Pareto optimization for multiobjective matching of geospatial ontologies

Geospatial information is different than conventional information. Harmonization is needed for interoperability and seamless access to data. Ontology matching is an emerging solution to achieve this harmonization. The input data of the Geospatial ontologies vary from the conventional ontologies and hence it is conceptualized in a different manner. There are two major obstacles for geoinformation fusion: heterogeneity and uncertainty. Heterogeneity is more prevalent and uncertainty is an unavoidable entity in geospatial domain. This paper explores a novel multi-objective algorithm for geospatial ontology matching. It uses Pareto ranking to sort the probable solution and derives the pareto front. This pareto front is used further to find the best match.