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Semantic Web technologies are becoming more interleaved with geospatial databases, which should lead to an easier integration and querying of spatial data. This is fostered by a growing amount of publicly available geospatial data like OpenStreetMap. However, the integration can lead to geographic inconsistencies when combining multiple knowledge bases. Having the integration in place, users might not just issue a points-of-interest search, but rather might be interested in regions with specific attributes assigned to them. Though, having large amounts of spatial data available, standard databases and reasoners do not provide the means for (quantitative) spatial queries, or struggle to answer them efficiently. We seek to combine spatial reasoning, (nonmonotonic) logic programming, and ontologies for integrating geospatial databases with Semantic Web technologies. The focus of our investigation will be on a modular design, on efficient processing of large amounts of spatial data, and on enabling default reasoning. We propose a two-tier design related to HEX-programs, which should lead to a plausible trade-off between modularity and efficiency. Furthermore, we consider suitable geo-ontologies to semantically annotate and link different sources. Finally, the findings should lead to a proof-of-concept implementation, which will be tested for efficiency and modularity in artificial and real-world use cases.

DC Proposal: Towards a Framework for Efficient Query Answering and Integration of Geospatial Data