An overview of the KL-ONE Knowledge Representation System
TLDR
KL-ONE as mentioned in this paper is a system for representing knowledge in Artificial Intelligence programs and has been used in both basic research and implemented knowledge-based systems in a number of places in the AI community.About:
This article is published in Cognitive Science.The article was published on 1985-04-01 and is currently open access. It has received 1719 citations till now. The article focuses on the topics: Knowledge representation and reasoning & KL-ONE.read more
Citations
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Knowledge engineering: principles and methods
TL;DR: The paradigm shift from a transfer view to a modeling view is discussed and two approaches which considerably shaped research in Knowledge Engineering are described: Role-limiting Methods and Generic Tasks.
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The generative lexicon
TL;DR: It is argued that lexical decomposition is possible if it is performed generatively and a theory of lexical inheritance is outlined, which provides the necessary principles of global organization for the lexicon, enabling us to fully integrate the authors' natural language lexicon into a conceptual whole.
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Development and application of a metric on semantic nets
TL;DR: Experiments in which distance is applied to pairs of concepts and to sets of concepts in a hierarchical knowledge base show the power of hierarchical relations in representing information about the conceptual distance between concepts.
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Semantic database modeling: survey, applications, and research issues
Richard Hull,Roger King +1 more
TL;DR: This paper provides a tutorial introduction to the primary components of semantic models, which are the explicit representation of objects, attributes of and relationships among objects, type constructors for building complex types, ISA relationships, and derived schema components.
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What Is a Knowledge Representation
TL;DR: It is argued that keeping in mind all five of these roles that a representation plays provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field.
References
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Word and Object
TL;DR: This edition offers a new preface by Quine's student and colleague Dagfinn Follesdal that describes the never-realized plans for a second edition of Word and Object, in which Quine would offer a more unified treatment of the public nature of meaning, modalities, and propositional attitudes.
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Naming and Necessity
TL;DR: In this paper, the authors make a connection between the mind-body problem and the so-called "identity thesis" in analytic philosophy, which has wide-ranging implications for other problems in philosophy that traditionally might be thought far-removed.
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TL;DR: This paper proposes a logic for default reasoning, develops a complete proof theory and shows how to interface it with a top down resolution theorem prover, and provides criteria under which the revision of derived beliefs must be effected.
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Principles of Artificial Intelligence
TL;DR: This classic introduction to artificial intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval.