Learning from ontologies for common meaningful structures

Abstract

We put forward a hypothesis that there exist common meaningful structures among ontologies whose domains are analogous to each other The initial motivation of our hypothesis is to make full use of the structural information in existing ontologies, in order to benefit the domain of ontology. To verify the hypothesis we give a precise definition of the candidate of the common meaningful structure called MICISO (maximum isomorphic common induced sub-ontology). Based on the hypothesis and the definition we present a novel data mining problem called MICISO mining, whose aim is learning from ontologies to find out MICISOs and further recommend the common meaningful structures. We also provide an algorithm for MICISO mining, based on which we have developed a practical tool for mining and checking such structures. With the tool, the algorithm is implemented with quite a few pairs of existing ontologies, and the interesting meaningful results support our hypothesis. Thus we consider that the hypothesis is preliminarily verified. We suppose that our work sparks a novel promising thinking for the domain of ontology -to study existing ontologies for useful things.