Computing and querying strict, approximate, and metrically refined topological relations in linked geographic data.

Geographic entities and the information associated with them play a major role in Web-scale knowledge graphs such as Linked Data. Interestingly, almost all major datasets represent places and even entire regions as point coordinates. There are two key reasons for this. First, complex geometries are difficult to store and query using the current Linked Data technology stack to a degree where many queries take minutes to return or will simply time out. Second, the absence of complex geometries confirms a common suspicion among GIScientists, namely that for many everyday queries place-based relational knowledge is more relevant than raw geometries alone. To give an illustrative example, the statement that the White House is in Washington, DC is more important for gaining an understating of the city than the exact geometries of both entities. This does not imply that complex geometries are unimportant but that (topological) relations should also be extracted from them. As Egenhofer and Mark (1995b) put it in their landmark paper on naive geography, topology matters, metric refines. In this work we demonstrate how to compute and utilize strict, approximate, and metrically refined topological relations between several geographic feature types in DBpedia and compare our results to approaches that compute result sets for topological queries on the fly.