Lean kernels in description logics

Rafael Peñaloza, Carlos Mencía, Alexey Ignatiev, Joao Marques-Silva

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7 Citations (Scopus)

Abstract

Lean kernels (LKs) are an effective optimization for deriving the causes of unsatisfiability of a propositional formula. Interestingly, no analogous notion exists for explaining consequences of description logic (DL) ontologies. We introduce LKs for DLs using a general notion of consequence-based methods, and provide an algorithm for computing them which incurs in only a linear time overhead. As an example, we instantiate our framework to the DL ALC. We prove formally and empirically that LKs provide a tighter approximation of the set of relevant axioms for a consequence than syntactic locality-based modules.

Original languageEnglish
Title of host publicationThe Semantic Web
Subtitle of host publication14th International Conference, ESWC 2017 Portorož, Slovenia, May 28 – June 1, 2017 Proceedings, Part I
EditorsEva Blomqvist, Diana Maynard, Aldo Gangemi, Rinke Hoekstra, Pascal Hitzler, Olaf Hartig
Place of PublicationCham Switzerland
PublisherSpringer
Pages518-533
Number of pages16
ISBN (Electronic)9783319580685
ISBN (Print)9783319580678
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventEuropean Semantic Web Conference 2017 - Portoroz, Slovenia
Duration: 28 May 20171 Jun 2017
Conference number: 14th
http://2017.eswc-conferences.org/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10249
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Semantic Web Conference 2017
Abbreviated titleESWC 2017
Country/TerritorySlovenia
CityPortoroz
Period28/05/171/06/17
Internet address

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