A Neural Approach to Entity Linking on Wikidata

  • Alberto CetoliEmail author
  • Stefano Bragaglia
  • Andrew D. O’Harney
  • Marc Sloan
  • Mohammad Akbari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11438)


We tackle Named Entity Disambiguation (NED) by comparing entities in short sentences with Wikidata graphs. Creating a context vector from graphs through deep learning is a challenging problem that has never been applied to NED. Our main contribution is to present an experimental study of recent neural techniques, as well as a discussion about which graph features are most important for the disambiguation task. In addition, a new dataset (Wikidata-Disamb) is created to allow a clean and scalable evaluation of NED with Wikidata entries, and to be used as a reference in future research. In the end our results show that a Bi-directional Long Short-TermMemory (Bi-LSTM) encoding of the graph triplets performs best, improving upon the baseline models and scoring an F1 value of 91.6% on the Wikidata-Disamb test set (The dataset and the code (with configurations) for this paper can be found at


Named Entity Disambiguation Graphs Wikidata RNN GCN 



This work was partially supported by InnovateUK grant Ref. 103677.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alberto Cetoli
    • 1
    Email author
  • Stefano Bragaglia
    • 1
  • Andrew D. O’Harney
    • 1
  • Marc Sloan
    • 1
  • Mohammad Akbari
    • 2
  1. 1.ContextscoutLondonUK
  2. 2.UCLLondonUK

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