Standard
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
Harvard
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, Nimes, France,
13.06.16.
https://doi.org/10.1145/2912845.2912864APA
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/10.1145/2912845.2912864Vancouver
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
Bibtex
@inbook{8e9835583a094af68aa578bbbb57b8e2,
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",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016, WIMS 2016 ; Conference date: 13-06-2016 Through 15-06-2016",
year = "2016",
month = jun,
day = "13",
doi = "10.1145/2912845.2912864",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery, Inc",
editor = "Patrice Bellot and Jacky Montmain and Sebastien Harispe and Francois Trousset and Michel Plantie and Rajendra Akerkar and Anne Laurent and Sylvie Ranwez",
booktitle = "6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016",
address = "United States",
url = "https://dl.acm.org/doi/proceedings/10.1145/2912845",
}
RIS
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 - http://www.scopus.com/inward/record.url?scp=84978532297&partnerID=8YFLogxK
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 2016 through 15 June 2016
ER -
