A scalable approach for computing semantic relatedness using semantic web data

Dennis Diefenbach; Ricardo Usbeck; Kamal Deep Singh; Pierre Maret

doi:10.1145/2912845.2912864

A scalable approach for computing semantic relatedness using semantic web data

Dennis Diefenbach
, Ricardo Usbeck
, Kamal Deep Singh
, Pierre Maret

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

3 Zitate (Scopus)

Abstract

Computing semantic relatedness is an essential operation for many natural language processing (NLP) tasks, such as Entity Linking (EL) and Question Answering (QA). It is still challenging to find a scalable approach to compute the semantic relatedness using Semantic Web data. Hence, we present for the first time an approach to pre-compute the semantic relatedness between the instances, relations, and classes of an ontology, such that they can be used in real-time applications.

Originalsprache	Englisch
Titel	6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016
Redakteure/-innen	Patrice Bellot, Jacky Montmain, Sebastien Harispe, Francois Trousset, Michel Plantie, Rajendra Akerkar, Anne Laurent, Sylvie Ranwez
Seitenumfang	9
Herausgeber (Verlag)	Association for Computing Machinery, Inc
Erscheinungsdatum	13.06.2016
Aufsatznummer	20
ISBN (elektronisch)	9781450340564
DOIs	https://doi.org/10.1145/2912845.2912864
Publikationsstatus	Erschienen - 13.06.2016
Extern publiziert	Ja
Veranstaltung	6th International Conference on Web Intelligence, Mining and Semantics - WIMS 2016 - Nîmes, France, Nîmes, Frankreich Dauer: 13.06.2017 → 15.06.2017 Konferenznummer: 6 http://wims2016.mines-ales.fr/ (Offizielle Event-Webseite) https://dl.acm.org/doi/proceedings/10.1145/2912845

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© 2016 ACM.

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Diefenbach, D., Usbeck, R., Singh, K. D., & Maret, P. (2016). A scalable approach for computing semantic relatedness using semantic web data. in P. Bellot, J. Montmain, S. Harispe, F. Trousset, M. Plantie, R. Akerkar, A. Laurent, & S. Ranwez (Hrsg.), 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016 Artikel 20 (ACM International Conference Proceeding Series; Band 13-15-June-2016). Association for Computing Machinery, Inc. https://doi.org/10.1145/2912845.2912864

Diefenbach, Dennis ; Usbeck, Ricardo ; Singh, Kamal Deep et al. / A scalable approach for computing semantic relatedness using semantic web data. 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016. Hrsg. / Patrice Bellot ; Jacky Montmain ; Sebastien Harispe ; Francois Trousset ; Michel Plantie ; Rajendra Akerkar ; Anne Laurent ; Sylvie Ranwez. Association for Computing Machinery, Inc, 2016. (ACM International Conference Proceeding Series).

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title = "A scalable approach for computing semantic relatedness using semantic web data",

abstract = "Computing semantic relatedness is an essential operation for many natural language processing (NLP) tasks, such as Entity Linking (EL) and Question Answering (QA). It is still challenging to find a scalable approach to compute the semantic relatedness using Semantic Web data. Hence, we present for the first time an approach to pre-compute the semantic relatedness between the instances, relations, and classes of an ontology, such that they can be used in real-time applications.",

keywords = "Scalability, Semantic relatedness, Semantic web, Informatics, Business informatics",

author = "Dennis Diefenbach and Ricardo Usbeck and Singh, \{Kamal Deep\} and Pierre Maret",

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Diefenbach, D, Usbeck, R, Singh, KD & Maret, P 2016, A scalable approach for computing semantic relatedness using semantic web data. in P Bellot, J Montmain, S Harispe, F Trousset, M Plantie, R Akerkar, A Laurent & S Ranwez (Hrsg.), 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016., 20, ACM International Conference Proceeding Series, Bd. 13-15-June-2016, Association for Computing Machinery, Inc, 6th International Conference on Web Intelligence, Mining and Semantics - WIMS 2016, Nîmes, Frankreich, 13.06.17. https://doi.org/10.1145/2912845.2912864

A scalable approach for computing semantic relatedness using semantic web data. / Diefenbach, Dennis; Usbeck, Ricardo; Singh, Kamal Deep et al.
6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016. Hrsg. / Patrice Bellot; Jacky Montmain; Sebastien Harispe; Francois Trousset; Michel Plantie; Rajendra Akerkar; Anne Laurent; Sylvie Ranwez. Association for Computing Machinery, Inc, 2016. 20 (ACM International Conference Proceeding Series; Band 13-15-June-2016).

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

TY - CHAP

T1 - A scalable approach for computing semantic relatedness using semantic web data

AU - Diefenbach, Dennis

AU - Usbeck, Ricardo

AU - Singh, Kamal Deep

AU - Maret, Pierre

N1 - Conference code: 6

PY - 2016/6/13

Y1 - 2016/6/13

N2 - Computing semantic relatedness is an essential operation for many natural language processing (NLP) tasks, such as Entity Linking (EL) and Question Answering (QA). It is still challenging to find a scalable approach to compute the semantic relatedness using Semantic Web data. Hence, we present for the first time an approach to pre-compute the semantic relatedness between the instances, relations, and classes of an ontology, such that they can be used in real-time applications.

AB - Computing semantic relatedness is an essential operation for many natural language processing (NLP) tasks, such as Entity Linking (EL) and Question Answering (QA). It is still challenging to find a scalable approach to compute the semantic relatedness using Semantic Web data. Hence, we present for the first time an approach to pre-compute the semantic relatedness between the instances, relations, and classes of an ontology, such that they can be used in real-time applications.

KW - Scalability

KW - Semantic relatedness

KW - Semantic web

KW - Informatics

KW - Business informatics

UR - https://www.scopus.com/pages/publications/84978532297

U2 - 10.1145/2912845.2912864

DO - 10.1145/2912845.2912864

M3 - Article in conference proceedings

AN - SCOPUS:84978532297

T3 - ACM International Conference Proceeding Series

BT - 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016

A2 - Bellot, Patrice

A2 - Montmain, Jacky

A2 - Harispe, Sebastien

A2 - Trousset, Francois

A2 - Plantie, Michel

A2 - Akerkar, Rajendra

A2 - Laurent, Anne

A2 - Ranwez, Sylvie

PB - Association for Computing Machinery, Inc

T2 - 6th International Conference on Web Intelligence, Mining and Semantics - WIMS 2016

Y2 - 13 June 2017 through 15 June 2017

ER -

Diefenbach D, Usbeck R, Singh KD, Maret P. A scalable approach for computing semantic relatedness using semantic web data. in Bellot P, Montmain J, Harispe S, Trousset F, Plantie M, Akerkar R, Laurent A, Ranwez S, Hrsg., 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016. Association for Computing Machinery, Inc. 2016. 20. (ACM International Conference Proceeding Series). doi: 10.1145/2912845.2912864

A scalable approach for computing semantic relatedness using semantic web data

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