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Semantic entity enrichment by leveraging multilingual descriptions for link prediction

Gesese, Genet Asefa ORCID iD icon 1; Alam, Mehwish 1; Sack, Harald 1
1 Karlsruher Institut für Technologie (KIT)


Most Knowledge Graphs (KGs) contain textual descriptions of entities in various natural languages. These descriptions of entities provide valuable information that may not be explicitly represented in the structured part of the KG. Based on this fact, some link prediction methods which make use of the information presented in the textual descriptions of entities have been proposed to learn representations of (monolingual) KGs. However, these methods use entity descriptions in only one language and ignore the fact that descriptions given in different languages may provide complementary information and thereby also additional semantics. In this position paper, the problem of effectively leveraging multilingual entity descriptions for the purpose of link prediction in KGs will be discussed along with potential solutions to the problem.

Verlagsausgabe §
DOI: 10.5445/IR/1000125309
Veröffentlicht am 27.10.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1613-0073
KITopen-ID: 1000125309
Erschienen in Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG2020), co-located with the 17th Extended Semantic Web Conference 2020 (ESWC 2020), Heraklion, Greece, June 02, 2020 - moved online. Ed.: M. Alam
Veranstaltung International​ Workshop on Deep Learning for Knowledge Graphs (DL4KG 2020), Online, 02.06.2020
Verlag RWTH Aachen
Serie CEUR Workshop Proceedings ; 2635
Externe Relationen Abstract/Volltext
Nachgewiesen in Scopus
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