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. • Demonstration of techniques to develop enhanced large-scale knowledge graphs. • Integration of qualitative spatial reasoning into query answering. • Development of a distributed spatial query answering system for large-scale knowledge graphs. • Evaluation shows advantages of querying using qualitative spatial reasoning. [ABSTRACT FROM AUTHOR]