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A framework for semantic-based similarity measures for ELH-concepts

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dc.contributor.authorLehmann, Karsten
dc.contributor.authorTurhan, Anni-Yasmin
dc.coverage.spatialToulouse France
dc.date.accessioned2015-12-10T23:31:34Z
dc.date.available2015-12-10T23:31:34Z
dc.date.createdSeptember 26-28 2012
dc.date.issued2012
dc.date.updated2016-02-24T08:50:59Z
dc.description.abstractSimilarity measures for concepts written in Description Logics (DLs) are often devised based on the syntax of concepts or simply by adjusting them to a set of instance data. These measures do not take the semantics of the concepts into account and can thus lead to unintuitive results. It even remains unclear how these measures behave if applied to new domains or new sets of instance data. In this paper we develop a framework for similarity measures for ELH-concept descriptions based on the semantics of the DL ELH. We show that our framework ensures that the measures resulting from instantiations fulfill fundamental properties , such as equivalence invariance, yet the framework provides the flexibility to adjust measures to specifics of the modelled domain.
dc.identifier.isbn9783642333521
dc.identifier.urihttp://hdl.handle.net/1885/68695
dc.publisherSpringer
dc.relation.ispartofseriesEuropean Conference on Logics in Artificial Intelligence (JELIA 2012)
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.subjectKeywords: Description logic; Fundamental properties; Similarity measure; Artificial intelligence; Data description; Semantics
dc.titleA framework for semantic-based similarity measures for ELH-concepts
dc.typeConference paper
local.bibliographicCitation.lastpage319
local.bibliographicCitation.startpage307
local.contributor.affiliationLehmann, Karsten, College of Engineering and Computer Science, ANU
local.contributor.affiliationTurhan, Anni-Yasmin, Institute for Theoretical Computer
local.contributor.authoruidLehmann, Karsten, u5105743
local.description.notesImported from ARIES
local.identifier.absfor089999 - Information and Computing Sciences not elsewhere classified
local.identifier.ariespublicationf5625xPUB1801
local.identifier.doi10.1007/978-3-642-33353-8_24
local.identifier.scopusID2-s2.0-84866907922
local.type.statusPublished Version

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