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Characterizing the semantics of terminological cycles in ALN using finite automata

The representation of terminological knowledge may naturally lead to terminological cycles. In addition to descriptive semantics, the meaning of cyclic terminologies can also be captured by fixed-point semantics, namely, greatest and least fixed-point semantics. To gain a more profound understanding of these semantics and to obtain inference algorithms as well as complexity results for inconsistency, subsumption, and related inference tasks, this paper provides automata theoretic characterizations of these semantics. More precisely, the already existing results for FL₀ are extended to the language ALN, which additionally allows for primitive negation and number-restrictions. Unlike FL₀, the language ALN can express inconsistent concepts, which makes non-trivial extensions of the characterizations and algorithms necessary. Nevertheless, the complexity of reasoning does not increase when going from FL₀ to ALN. This distinguishes ALN from the very expressive languages with fixed-point operators proposed in the literature. It will be shown, however, that cyclic ALN-terminologies are expressive enough to capture schemata in certain semantic data models.

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:78817
Date19 May 2022
CreatorsKüsters, Ralf
PublisherAachen University of Technology
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/acceptedVersion, doc-type:report, info:eu-repo/semantics/report, doc-type:Text
Rightsinfo:eu-repo/semantics/openAccess
Relationurn:nbn:de:bsz:14-qucosa2-785040, qucosa:78504

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