计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

基于路径特征的复杂本体匹配

王汉博,孙启霖   

  1. (中国科学院数学与系统科学研究院,北京 100190)
  • 收稿日期:2016-02-04 出版日期:2017-02-15 发布日期:2017-02-15
  • 作者简介:王汉博(1991—),女,硕士研究生,主研方向为模式识别、机器学习;孙启霖,博士研究生。
  • 基金项目:
    国家自然科学基金(61232015)。

Complex Ontology Matching Based on Path Feature

WANG Hanbo,SUN Qilin   

  1. (Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China)
  • Received:2016-02-04 Online:2017-02-15 Published:2017-02-15

摘要: 复杂本体匹配方法主要分为基于匹配模式的方法和基于机器学习的方法,前者需要人工制定启发式规则而后者易陷入局部最优解。针对上述问题,提出一种融合匹配模式和机器学习的复杂匹配方法。引入路径特征刻画本体中实例具有的性质,路径特征是匹配模式的具体化。在此基础上,使用一阶归纳学习器组合路径特征得到复杂的匹配结果。实验结果表明,该方法可以自动学习到本体间的复杂匹配,且与基于一阶归纳学习器的复杂匹配方法相比,能够有效缓解局部最优问题。

关键词: 链接开放数据, 本体, 复杂本体匹配, 路径特征, 属性信息

Abstract: The complex ontology matching methods are mainly classified into correspondence pattern based methods and machine learning based ones.The former are heuristic ones that are designed with some knowledge about the ontologies to be aligned and the latter are easy to fall into local optimums.This paper proposes a novel complex ontology matching method which benefits from both correspondence pattern and machine learning.The key of the method is the introduction of path feature to characterize the information of instances and path feature is an instantiation of correspondence pattern.Path features are combined by First Order Inductive Learner(FOIL) to acquire complex mappings.Experimental results show that this method automatically learns complex correspondences between ontologies,and effectively relieves the problem of local optimum compared with FOIL-based complex matching method.

Key words: Linked Open Data(LOD), ontology, complex ontology matching, path feature, attribute information

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