Abstract
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 https://github.com/contextscout/ned-graphs).
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- 1.
The Wikidata graph is oriented so that the edges direct outwards with respect to the central node. In order to percolate information towards the central node we must consider the outgoing edges.
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This work was partially supported by InnovateUK grant Ref. 103677.
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Cetoli, A., Bragaglia, S., O’Harney, A.D., Sloan, M., Akbari, M. (2019). A Neural Approach to Entity Linking on Wikidata. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11438. Springer, Cham. https://doi.org/10.1007/978-3-030-15719-7_10
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