Joint entity and relation linking using EARL

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 demo paper, we present EARL, which performs entity linking and relation linking as a joint single task. 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. EARL uses Bloom filters for faster retrieval of connection density and uses an extended label vocabulary for higher recall to improve the overall accuracy.