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Named entity disambiguation in short texts over knowledge graphs.

Wissem Bouarroudj1, Zizette Boufaida1, Ladjel Bellatreche2

  • 1LIRE Laboratory, Abdelhamid Mehri Constantine 2 University, Constantine, Algeria.

Knowledge and Information Systems
|January 10, 2022
PubMed
Summary

This study introduces a novel Named Entity Disambiguation (NED) approach for knowledge graphs (KGs) specifically designed for short texts. The proposed method significantly improves accuracy in KG-driven systems like query answering systems (QAS).

Keywords:
Entity linkingLinked open dataNamed entity disambiguationQueriesSemantic and syntactic featuresShort texts

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Information Retrieval

Background:

  • Knowledge graphs (KGs) are increasingly used in systems like query answering systems (QAS).
  • Existing Named Entity Disambiguation (NED) methods often struggle with short texts due to limited context.
  • Accurate NED on short texts is crucial for the performance of KG-driven QAS.

Purpose of the Study:

  • To address the challenge of NED on short texts within KGs.
  • To develop an improved NED approach that enhances the accuracy of KG-driven QAS.

Main Methods:

  • Context expansion using WordNet for resource similarity.
  • Leveraging entity coherence in multi-entity queries.
  • Utilizing word-relation similarity to resource properties.
  • Incorporating syntactic features into the NED process.

Main Results:

  • The proposed NED approach demonstrated superior performance compared to state-of-the-art methods.
  • Achieved a 27% improvement in F-measure across five benchmark datasets.
  • A system named Welink was developed to implement the proposed NED solution.

Conclusions:

  • The developed NED approach effectively handles the complexities of short texts in KGs.
  • The Welink system offers a practical and accessible solution for NED on KGs.
  • The findings highlight the importance of context expansion, entity coherence, and syntactic features for accurate NED.