Matching geospatial ontologies

In recent years, multiple geospatial ontologies have been developed for a wide range of different spatial databases. In addition, the development of volunteered geographic information both challenges and provides opportunities to the traditional authenticated geospatial information. Though volunteered geographic information is typically not as reliable and structured as the authenticated geospatial information, it often reflects changes in the real world more quickly and contains richer information related to human activity [1]. It is therefore desirable to link the corresponding information from disparate geospatial information sources, allowing users to use them synergistically. Aligning disparate geospatial ontologies is an essential element to realizing this. We propose a new semi-automatic method to align geospatial ontologies, based on coherence and consistency checking in description logic, as well as domain experts' knowledge. We evaluate it on real world data and compare it to two state of the art ontology mapping systems, CODI [2] and LogMap [3]. By a geospatial ontology we mean an ontology which contains both definitions of geospatial concepts in its TBox and facts about geospatial individuals in its ABox. When designing our approach, we assume that the TBox is not very large, but contains concepts which are more ambiguous, compared to for example biomedical ontologies. We also assume that geospatial individuals have geometry and location information. In common with other approaches, we use additional disjointness axioms to improve the quality of mapping. Since they are not part of the original ontology and may be wrong, we treat generated disjointness axioms as assumptions retractable by users. We treat original ontol-ogy axioms as correct and not retractable. Given two geospatial ontologies, our method has two main steps: generating assumptions and calculating a consistent and coherent assumption set (CAS) which contains the mapping. Step 1 : Retractable assumptions include disjointness axioms and mapping axioms. For TBoxes, disjointness axioms are generated for sibling classes. Initial mapping axioms between TBoxes are generated by stating equivalence of atomic concepts with identical names. Initial mapping axioms between ABoxes are generated based on three criteria: location, lexical labelling, and cardinality of mapping (one-to-one or one-to-many). We ensure that the geospatial instances from different sources are first represented at the same scale and using the same coordinate reference system scaling and transforming the input data as necessary. Given two instances, if their geometries are not spatially disjoint, we first generate a candidate 'sameAs' axiom for them. (When dealing with polygon geometries, …