Querying large-scale knowledge graphs using Qualitative Spatial Reasoning

In this paper we consider how Qualitative Spatial Reasoning (QSR) can be used to answer queries over large-scale knowledge graphs such as YAGO and DBPedia. We describe the challenges associated with spatially querying knowledge graphs such as point based representations, sparsity of qualitative relations, and scale. We address these challenges and present a query engine, Parallel Qualitative Reasoner-Query Engine (ParQR-QE), that uses a novel distributed qualitative spatial reasoning algorithm to provide answers to GeoSPARQL queries. An experimental evaluation using a range of different query types and the YAGO knowledge graph shows the advantages of QSR techniques in comparison to purely quantitative approaches.