Method of Automatic Ontology Mapping through Machine Learning and Logic Mining

Ontology mapping is the bottleneck of handling conflicts among heterogeneous ontologies and of implementing reconfiguration or interoperability of legacy systems. We proposed an ontology mapping method by using machine learning, type constraints and logic mining techniques. This method is able to find concept correspondences through instances and the result is optimized by using an error function; it is able to find attribute correspondence between two equivalent concepts and the mapping accuracy is enhanced by combining together instances learning, type constraints and the logic relations that are imbedded in instances; moreover, it solves the most common kind of categorization conflicts. We then proposed a merging algorithm to generate the shared ontology and proposed a reconfigurable architecture for interoperation based on multi agents. The legacy systems are encapsulated as information agents to participate in the integration system. Finally we give a simplified case study.

Ontology mapping approach based on set & relation theory and OCL

An ontology mapping approach based on set & relation theory and OCL is introduced,then an ontology mapping meta-model is established which is composed of ontology related elements,mapping related elements and definition rule related elements.This ontology mapping meta-model can be regarded as a unified mechanism to realize different kinds of ontology mappings.The powerful computation capability of set and relation theory and the flexible expressive capability of OCL can be used in the computation of ontology mapping meta-model to realize the unified mapping among different ontology models.Based on the mapping meta-model,a general mapping management framework is developed to provide a common mapping storage mechanism,some mapping APIs and mapping rule APIs.

A Parsing Graph-based Algorithm for Ontology Mapping

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.