语义网格中基于多策略学习的Ontology匹配方法

近些年来，语义Web和网格计算这两个方向在各自的研究社区分别发展着，这两方面的交叉即语义网格(semantic grid)则是最近一段时间兴起的研究领域．通过给网格附加语义层，能够促进网格自组织的形成．现有的Gnd社区都是使用集中式的、一致性的、可扩充的Ontology库．超越集中式的语义存储是语义网格发展面临的最大挑战之一．针对网格社区间的Ontology异构性这个问题，提出了一种多策略的Ontology匹配学习方法．它使用多种分类方法来学习Ontology之间的匹配：使用一般的基于统计的分类方法来发现数据实例内部的分类特征；或者使用基于一阶逻辑的学习算法FOIL来发现数据实例之间的语义联系．在单个方法预测的基础上，匹配系统使用称之为最突出的冠军的匹配委员会方法来集成分类结果．实验表明在现实的知识领域中，系统能达到很高的匹配精度．

A Multi-Strategy Learning Based Ontology Matching Method on the Semantic Grid

Over the past few years, semantic Web and grid computing have developed separately in distinct communities.The semantic grid, which is the cross of two fields, is the emerging research focus recently.Ontology can enhance the self-organization of grid by applying the semantic layer on the grid.Ontology is a good solution for interoperation inside a grid community.However, current grid community is using a centralized, persistent, scalable ontology repository.Ontology heterogeneity among grid communities is becoming an ever more important issue.In this paper, a multi-strategy learning approach is proposed to resolve the problem.An ontology mapping system is described, which applies multiple classification methods to learn the matching between ontologies.It uses the general statistic classification method to discover category features in data instances and use the first-order learning algorithm FOIL to exploit the semantic relations among data instances.On the basis of the prediction results of individual methods, the system combines their outcomes using the matching committee rule called the Best Outstanding Champion.The experiments show that the system achieves high accuracy in the real-world domain.