Paper 4
Ontology Matching with Knowledge RulesAuthors: Shangpu Jiang, Daniel Lowd, Sabin Ka e, and Dejing Dou |
AbstractOntology matching is the process of automatically determin- ing the semantic equivalences between the concepts of two ontologies. Most ontology matching algorithms are based on two types of strate- gies: terminology-based strategies, which align concepts based on their names or descriptions, and structure-based strategies, which exploit con- cept hierarchies to nd the alignment. In many domains, there is ad- ditional information about the relationships of concepts represented in various ways, such as Bayesian networks, decision trees, and association rules. We propose to use the similarities between these relationships to nd more accurate alignments. We accomplish this by defi ning soft con- straints that prefer alignments where corresponding concepts have the same local relationships encoded as knowledge rules. We use a probabilis- tic framework to integrate this new knowledge-based strategy with stan- dard terminology-based and structure-based strategies. Furthermore, our method is particularly e ective in identifying correspondences between complex concepts. Our method achieves better F-score than the state- of-the-art on three ontology matching domains. |