Evaluating semantic relatedness through categorical and contextual information for entity disambiguation

Yiming Zhang, Mizuho Iwaihara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The number of entities in large-scale knowledge bases has been growing in recent years. The key issue to entity linking using a knowledge base such as Wikipedia is entity disambiguation. The objective of our proposing system is to disambiguate entities in documents and link entity mentions to their corresponding Wikipedia articles. To this end, our system ranks the set of candidate entities based on relatedness by utilizing semantic features derived from Wikipedia category hierarchies and articles. In addition, to reflect contextual information of Wikipedia, we utilize word embedding for refining the ranking result of candidate entities. Our experimental results show that these features have given good correlation with human rankings in candidate relatedness ranking and the combination of features has high disambiguation accuracy on news articles.

Original languageEnglish
Title of host publication2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings
EditorsKuniaki Uehara, Masahide Nakamura
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509008063
DOIs
Publication statusPublished - 2016 Aug 23
Event15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016 - Okayama, Japan
Duration: 2016 Jun 262016 Jun 29

Publication series

Name2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings

Other

Other15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016
CountryJapan
CityOkayama
Period16/6/2616/6/29

Keywords

  • Entity Disambiguation
  • Entity Similarity
  • Semantic Relatedness

ASJC Scopus subject areas

  • Computer Science(all)
  • Energy Engineering and Power Technology
  • Control and Optimization

Fingerprint Dive into the research topics of 'Evaluating semantic relatedness through categorical and contextual information for entity disambiguation'. Together they form a unique fingerprint.

  • Cite this

    Zhang, Y., & Iwaihara, M. (2016). Evaluating semantic relatedness through categorical and contextual information for entity disambiguation. In K. Uehara, & M. Nakamura (Eds.), 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings [7550852] (2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIS.2016.7550852