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Semantic Query Processing : Estimating Relational Purity. / Kalo, Jan-Christoph; Lofi, Christoph; Maseli, René Pascal; Balke, Wolf-Tilo.

LWDA 2017 Lernen Wissen Daten Analysen 2017: Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings. ed. / M. Leyer. Rostock, Germany : CEUR-WS, 2017. p. 113-124 (CEUR Workshop Proceedings; Vol. 1917).

Research output: Scientific - peer-reviewConference contribution

Harvard

Kalo, J-C, Lofi, C, Maseli, RP & Balke, W-T 2017, Semantic Query Processing: Estimating Relational Purity. in M Leyer (ed.), LWDA 2017 Lernen Wissen Daten Analysen 2017: Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings. CEUR Workshop Proceedings, vol. 1917, CEUR-WS, Rostock, Germany, pp. 113-124, Lernen, Wissen, Daten, Analysen 2017, Rostock, Germany, 11/09/17.

APA

Kalo, J-C., Lofi, C., Maseli, R. P., & Balke, W-T. (2017). Semantic Query Processing: Estimating Relational Purity. In M. Leyer (Ed.), LWDA 2017 Lernen Wissen Daten Analysen 2017: Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings (pp. 113-124). (CEUR Workshop Proceedings; Vol. 1917). Rostock, Germany: CEUR-WS.

Vancouver

Kalo J-C, Lofi C, Maseli RP, Balke W-T. Semantic Query Processing: Estimating Relational Purity. In Leyer M, editor, LWDA 2017 Lernen Wissen Daten Analysen 2017: Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings. Rostock, Germany: CEUR-WS. 2017. p. 113-124. (CEUR Workshop Proceedings).

Author

Kalo, Jan-Christoph ; Lofi, Christoph ; Maseli, René Pascal ; Balke, Wolf-Tilo. / Semantic Query Processing : Estimating Relational Purity. LWDA 2017 Lernen Wissen Daten Analysen 2017: Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings. editor / M. Leyer. Rostock, Germany : CEUR-WS, 2017. pp. 113-124 (CEUR Workshop Proceedings).

BibTeX

@inbook{ea4bb070f4654103b068a31c59ac1623,
title = "Semantic Query Processing: Estimating Relational Purity",
abstract = "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.",
keywords = "Semantics of Relationships, LOD, Structured Knowledge Reposito-ries, Word Embeddings",
author = "Jan-Christoph Kalo and Christoph Lofi and Maseli, {René Pascal} and Wolf-Tilo Balke",
year = "2017",
month = "9",
series = "CEUR Workshop Proceedings",
publisher = "CEUR-WS",
pages = "113--124",
editor = "M. Leyer",
booktitle = "LWDA 2017 Lernen Wissen Daten Analysen 2017",

}

RIS

TY - CHAP

T1 - Semantic Query Processing

T2 - Estimating Relational Purity

AU - Kalo,Jan-Christoph

AU - Lofi,Christoph

AU - Maseli,René Pascal

AU - Balke,Wolf-Tilo

PY - 2017/9/1

Y1 - 2017/9/1

N2 - 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.

AB - 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.

KW - Semantics of Relationships

KW - LOD

KW - Structured Knowledge Reposito-ries

KW - Word Embeddings

UR - http://ceur-ws.org/Vol-1917/

UR - http://resolver.tudelft.nl/uuid:ea4bb070-f465-4103-b068-a31c59ac1623

M3 - Conference contribution

T3 - CEUR Workshop Proceedings

SP - 113

EP - 124

BT - LWDA 2017 Lernen Wissen Daten Analysen 2017

PB - CEUR-WS

ER -

ID: 34056723