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基于模糊集的地理信息模式匹配算法 被引量:5

Fuzzy set-based schema matching algorithm for geographic information
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摘要 结合现有模式匹配算法和GML模式的特点,给出了元素与元素语义贴近度的定义,在通用匹配规则的基础上引入具体的匹配规则.针对匹配规则在计算语义贴近度中的作用定义了权值调配函数,并给出了关于元素对的匹配函数的定义和计算公式.实验表明,该算法能够有效发现地理信息模式中元素之间的相似关系,提高了元素之间匹配结果的准确度,对地理信息模式的整合有较大的帮助. Combined with existing scheme matching algorithm and the characteristics of geography markup language (GML), elements and semantic similarity between elements were defined. On the basis of general matching regulations, specific matching rules were introduced. After reviewing some existing solutions to schema matching; the formula for calculating the semantic similarity was presented. The formula was based on matching criteria, so the effect of individual criteria in the formula needs to be thought over. Thus the weight adjusting function was adopted to control the effect of individual criteria in the formula. The experimental results of this algorithm testified that it can effectively discovery the semantic similarity of the schema elements, and it can improve the precision of the matching results between elements. It will be a strong auxiliary tool for information integration.
出处 《华中科技大学学报(自然科学版)》 EI CAS CSCD 北大核心 2006年第7期46-48,共3页 Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金 国家自然科学基金资助项目(60273076).
关键词 模糊集 模式匹配 语义贴近度 权值调配函数 fuzzy set schema matching semantic similarity weight adjusting function
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参考文献5

  • 1Madhavan J, Bernstein P A, Rahm E. Generic schema matching with cupid[C]//Proceedings of the 27th International Conference on Very Large Data Bases.San Francisco: Morgan Kaufman Publishers Inc,2001: 49-58.
  • 2何新贵.模糊知识处理的理论与技术[M].北京:国防工业出版社,1999..
  • 3关佶红,虞为,安扬.GML模式匹配算法[J].武汉大学学报(信息科学版),2004,29(2):169-174. 被引量:24
  • 4Erhard Rahm, Philip A. Bernstein. A survey of approaches to automatic schema matching [J]. The VLDB Journal, 2001, 10(4) : 334-350.
  • 5Ine GML. Open GIS Consortium. Ine Geography Markup Language(GML), 2004.

二级参考文献8

  • 1[1]Geography Markup Language (GML).http://opengis.net/gml/01-029/GML2.html,2000
  • 2[2]Madhavan J, Bernstein P A, Rahm E. Generic Schema Matching with Cupid. The 27th VLDB Conference, Rome, 2001
  • 3[3]Rahm E, Bernstein P A. A Survey of Approaches to Automatic Schema Matching. The VLDB Journal, 2001(10): 334~350
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  • 5[5]Pottinger R A, Bernstein P A. Creating a Mediated Schema Based on Initial Correspondences. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 2002
  • 6[6]Li W S, Clifton C. SemInt: A Tool for Identifying Attribute Correspondences in Heterogeneous Databases Using Neural Network. Data and Knowledge Engineering, 2000, 33(1): 49~84
  • 7[7]Reynaud C, Sirot J P, Vodislav D. Semantic Integration of XML Heterogeneous Data Sources. 2001 Int. Database Engineering Applications Symposium, 2001
  • 8[8]Guan J H, Zhou S G, Chen J P, et al. Ontology-based GML Schema Matching for Information Integration. ICMLC'03, Xi'an, 2003

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