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A scalable approach for computing semantic relatedness using semantic web data

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

Authors

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 languageEnglish
Title of host publication6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016
EditorsPatrice Bellot, Jacky Montmain, Sebastien Harispe, Francois Trousset, Michel Plantie, Rajendra Akerkar, Anne Laurent, Sylvie Ranwez
Number of pages9
PublisherAssociation for Computing Machinery, Inc
Publication date13.06.2016
Article number20
ISBN (electronic)9781450340564
DOIs
Publication statusPublished - 13.06.2016
Externally publishedYes
Event6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016 - Nimes, France
Duration: 13.06.2016 → 15.06.2016
Conference number: 6
https://dl.acm.org/doi/proceedings/10.1145/2912845

Bibliographical note

Publisher Copyright:
© 2016 ACM.

Other publications by the same author(s)

Analyzing the Influence of Knowledge Graph Information on Relation Extraction

Möller, C. & Usbeck, R., 2025, The Semantic Web: 22nd European Semantic Web Conference, ESWC 2025 Portoroz, Slovenia, June 1–5, 2025 Proceedings, Part I. Curry, E., Acosta, M., Poveda-Villalón, M., van Erp, M., Ojo, A., Hose, K., Shimizu, C. & Lisena, P. (eds.). Cham: Springer Nature Switzerland AG, Vol. 1. p. 460-480 21 p. (Lecture Notes in Computer Science ; vol. 15718).

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

Bridge-Generate: Scholarly Hybrid Question Answering

Taffa, T. A. & Usbeck, R., 2025, WWW Companion’25: Companion Proceedings of the ACM Web Conference 2025, April 28-May 2, 2025 Sydney, NSW, Australia. Long, G., Blumestein, M., Chang, Y., Lewin-Eytan, L., Huang, H. & Yom-Tov, E. (eds.). New York: Association for Computing Machinery, Inc, p. 1321-1325 5 p.

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

Junior fellows and distinguished dissertation of the GI and AI for crisis

Usbeck, R., Kraft, A. & Westphal, P., 01.02.2025, In: IT - Information Technology. 67, 1, p. 1-2 2 p.

Research output: Journal contributions › Other (editorial matter etc.) › Research

Knowledge Graph Question Answering and Large Language Models

Banerjee, D., Hu, N., Tan, Y., Min, D., Wu, Y., Usbeck, R. & Qi, G., 17.03.2025, Handbook on Neurosymbolic AI and Knowledge Graphs. Hitzler, P., Dalal, A., Mahdavinejad, M. S. & Norouzi, S. S. (eds.). IOS Press BV, p. 466-531 66 p. ( Frontiers in Artificial Intelligence and Applications; vol. 400).

Research output: Contributions to collected editions/works › Chapter

Neuro-Symbolic Relation Extraction

Yan, X., Usmanova, A., Möller, C., Westphal, P. & Usbeck, R., 17.03.2025, Handbook on Neurosymbolic AI and Knowledge Graphs. Hitzler, P., Dalal, A., Mahdavinejad, M. S. & Norouzi, S. S. (eds.). IOS Press BV, p. 550-576 27 p. ( Frontiers in Artificial Intelligence and Applications; vol. 400).

Research output: Contributions to collected editions/works › Chapter

DOI

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