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Entity linking - connecting entity mentions in a natural language utteranceto knowledge graph (KG) entities is a crucial step for question answering overKGs. It is often based on measuring the string similarity between the entitylabel and its mention in the question. The relation referred to in the questioncan help to disambiguate between entities with the same label. This can bemisleading if an incorrect relation has been identified in the relation linkingstep. However, an incorrect relation may still be semantically similar to therelation in which the correct entity forms a triple within the KG; which couldbe captured by the similarity of their KG embeddings. Based on this idea, wepropose the first end-to-end neural network approach that employs KG as well asword embeddings to perform joint relation and entity classification of simplequestions while implicitly performing entity disambiguation with the help of anovel gating mechanism. An empirical evaluation shows that the proposedapproach achieves a performance comparable to state-of-the-art entity linkingwhile requiring less post-processing.

End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings