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- W2387665634 abstract "Probabilistic model is a valid tool to solve the problem of uncertainty inference and data analysis. An improved algorithm based on Markov network is proposed, which focuses on the uncertainty of ontology matching. The similarity matrix is computed using several traditional algorithms, then the similarity propagation rule is improved, and two structure stability constraint rules and one Disjoint coherence constraint rule are added. The corresponding clique potentials are defined. According to the similarity matrix and these rules, a method to construct the Markov network is proposed. The results of ontology matching are obtained from the posterior probability, which is computed by doing approximate reasoning of the Loopy Belief Propagation(LBP) algorithm. Experimental results on OAEI 2010 show that the algorithm can reduce the complexity of probabilistic model effectively compared with iMatch ontology matching system, meanwhile such various clique rules and the corresponding potentials can increase the precision and the recall rate." @default.
- W2387665634 created "2016-06-24" @default.
- W2387665634 creator A5003787556 @default.
- W2387665634 date "2013-01-01" @default.
- W2387665634 modified "2023-09-28" @default.
- W2387665634 title "An Ontology Matching Algorithm Based on Markov Network" @default.
- W2387665634 hasPublicationYear "2013" @default.
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