An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Constructing virtual documents for ontology matching
Proceedings of the 15th international conference on World Wide Web
Using Bayesian decision for ontology mapping
Web Semantics: Science, Services and Agents on the World Wide Web
Fast and effective kernels for relational learning from texts
Proceedings of the 24th international conference on Machine learning
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A string metric for ontology alignment
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
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The ontology alignment has two kinds of major problems. First, the features used for ontology alignment are usually defined by experts, but it is highly possible for some critical features to be excluded from the feature set. Second, the semantic and the structural similarities are usually computed independently, and then they are combined in an ad-hoc way where the weights are determined heuristically. This paper proposes the modified parse tree kernel (MPTK) for ontology alignment. In order to compute the similarity between entities in the ontologies, a tree is adopted as a representation of an ontology. After transforming an ontology into a set of trees,their similarity is computed using MPTK without explicit enumeration of features. In computing the similarity between trees,the approximate string matching is adopted to naturally reflect not only the structural information but also the semantic information. According to a series of experiments with a standard data set, the kernel method outperforms other structural similarities such as GMO. In addition, the proposed method shows the state-of-the-art performance in the ontology alignment.