Author(s): Mohnish Dubey, Debayan Banerjee, Debanjan Chaudhuri, Jens Lehmann
Abstract: In order to answer natural language questions over knowledge graphs, most processing pipelines involve entity and relation linking. Traditionally, entity linking and relation linking has 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. We model the linking task as an instance of the Generalised Travelling Salesman problem (GTSP). EARL uses a graph connection based solution to the 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. The Connection-Density based solution performs at par with GTSP solution and approximate GTSP solution. We have empirically evaluated the framework on a dataset with 5000 questions. Our system surpasses state-of-the-art scores for entity linking task by reporting an accuracy of 0.65 to 0.40 from the next best entity linker.
Keywords: Entity Linking; Relation Linking; Generalised Travelling Salesman Problem; Question Answering