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

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

3 Citations (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.

Original language	English
Title of host publication	6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016
Editors	Patrice Bellot, Jacky Montmain, Sebastien Harispe, Francois Trousset, Michel Plantie, Rajendra Akerkar, Anne Laurent, Sylvie Ranwez
Number of pages	9
Publisher	Association for Computing Machinery, Inc
Publication date	13.06.2016
Article number	20
ISBN (Electronic)	9781450340564
DOIs	https://doi.org/10.1145/2912845.2912864
Publication status	Published - 13.06.2016
Externally published	Yes
Event	6th International Conference on Web Intelligence, Mining and Semantics - WIMS 2016 - Nîmes, France, Nîmes, France Duration: 13.06.2017 → 15.06.2017 Conference number: 6 http://wims2016.mines-ales.fr/ (Official Event Website) https://dl.acm.org/doi/proceedings/10.1145/2912845

Bibliographical note

Publisher Copyright:
© 2016 ACM.

Research areas and keywords

Scalability
Semantic relatedness
Semantic web
Informatics
Business informatics

Access to Document

10.1145/2912845.2912864

Cite this

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 (Eds.), 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016 Article 20 (ACM International Conference Proceeding Series; Vol. 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. editor / 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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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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year = "2016",

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doi = "10.1145/2912845.2912864",

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booktitle = "6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016",

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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 (eds), 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016., 20, ACM International Conference Proceeding Series, vol. 13-15-June-2016, Association for Computing Machinery, Inc, 6th International Conference on Web Intelligence, Mining and Semantics - WIMS 2016, Nîmes, France, 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. ed. / 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; Vol. 13-15-June-2016).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

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AU - Usbeck, Ricardo

AU - Singh, Kamal Deep

AU - Maret, Pierre

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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

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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, editors, 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

Abstract

Bibliographical note

Research areas and keywords

Access to Document

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Cite this