Content-based ontology matching for GIS datasets

The alignment of separate ontologies by matching related concepts continues to attract great attention within the database and artificial intelligence communities, especially since semantic heterogeneity across data sources remains a widespread and relevant problem. In particular, the Geographic Information System (GIS) domain presents unique forms of semantic heterogeneity that require a variety of matching approaches.

Our approach considers content-based techniques for aligning GIS ontologies. We examine the associated instance data of the compared concepts and apply a content-matching strategy to measure similarity based on value types based on N-grams present in the data. We focus special attention on a method applying the concepts of mutual information and N-grams by developing 2 separate variations and testing them over GIS dataset including multi-jurisdictions. In order to align concepts, first we find the appropriate columns. For this, we will exploit mutual information between two columns based on the type distribution of their content. Intuitively, if two columns are semantically same, type distribution should be very similar. We justify the conceptual validity of our ontology alignment technique with a series of experimental results that demonstrate the efficacy and utility of our algorithms on a wide-variety of authentic GIS data.