EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs

In order to answer natural language questions over knowledge graphs, most processing pipelines involve entity and relation linking. Traditionally, entity linking and relation linking have been performed either as dependent sequential tasks or independent parallel tasks. In this paper, we propose a framework, called EARL, which performs entity linking and relation linking as a joint single task. EARL is modelled on a optimised variation of Generalised Travelling Salesperson Problem. The system determines the best semantic connection between all keywords of the question by referring to the knowledge graph. This is achieved by exploiting the connection density between entity candidates and relation candidates. We have empirically evaluated the framework on a dataset with 3000 complex questions. Our system surpasses state-of-the-art scores for entity linking task by reporting an accuracy of 0.67 against 0.40 from the next best entity linker.