Named Entity Disambiguation using Deep Learning on Graphs

Abstract

We tackle \ac{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 \ac{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 (\wikidatadisamb{}) is created to allow a clean and scalable evaluation of \ac{NED} with \wikidata{} entries, and to be used as a reference in future research. In the end our results show that a \ac{Bi-LSTM} encoding of the graph triplets performs best, improving upon the baseline models and scoring an \rm{F1} value of $91.6\%$ on the \wikidatadisamb{} test set

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Cited by in corpus (5)

• Falcon 2.0: An Entity and Relation Linking Tool over Wikidata
• Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models
• Commonsense Knowledge in Wikidata
• Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking
• User-friendly Comparison of Similarity Algorithms on Wikidata