Local Ontology Algorithm Using Local Spectral Method
Ontology is an important topic in computer science. It has many applications in various fields. Ontology similarity computation plays a critical role in practical implementations. In some applications, however, ontology graphs that arise have certain local regions of interest, and the traditional spectral method will typically fail to provide information fine-tuned to every local region. In this paper, we use the locally-biased spectral method for ontology similarity computation and ontology mapping. The new ontology algorithm is given by Localspectral Optimization Program. At last, two experiments results show that the proposed algorithm has high accuracy and efficiency for similarity calculation and ontology mapping.
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