A.M. Frisch ; Knowledge Retrieval as Specialized Inference ; Report No. 214, Department of Computer Science, University of Rochester, May 1987.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....and of a common semantics to define the relationship between expressions of different formalisms in a semantically sound manner. The decidability of all algorithms involved is achieved by adopting a four valued semantics based on the works of Belnap [2] Patel Schneider [12] 13] Frisch [9] and Thomason et al. 14] The system supports four different knowledge representation formalisms first order logic, frames, semantic nets and production systems with their associated inference mechanisms assertional reasoning, terminological reasoning, inheritance with exceptions and ....

....section 7, some applications and conclusions are presented. 2 The Semantics One of the main characteristics of Mantra is the definition of a unified semantics for knowledge representation and reasoning methods and their interaction. This semantics is based upon a four valued logic [2] 12] 13] [9] [14] Many knowledge representation systems support standard first order logic due to its naturality and expressive power. However, when reasoning about some formulae, these systems may never stop due to the fact that first order logic is undecidable. This situation is higly unsatisfatory if we ....

A.M. Frisch, Knowledge Retrieval as Specialized Inference, Report No. 214, University of Rochester, Department of Computer Science, 1987.

....performance of the problem solver, clearly the efficiency of the KR system is important. Recent attempts to deal with the intractability of such systems have generally fallen under two headings: limited languages (e.g. Patel Schneider 1984, Borgida et al. 1989] and limited inference (e.g. [Frisch 1988, Patel Schneider 1989] In the former, what can be expressed in the knowledge base (KB) is restricted (sometimes severely) to guarantee that queries can be answered in more or less reasonable time. In the latter, restrictions like avoiding chaining or four valued interpretations yield limited ....

Frisch, A. M. [1988], Knowledge Retrieval as Specialized Inference, Technical Report UIUCDCS-R-88-1404, Department of Computer Science, University of Illinois at Urbana-Champaign, February, 1988.

....in general knowledge and reasoning. Two ways of doing this involve restricting the notion of completeness, either by seeking completeness with respect to weaker logics (see, for example [31] or by seeking completeness with respect to ordinary deduction over sublanguages (see, for example [10, 11]) Virtually all the work on restricted language classification can be immediately reformulated as a contribution toward this end. Another useful approach is to permit the user to directly assert subsumption relationships in cases too hard for the classifier to determine, as is already done (in a ....

A. M. Frisch. Knowledge retrieval as specialized inference. Technical Report UIUCDCS-R-88-1404, Department of Computer Science, University of Illinois at Urbana-Champaign, 1988.

....complexity, we have outlined techniques that retain acceptable performance at the cost of some degradation in the soundness and or completeness of the representation and or query answering. Unlike some other approaches to achieving tractable reasoning with worst case intractable formalisms (c.f. Frisch, 1988 ] our approach has the advantage that its departures from sound and complete reasoning are motivated by relationships in the knowledge base. The structure of the system s knowledge plays a significant role in determining the system s performance in assimilating new knowledge and in reasoning ....

Frisch, A.M., Knowledge Retrieval as Specialized Inference, Technical Report UIUCDCS-R-88-1404, Department of Computer Science, University of Illinois at UrbanaChampaign, February 1988.

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Alan M. Frisch. Knowledge Retrieval as Specialized Inference. PhD thesis, Computer Science Department, University of Rochester, August 1986.

.... database system that incorporates special mechanisms for sort information [Reiter, 1977; Reiter, 1981] ffl systems that combine assertional information and terminological information (e.g. krypton [ and kl two [ ffl systems that combine a weak logic with a taxonomic representation (e.g. Frisch, 1986a] and [ ffl logic programming systems that integrate Horn clause reasoning with inheritance (e.g. login [ and horne [Frisch et al. 1983] or with more general forms of constraints (e.g. rhet [Allen and Miller, 1988] and constraint logic programming systems [Jaffar and Lassez, 1987; ....

.... of reasoners including logic programming systems [Allen et al. 1984; Frisch et al. 1983] general purpose resolution systems [Schmidt Schauss, 1989; Walther, 1987] a deductive database system [Reiter, 1977; Reiter, 1981] a parser for logic grammars [Frisch, 1986b] and a knowledge retriever [Frisch, 1986a] In spite of their popularity, it was not until 1986 [Frisch, 1986a] that anyone raised the possibility that all of these systems could be explained with a common set of principles. This is the first piece of work to examine these principles in a general setting. It should be pointed out, ....

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Alan M. Frisch. Knowledge Retrieval as Specialized Inference. PhD thesis, Computer Science Department, University of Rochester, August 1986.

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A.M. Frisch ; Knowledge Retrieval as Specialized Inference ; Report No. 214, Department of Computer Science, University of Rochester, May 1987.

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A.M. Frisch, Knowledge Retrieval as Specialized Inference, Report No. 214, University of Rochester, Department of Computer Science, 1987.

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Frisch, A. M., Knowledge Retrieval as Specialized Inference, Ph.D. Thesis, University of Rochester, 1986.

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Frisch,A.M., Knowledge Retrieval as Specialized Inference, Report No.214, Department of Computer Science, University of Rochester, May 1987.

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