计算机科学 ›› 2014, Vol. 41 ›› Issue (8): 263-266.doi: 10.11896/j.issn.1002-137X.2014.08.055

• 人工智能 • 上一篇    下一篇

基于动态权值的关联数据语义相似度算法研究

贾丽梅,郑志蕴,李钝,王振飞   

  1. 郑州大学信息工程学院 郑州450001;郑州大学信息工程学院 郑州450001;郑州大学信息工程学院 郑州450001;郑州大学信息工程学院 郑州450001
  • 出版日期:2018-11-14 发布日期:2018-11-14

Research on Semantic Similarity Algorithm of Linked Data Based on Dynamic Weight

JIA Li-mei,ZHENG Zhi-yun,LI Dun and WANG Zhen-fei   

  • Online:2018-11-14 Published:2018-11-14

摘要: 语义相似度计算对关联数据的信息检索有重要作用,直接影响数据的语义挖掘效果。实例的属性信息是关联数据语义相似度计算的一个重要因素。针对传统的关联数据语义相似度算法未考虑属性的重要性和取值类型导致计算精度较低的问题,提出基于动态权值的关联数据语义相似度计算方法,即根据待匹配的数据集中属性不同取值的数量、属性值的分布以及属性的有效性3个因素动态计算属性的权值,然后依据属性取值类型选用匹配相似度算法,最后结合属性的动态权值对概念进行实例的相似度计算。实验表明,基于动态权值的相似度计算方法与传统方法相比,实例相似度的计算精度得到了一定的提高。

关键词: 关联数据,语义相似度,实例属性,动态权值

Abstract: Semantic similarity calculation has an important role in information retrieval of linked data,and the results of calculation directly affect the effect of data mining.The attribute information of instance is an essential factor for semantic similarity computation of linked data.To solve the problem of lower computation precision caused by lack of considering the importance of attribute and type of attribute value,this paper proposed a new semantic similarity calculation method based on dynamic weight.This method dynamically computes the attribute weight according to quantity of different attribute values,distribution of attribute values,and validity of attribute.Then,it chooses the matching similarity algorithm of attributes according to the types of attribute value.Finally,it combines the dynamic weight of attributes to calculate the semantic similarity of instances.The experiment confirms that the computation precision of the semantic similarity of instances obtained from the methods in this thesis is better than existing methods.

Key words: Linked data,Semantic similarity,Instance attributes,Dynamic weight

[1] Berners-Lee T.Linked data-the story so far [J].International Journal on Semantic Web and Information Systems,2009,5(3):1-22
[2] Servant F P.Linking enterprise data [C]∥Linked Data on the Web.2008
[3] Hartig O,Sequeda J,Taylor J,et al.How to consume LinkedData on the Web:tutorial description[C]∥Proceedings of the 19th international conference on World Wide Web.ACM,2010:1347-1348
[4] Tversky A.Features of similarity [J].Psychological review,1977,84(4):327-352
[5] 高学东,吴玲玉,武森,等.基于属性与对象关系信息的综合差异度计算[J].计算机工程,2011,37(22):35-38
[6] Sheth A,Aleman-Meza B,Arpinar I B,et al.Semantic association identification and knowledge discovery for national security applications[J].Journal of Database Management,2005,16(1):33-53
[7] 刘宏哲,须德.基于本体的语义相似度和相关度计算研究综述[J].计算机科学,2012,9(2):8-13
[8] Bhattacharya I,Getoor L.Iterative record linkage for cleaning and integration [C]∥Proceedings of the 9th ACM SIGMOD Workshop on Research Issues in data Mining and Knowledge Discovery.ACM,2004:11-18
[9] Zadeh P D H,Reformat M Z.Fuzzy semantic similarity in linked data using the OWA operator[C]∥Fuzzy Information Proces-sing Society (NAFIPS).2012 Annual Meeting of the North American.IEEE,2012:1-6
[10] Song D,Heflin J.Domain-independent entity reference in RDFgraphs[C]∥Proceedings of the 19th ACM International Conference on Information and Knowledge Management.ACM,2010:1821-1824
[11] 张晓辉,蒋海华,邸瑞华.基于属性权重的链接数据共指关系构建[J].计算机科学,2013,0(2):40-43
[12] Glaser H,Millard I C,Jaffri A.RKBExplorer.com:a knowledge driven infrastructure for linked data providers[M]∥The Semantic Web:Research and Applications.Springer Berlin Heidelberg,2008:797-801

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!