the most part retained their basic associative nature, their primitive representational elements have differed significantly from one project to the next. These differences in underlying primitives are symptomatic of deeper philosophical disparities, and I discuss a set of five significantly different “levels” at which networks can be understood. One of these levels, the “epistemological,” or “knowledge-structuring,” level, has played an important implicit part in all previous notations, and is here made explicit in a way that allows a new type of network formalism to be specified. This new type of formalism accounts precisely for operations like individuation of description, internal concept structure in terms of roles and interrelations between them, and structured inheritance. In the final section, I present a brief sketch of an example of a particular type of formalism (“Structured Inheritance Networks”) that was designed expressly to treat concepts as formal representational objects. This language, currently under development, is called KLONE, and it allows the explicit expression of epistemological level relationships as network links.

767 citations

Book
01 Jan 1839

276 citations

Book
01 Jan 1996

242 citations

Journal ArticleDOI
TL;DR: The author discusses a number of issues that serve as research goals for discovering the principles of knowledge representation, using techniques and concepts evolved while developing the knowledge-representation system KL-one as illustrations.
Abstract: The author discusses a number of issues that serve as research goals for discovering the principles of knowledge representation, using techniques and concepts evolved while developing the knowledge-representation system KL-one as illustrations. The focus is on what constitutes a good representational system and a good set of representational primitives for dealing with an open-ended range of knowledge domains. Issues of interest include those problems that arise in attempting to construct intelligent computer programs that use knowledge to perform some task. 7 references.

85 citations