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A Framework for Semantic Similarity Measures to enhance Knowledge Graph Quality

Traverso Ribón, Ignacio

Abstract (englisch):
Precisely determining similarity values among real-world entities becomes a building block for data driven tasks, e.g., ranking, relation discovery or integration. Semantic Web and Linked Data initiatives have promoted the publication of large semi-structured datasets in form of knowledge graphs. Knowledge graphs encode semantics that describes resources in terms of several aspects or resource characteristics, e.g., neighbors, class hierarchies or attributes. Existing similarity measures take into account these aspects in isolation, which may prevent them from delivering accurate similarity values. In this thesis, the relevant resource characteristics to determine accurately similarity values are identified and considered in a cumulative way in a framework of four similarity measures. Additionally, the impact of considering these resource characteristics during the computation of similarity values is analyzed in three data-driven tasks for the enhancement of knowledge graph quality.

First, according to the identified resource characteristics, new similarity measures able to combine two or more of them are described. In total fo ... mehr


Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Hochschulschrift
Jahr 2017
Sprache Englisch
Identifikator DOI(KIT): 10.5445/IR/1000073179
URN: urn:nbn:de:swb:90-731792
KITopen ID: 1000073179
Verlag Karlsruhe
Umfang VI, 126 S.
Abschlussart Dissertation
Fakultät Fakultät für Wirtschaftswissenschaften (WIWI)
Institut Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Prüfungsdatum 10.08.2017
Referent/Betreuer Prof. Y. Sure-Vetter
Schlagworte Semantic Similarity Measures, Knowledge Graphs, Knowledge Graph Quality, Semantic Technologies
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