Named Entity Disambiguation using Deep Learning on Graphs.

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