Low-cost similarity calculation on ontology fusion in knowledge bases

Ontology fusion in knowledge bases has become less easy, due to the massive capacity involved in the process of semantic similarity calculation. Many similarity calculation methods have been developed, although they are hardly united. This article contributes a low-cost similarity calculation method for ontology fusion, based on the inspiration of binary metrics, with the aim of reducing the size of similarity calculations both spatially and logically. By introducing the definitions of a heterogeneous ontology, entities of ontologies and rules of ontology fusion on the basis of concept fusion and relationship fusion, we put forward the algorithm of main traverse procedure and calculated to be the least cost in time and space in comparison with traditional methods. We adopted three experiments to testify the usability of our approach from the perspective of actual library resources, small datasets and large datasets. In Experiment 1, the bibliographic data from East China Normal University Library were used to show the feasibility and capability of our proposal and present the process of the algorithm. In both Experiments 2 and 3, our approach had at least 88% confidence in detecting accurate merging mappings and also decreased time cost. The test demonstrated a good fusion result. The problem of lower recalls caused by error analysis results from the conflict between the complex structures in ontologies and the recursive functions, which will be improved in the future.