Abstract. Despite serious research efforts, automatic ontology matching still suffers from severe problems with respect to the quality of matching results. Existing matching systems trade-off precision and recall and have their specific strengths and weaknesses. This leads to problems when the right matcher for a given task has to be selected. In this paper, we present a method for improving matching results by not choosing a specific matcher but applying machine learning techniques on an ensemble of matchers. Hereby we learn rules for the correctness of a correspondence based on the output of different matchers and additional information about the nature of the elements to be matched, thus leveraging the weaknesses of an individual matcher. We show that our method always performs significantly better than the median of the matchers used and in most cases outperforms the best matcher with an optimal threshold for a given pair of ontologies. As a side product of our experiments, we discovered that the majority vote is a simple but powerful heuristic for combining matchers that almost reaches the quality of our learning results. Despite significant research efforts automatic ontology matching is one of the unfulfilled promises of semantic web technologies and might turn out to become the Achilles' heel for large scale applications of ontologies on the web. So far, a significant number of automatic matching systems have been developed that address the matching problem by applying different heuristics, most of which are based on the similarity of representations. Depending on the kind of heuristics used, these matchers show a varying quality on different matching problems. This problem is typically addressed by approaches for selecting the optimal matcher based on the nature of the matching task and the known characteristics of the different matching systems. Such an approach that has been based on extensive interviews and tests is described in [14] . Another typical approach for dealing with the problem of adapting to a given matching task is to apply machine learning techniques for learning the optimal configuration of a matcher for a given dataset [6] . This approach amounts to determining the right heuristics and the appropriate parameters to be used in order to achieve the best result. The approach proposed in this paper differs from the above mentioned ones by not signing up to a specific matcher but trying to exploit the results of available matchers
Combining several strategies or even matchers can significantly increase the quality of the resulting candidate alignment REF .
Improving Ontology Matching Using Meta-level Learning
ESWC
Computer Science