Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/49383
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dc.contributor.authorColucci, Simonait
dc.contributor.authorDonini, Francesco Mariait
dc.contributor.authorDi Sciascio, Eugenioit
dc.date.accessioned2023-03-21T17:46:13Z-
dc.date.available2023-03-21T17:46:13Z-
dc.date.issued2022it
dc.identifier.urihttp://hdl.handle.net/2067/49383-
dc.description.abstractEvaluating the similarity of RDF resources is nowadays a thoroughly investigated research problem, with reference to a variety of contexts. In fact, several tools are available for the comparison of pairs and/or groups of resources in a knowledge graph, mostly based on machine learning techniques. Unfortunately such tools, though extensively tested and fully scalable, return non-explainable (often numerical) similarity results also when comparing RDF resources, treating them according to their vector embeddings. and making no use of the semantic information carried by RDF triples. In this work, we propose a tool able to compute the commonalities of compared resource and explain them through a text in English, produced by a Natural Language Generation approach. The proposed approach is logic-based and is grounded on the computation of the Least Common Subsumer (re)defined in RDF. The feasibility of the tool is demonstrated with reference to the similarity of Twitter accounts.it
dc.format.mediumELETTRONICOit
dc.language.isoengit
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleA Human-readable Explanation for the Similarity of RDF Resourcesit
dc.typeconferenceObject*
dc.identifier.scopus2-s2.0-85142842652it
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85142842652it
dc.relation.journalCEUR WORKSHOP PROCEEDINGSit
dc.relation.seriesCEUR WORKSHOP PROCEEDINGSit
dc.relation.ispartofbook3rd Italian Workshop on Explainable Artificial Intelligenceit
dc.relation.firstpage88it
dc.relation.lastpage103it
dc.relation.numberofpages15it
dc.relation.alleditorsMusto C., Guidotti R., Monreale A., Semeraro G.it
dc.relation.conferencename3rd Italian Workshop on Explainable Artificial Intelligenceit
dc.relation.conferenceplaceUdineit
dc.relation.conferencedate29/11/2022-02/12/2022it
dc.relation.volume3277it
dc.subject.scientificsectoring-inf/05it
dc.subject.scientificsectorinf/01it
dc.subject.ercsectorPE6it
dc.description.numberofauthors3it
dc.description.internationalnoit
dc.contributor.countryITAit
dc.type.refereeREF_1it
dc.type.invitednoit
dc.type.miur273*
dc.publisher.nameAachen: M. Jeusfeld c/o Redaktion Sun SITE, Informatik V, RWTH Aachen.it
dc.publisher.placeAachenit
dc.publisher.countryDEUit
item.fulltextWith Fulltext-
item.openairetypeconferenceObject-
item.cerifentitytypePublications-
item.grantfulltextrestricted-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.journal.journalissn1613-0073-
crisitem.journal.journalissn1613-0073-
crisitem.journal.anceE211129-
crisitem.journal.anceE211129-
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