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6 July 2015 Improving ontology matching with propagation strategy and user feedback
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Proceedings Volume 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015); 963127 (2015) https://doi.org/10.1117/12.2197167
Event: Seventh International Conference on Digital Image Processing (ICDIP15), 2015, Los Angeles, United States
Abstract
Markov logic networks which unify probabilistic graphical model and first-order logic provide an excellent framework for ontology matching. The existing approach requires a threshold to produce matching candidates and use a small set of constraints acting as filter to select the final alignments. We introduce novel match propagation strategy to model the influences between potential entity mappings across ontologies, which can help to identify the correct correspondences and produce missed correspondences. The estimation of appropriate threshold is a difficult task. We propose an interactive method for threshold selection through which we obtain an additional measurable improvement. Running experiments on a public dataset has demonstrated the effectiveness of proposed approach in terms of the quality of result alignment.
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Chunhua Li, Zhiming Cui, Pengpeng Zhao, Jian Wu, Jie Xin, and Tianxu He "Improving ontology matching with propagation strategy and user feedback", Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 963127 (6 July 2015); https://doi.org/10.1117/12.2197167
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