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By Hermann Helbig

The booklet provides an interdisciplinary method of wisdom illustration and the remedy of semantic phenomena of average language, that is situated among synthetic intelligence, computational linguistics, and cognitive psychology. The proposed approach relies on Multilayered prolonged Semantic Networks (MultiNets), which are used for theoretical investigations into the semantics of traditional language, for cognitive modeling, for describing lexical entries in a computational lexicon, and for normal language processing (NLP). half I offers with primary difficulties of semantic wisdom illustration and semantic interpretation of ordinary language phenomena. half II presents a scientific description of the representational technique of MultiNet, some of the most entire and carefully exact collections of kin and services utilized in actual NLP functions. MultiNet is embedded right into a procedure of software program instruments comprising a workbench for the information engineer, a semantic interpreter translating common language expressions into formal which means constructions, and a workbench for the pc lexicographer. The booklet has been used for classes in man made intelligence at a number of universities and is among the cornerstones for instructing computational linguistics in a digital digital laboratory.

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7) with the description of the state of affairs “the house on the Schiller Square is large”, which is represented by the arc (G02 PROP large). In the first sentence (attributive use of “large”), the stated property belongs to the meaning range of the concept G01 and, therefore, to its concept capsule. In the second case (predicative use of “large”), the object G02 is already uniquely characterized by the phrase “the house at the Schiller Square” with [REFER = det] for G02. The node G02 is additionally described by the assertion of a property, which is the reason why the PROP arc has not been included into the definitional capsule.

2 and 15). Nevertheless, a further underpinning from linguistic or psychological quarters would be very helpful. Since a comparison with other knowledge representation formalisms and methods will be comprehensible only after an explanation of the representational means of MultiNet, this juxtaposition must be postponed to the end of this work (see Chap. 15). An overview of the representational means, which are described in full detail in Part II, is given in Fig. 2. 22 3. 2. 1 Sorts and Features Almost all paradigms of knowledge representation are based on a so-called ontology of the entities to be represented: ¯ A classification of concepts from an epistemic point of view, which to a certain degree also mirrors a classification of the real world according to ontological aspects, is called an ontology.

5. Overview of the multilayer model of MultiNet preextensional level corresponds to the sorts at the intensional level. The sorts are not shown in Fig. 5 (see Part II, Sect. 1). e. parts of the real or a possible world) could never be mirrored completely in a knowledge representation (be it on a computer or in the human mind). Sets – even finite sets – are always only exemplarily modeled by some selected elements or by a prototypical representative for the whole set, a fact which is also confirmed by psychological investigations [132].

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