摘要
Ontology mapping is a critical problem for integrating the heterogeneous information sources. It can identify the elements corresponding to each other. At present, there are many ontology mapping algorithms, but most of them are based on database schema. After analyzing the similarity and difference of ontology and schema, we propose a parsing graph-based algorithm for ontology mapping. The ontology parsing graph （OP-graph） extends the general concept of graph, encodes logic relationship, and semantic information which the ontology contains into vertices and edges of the graph. Thus, the problem of ontology mapping is translated into a problem of finding the optimal match between the two OP-graphs. With the definition of a universal measure for comparing the entities of two ontoiogies, we calculate the whole similarity between the two OP-graphs iteratively, until the optimal match is found. The results of experiments show that our algorithm is promising.
Ontology mapping is a critical problem for integrating the heterogeneous information sources. It can identify the elements corresponding to each other. At present,there are many ontology mapping algorithms,but most of them are based on database schema. After analyzing the similarity and difference of ontology and schema,we propose a parsing graph-based algorithm for ontology mapping. The ontology parsing graph (OP-graph) extends the general concept of graph,encodes logic relationship,and semantic information which the ontology contains into vertices and edges of the graph. Thus,the problem of ontology mapping is translated into a problem of finding the optimal match between the two OP-graphs. With the definition of a universal measure for comparing the entities of two ontologies,we calculate the whole similarity between the two OP-graphs iteratively,until the optimal match is found. The results of experiments show that our algorithm is promising.
基金
National Natural Science Fundation of China (No.60374071)
National Basic Research Program of China( No.2003CB316905)
