计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

一种基于马尔科夫网的本体匹配算法

崔 恺,凌兴宏,姚望舒,伏玉琛   

  1. (苏州大学计算机科学与技术学院,江苏 苏州 215006)
  • 收稿日期:2012-09-04 出版日期:2013-11-15 发布日期:2013-11-13
  • 作者简介:崔 恺(1987-),男,硕士研究生,主研方向:本体匹配,机器学习;凌兴宏、姚望舒、伏玉琛,副教授、博士
  • 基金项目:

    国家自然科学基金资助项目(61070122)

An Ontology Matching Algorithm Based on Markov Network

CUI Kai, LING Xing-hong, YAO Wang-shu, FU Yu-chen   

  1. (College of Computer Science and Technology, Soochow University, Suzhou 215006, China)
  • Received:2012-09-04 Online:2013-11-15 Published:2013-11-13

摘要:

概率模型是解决不确定性推理和数据分析的有效工具。针对本体匹配的不确定性,提出一种基于马尔科夫网的本体匹配改进算法。采用多种传统匹配算法计算相似度矩阵,改进相似度传播规则,添加2种结构稳定性约束规则和1种Disjoint一致性约束规则,定义其对应团的势函数。根据相似度矩阵和上述规则,给出马尔科夫网的构造方法,使用循环置信度传播算法计算随机变量的后验概率,依据后验概率得到最后的本体匹配结果。在OAEI 2010数据集上进行实验,结果表明,与iMatch本体匹配系统相比,该算法能有效降低概率模型的复杂度,提高本体匹配的准确率和召回率。

关键词: 本体, 本体匹配, 马尔科夫网, 相似度传播, 循环置信度传播, 一致性约束

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.

Key words: ontology, ontology matching, Markov network, similarity propagation, Loopy Belief Propagation(LBP), coherence constraint

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