Overlapped ontology partitioning based on semantic similarity measures

Abstract

Today, public awareness about the benefits of using ontologies in information processing and the semantic web has increased. Since ontologies are useful in various applications, many large ontologies have been developed so far. But various areas like publication, maintenance, validation, processing, and security policies need further research. One way to better tackle these areas is to partition large ontologies into sub partitions. In this paper, we present a new method to partition large ontologies. For the proposed method, three steps are required: (1) transforming an ontology to a weighted graph, (2) partitioning the graph with an algorithm which recognizes the most important concepts, and (3) making sub-ontologies from results of the partitioning. Here, semantic distance measures are used to produce semantic graph, and using overlapped partitioning algorithms on the graph, a set of meaningful ontology partitions which can cause less communications in distributed reasoning is made. The proposed method shows better performance comparing with the previous partitioning method.