Risk Minimization Based Ontology Mapping

The key point to reach interoperability over distributed ontologies is the mediation between them, called ontology mapping. Absolutely manually specified mapping is tedious and time consumption. Additional, how to ensure the consistency and deal with error prone in manual process, further how to maintain the mapping with the evolution of ontologies are all beyond manual work. Therefore, it is indeed necessary to automatically discover the mapping between ontologies so that mergence and translation of different ontology-based annotations become possible. Existing (semi-)automatic processing system are restricted to limited information, which depress the performance especially when the taxonomy structures have little overlapping or the instances have few commons. In this paper, based on Bayesian decision theory, we propose an approach called RiMOM to automatically discover mapping between ontologies. RiMOM treats the entire mapping problem as a decision problem instead of similarity problem in previous work. It explicitly and formally gives a complete decision model for ontology mapping. Based on shallow NLP, this paper also introduces a method to deal with instances heterogeneity, which is a longstanding problem for information processing. Experiments on real world data show that RiMOM is promising.