Hybrid geo-spatial query processing on the semantic web

Semantic Web data sources such as DBpedia are a rich resource of structured representations of knowledge about geographical features and provide potential data for computing the results of Question Answering System queries that require geo-spatial computations. Retrieval from these resources of all content that is relevant to a particular spatial query of, for example, containment, proximity or crossing is not always straightforward as the geometry is usually confined to point representations and there is considerable inconsistency in the way in which geographical features are referenced to locations. In DBpedia, some geographical feature instances have point coordinates, others have qualitative properties that provide explicit or implicit spatial relationships between named places, and some have neither of these. This thesis demonstrates that structured geo-spatial query, a form of question answering, on DBpedia can be performed with a hybrid query method that exploits quantitative and qualitative spatial properties in combination with a high quality reference geo-dataset that can help to support a full range of geo-spatial query operators such as proximity, containment and crossing as well as vague directional queries such as Find airports north of London?. A quantitative model based on the spatial directional relations in DBpedia has been used to assist in query processing. Evaluation experiments confirm the benefits of combining qualitative and quantitative methods for containment queries and of employing high-quality spatial data, as opposed to DBpedia points, as reference objects for proximity queries, particularly for linear features. The high quality geo-data also enabled answering questions impossible to answer with Semantic Web resources alone, such as finding geographic features within some distance from a region boundary. The contributions were validated by a prototype geo-spatial query system that combined qualitative and quantitative processing and included ranking answers for directional queries based on models derived from DBpedia contributed data.