Combining sum-product network and noisy-or model for ontology matching.
Weizhuo Li · 2015
Ontology matching is the key challenge to achieve semantic interoperability in building the Semantic Web. We present an alternative probabilistic scheme, called GMap, which combines the sum-product network and the noisy-or model. More precisely, we employ the sum-product network to encode the similarities based on individuals and disjointness axioms across ontologies and calculate the contributions by the maximum a posterior inference. The noisy-or model is used to encode the probabilistic matching rules, which are independent of each other as well as the value calculated by the sum-product network. Experiments show that GMap is competitive with many OAEI top-ranked systems. Futhermore, GMap, benefited from these two graphical models, can keep inference tractable in the whole matching process.