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 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 | |
| Publication status | Published - 13.06.2016 |
| Externally published | Yes |
| Event | 6th 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.
- Scalability, Semantic relatedness, Semantic web
- Informatics
- Business informatics