Knowledge representation in LILOG

Pletat, Udo; Luck, Kai von

doi:10.1007/3-540-52337-6_22

Cited by 17 publications

(3 citation statements)

References 11 publications

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“…In these representation formalisms, however, usually no additional, more coarse-grained structuring primitives are available which might constitute the counterpart to modules in traditional computer science. Conversely, several proposals have been made for modularizing logic programs or logical theories (cf., e.g., [9], [10], [11]). These proposals usually use modules or contexts as the only structuring primitive, i.e.…”

Section: Introductionmentioning

confidence: 99%

Mechanisms for structuring knowledge-based systems

Landes

Studer

1994

Lecture Notes in Computer Science

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In order to reduce the complexity of large knowledge-based systems and promote reusability, means for decomposing them to smaller chunks are required. MIKE, our knowledge engineering framework, provides three basic means for structuring which are described in this paper: different kinds of knowledge are separated at different knowledge layers, knowledge layers can be structured by modules, and knowledge within modules is expressed in terms of an object-centred data model. In addition, ideas from entity relationship model clustering are adapted and extended to facilitate the understandability of domain knowledge and support the formation of modules.

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Section: Introductionmentioning

confidence: 99%

Mechanisms for structuring knowledge-based systems

Landes

Studer

1994

Lecture Notes in Computer Science

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“…The knowledge representation language LI, ILOG being processed by our deductive component has been described in lnore detail in [6]. It can be characterized as an advanced order-sorted predicate logic extended by the following features: * A type system offering a rich sort description language integrating concepts from the KL-ONE family of languages (see [3]) and unification-based grammar formMisms, c.f.…”

Section: Introductionmentioning

confidence: 99%

The LILOG knowledge representation system

Bollinger¹,

Pletat²

1991

SIGART Bull.

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The LILOG knowledge representation system is part of LBU/2 -the LILOG Experimentier Umgebung J -a naturM language understanding system for German. The knowledge representation system comprises a sophisticated knowledge representation language based on order-sorted predicate logic enriched by a type system of KL-ONE like languages, default reasoning, and the capalfility to delegate inferences to external deductive components. Tile inference engine processing Ltu.oc can be considered as an experimental theorem proving shell since, h~r example, we are able to exchange inference calculi and search strategies very easily. We sketch the knowledge representation language LHLOG, give an overview of the internal architecture of tile LILOG inference engine, and show how tile inference engine is embedded into the naturM language understanding system LEU/2.

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“…The stuf formalism [ Bouma et al, 1988;Dörre and Seiffert, 1991 ] is based on feature logic and is employed in the linguistic components. The knowledge representation language l-lilog [ Pletat and von Luck, 1990;Pletat, 1991 ] is a hybrid formalism combining predicate logic and attributive descriptions.…”

Section: Introductionmentioning

confidence: 99%

Attributive description formalisms ... and the rest of the world

Nebel¹,

Smolka

1991

Text Understanding in LILOG

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Research in knowledge representation has led to the development of so-called terminological logics, the purpose of which is to support the representation of the conceptual and terminological part of Artificial Intelligence applications. Independently, in computational linguistics, so-called feature logics have been developed which are aimed at representing the semantic and syntactic information natural language sentences convey. Since both of these logics rely mainly on attributes as the primary notational primitives for representing knowledge, they can be jointly characterized as attributive description formalisms.Although the intended applications for terminological logics and feature logics are not identical, and the computational services of systems based on the respective formalisms are quite different for this reason, the logical foundations turn out to be very similar -as we pointed out elsewhere. In this paper, we will show how attributive description formalisms relate to "the rest of the world." Recently, a number of formal results in the area of attributive description formalisms have been obtained by exploiting other research fields, such as formal language theory, automata theory, and modal logics. This connection between these different fields of formal research will be highlighted in the sequel.

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Knowledge representation in LILOG

Cited by 17 publications

References 11 publications

Mechanisms for structuring knowledge-based systems

Mechanisms for structuring knowledge-based systems

The LILOG knowledge representation system

Attributive description formalisms ... and the rest of the world

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