Semantic matching of ontologies

Introduction The discovery of semantic relationships such as subsumption and dis-jointness is still a challenge in ontology matching [6]. Existing methods use logical reasoning over computed equivalence relationships or machine learning based on lexical and structural features of ontology elements [7, 3]. While these methods deliver good results for some cases, they are limited to the information contained in the input ontologies to be matched. Therefore, background knowledge in form of an additional ontology may be useful to detect semantic relationships. In existing approaches, the identification of an appropriate ontology as background knowledge is often a task left for the user. We present two enhanced approaches for identifying semantic relationships. The first one is based on background knowledge; in contrast to other approaches, it is able to identify a background ontology automatically. The second approach builds on existing machine learning methods for identifying semantic relationships. First evaluation results for these methods and combined approaches show that the integration of these methods is reasonable as more semantic relationships are identified. Semantic Matching using Background Knowledge It has been shown in previous works that using an ontology as background knowledge can improve the match result [1]. The selection of the background ontology is obviously an important step in such an approach. While earlier works either relied on the user to provide such an ontology [1], or used very general upper ontologies (e.g. SUMO-OWL, [5]), our approach is able to select the background ontology automatically. The idea is illustrated in fig. 1 and 2. For the input ontologies S and T, we generate keyword queries for a web search engine (e.g., Google or Swoogle), and for our local ontology repository. The external search engine is only used if the local repository does not contain an appropriate ontology. When a background ontology O is found it can be used for matching. In addition to the direct alignment A dir , two alignments A O,S and A O,T , between the input ontologies and the background ontology, are computed. Then, for each pair of correspondences from A O,S and A O,T , existence of a relationship (i.e., equivalence, subsumption) between the model elements from O is determined. If that is the case, a new correspondence between the concepts from S and T can be inferred. All the correspondences found in this way are called semantic matches (A sem). Eventually, the final result is created by …