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The use of semantic information found in structured knowledge bases has become an integral part of the processing pipeline of modern intelligent in-
formation systems. However, such semantic information is frequently insuffi-cient to capture the rich semantics demanded by the applications, and thus cor-pus-based methods employing natural language processing techniques are often used conjointly to provide additional information. However, the semantic expres-siveness and interaction of these data sources with respect to query processing result quality is often not clear. Therefore, in this paper, we introduce the notion of relational purity which represents how well the explicitly modelled relation-ships between two entities in a structured knowledge base capture the implicit (and usually more diverse) semantics found in corpus-based word embeddings.
The purity score gives valuable insights into the completeness of a knowledge base, but also into the expected quality of complex semantic queries relying on reasoning over relationships, as for example analogy queries.
Original languageEnglish
Title of host publicationLWDA 2017 Lernen Wissen Daten Analysen 2017
Subtitle of host publicationLernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings
EditorsM. Leyer
Place of PublicationRostock, Germany
PublisherCEUR-WS
Pages113-124
Number of pages12
StatePublished - 1 Sep 2017
EventLernen, Wissen, Daten, Analysen 2017 - Rostock, Germany

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR
Volume1917
ISSN (Electronic)1613-0073

Conference

ConferenceLernen, Wissen, Daten, Analysen 2017
CountryGermany
CityRostock
Period11/09/1713/09/17

    Research areas

  • Semantics of Relationships, LOD, Structured Knowledge Reposito-ries, Word Embeddings

ID: 34056723