Abstract not found

Terminological Knowledge Representation Systems (TKRSs) are tools for designing and using knowledge bases that make use of terminological languages (or concept languages). The TKRS we consider in this paper is of practical interest since it goes beyond the capabilities of presently available TKRSs. First, our TKRS is equipped with a highly expressive concept, language, called ALCNR, including general complements of concepts, number restrictions and role conjunction. Second, it allows one to express inclusion statements between general concepts, in particular to express terminological cycles. We provide a sound, complete and terminating calculus for reasoning in ALCNR-knowledge bases based on the general technique of constraint systems.

Description logics are a family of knowledge representation formalisms that are descended from semantic networks and frames via the system Kl-one. During the last decade, it has been shown that the important reasoning problems (like subsumption and satisfiability) in a great variety of description logics can be decided using tableau-like algorithms. This is not very surprising since description logics have turned out to be closely related to propositional modal logics and logics of programs (such as propositional dynamic logic), for which tableau procedures have been quite successful. Nevertheless, due to different underlying intuitions and applications, most description logics differ significantly from run-of-the-mill modal and program logics. Consequently, the research on tableau algorithms in description logics led to new techniques and results, which are, however, also of interest for modal logicians. In this article, we will focus on three features that play an important role in description logics (number restrictions, terminological axioms, and role constructors), and show how they can be taken into account by tableau algorithms.

We presenan integrated system for knowledge representation, called AL-log, based on description logics and the deductive database language Datalog. AL-log embodies two subsystems, called structural and relational. The former allows for the definition of structural knowledge about classes of interest #concepts# and membership relation between objects and classes. The latter allows for the definition of relational knowledge about objects described in the structural component. The interaction between the two components is obtained by allowing constraints within Datalog clauses, thus requiring the variables in the clauses to range over the set of instances of a specified concept. We propose a method for query answering in AL-log based on constrained resolution, where the usual deduction procedure defined for Datalog is integrated with a method for reasoning on the structural knowledge.

Description Logics (DLs) are suitable, well-known, logics for managing structured knowledge. They allow reasoning about individuals and well defined concepts, i.e. set of individuals with common properties. The experience in using DLs in applications has shown that in many cases we would like to extend their capabilities. In particular, their use in the context of Multimedia Information Retrieval (MIR) leads to the convincement that such DLs should allow the treatment of the inherent imprecision in multimedia object content representation and retrieval. In this paper we will present a fuzzy extension of ALC, combining...

We consider different methods of optimizing the classification process of terminological representation systems, and evaluate their effect on three different types of test data. Though these techniques can probably be found in many existing systems, until now there has been no coherent description of these techniques and their impact on the performance of a system. One goal of this paper is to make such a description available for future implementors of terminological systems. Building the optimizations that came off best into the kris system greatly enhanced its efficiency.

The vision of a Semantic Web has recently drawn considerable attention, both from academia and industry. Description logics are often named as one of the tools that can support the Semantic Web and thus help to make this vision reality.

. We describe CARIN, a novel family of representation languages, which integrate the expressive power of Horn rules and of description logics. We address the key issue in designing such a language, namely, providing a sound and complete inference procedure. We identify existential entailment as a core problem in reasoning in CARIN, and describe an existential entailment algorithm for CARIN languages whose description logic component is ALCNR. This algorithm entails several important results for reasoning in CARIN, most notably: (1) a sound and complete inference procedure for non recursive CARIN-ALCNR, and (2) an algorithm for determining rule subsumption over ALCNR. 1 Introduction Horn rule languages have formed the basis for many Artificial Intelligence application languages because their expressive power is sufficient for many applications, and they have good computational properties. One of the significant limitations of Horn rules is that they are not expressive enough to mod...

Abstract not found

The need for N a ur a La ngua' Interfa3; to da ta ba ses (NLIs) ha s become increa3] glya cute a more a d more peoplea ccess informa ion through their web browsers, PDAs, a d cell phones.Yet NLIsa re only usa ble if theyma p na tura la gu a. questions to SQL queries correctly As Schneiderma a d Norma ha vea rgued, people a. unwilling to tr a e relia lea nd predicta ble user interfaMM for intelligent but unrelia ble ones In thispa per, we introducea theoretica l fra mework for relia ble NLIs, which is the founda tion for the fully implemented Precise NLI We prove tha t, fora broa cla3 of semantically tractable tura la gu a. questions, Precise is gua.' teed to ma p e a h question to the corresponding SQL query We report on experiments testing Precise on severa l hundred questions dra wn from user studies over three benchma rkda ta ba ses We findtha t over 80% of the questionsa' sem a tica;M traE a ble questions, which Precise a swers correctly lly recognizes the 20% of questions tha t it c a not h a dle, a d requestsa pa ra phra se Fina lly, we show tha t Precise compa res fa vora ly with Mooney'slea: ing NLIa nd with Microsoft 's English Query product Categories and Subject Descriptors H 2 3 [Database Management]: Query L a gua.E3 SQL; H 5 2 [Information Interfaces and Presentation]: User Interfa:M , N a ur a La ngua' General Terms gua:------ Algorithms, Relia ility Keywords tura la gu a. interfa'H da ta ba se, relia ility # We th a k Keith Golden, D a Weld, Bonnie Weber a d Tessa La for comments on previous dra' s Thisresea] h wa s supported in pa' by ONRgra t N00014-02-1-0324 Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for prof...