太阳成8722(中国)有限公司-GREEN NO.1

标题：Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest 作者：Li, Lin; Xing, Xiaoyu; Xia, Hui; Huang, Xiaoying

来源出版物：ENTROPY 卷：18 期：2 文献编号：UNSP 45 DOI: 0.3390/e18020045 出版年：FEB 2016

摘要：The crucial problem for integrating geospatial data is finding the corresponding objects (the counterpart) from different sources. Most current studies focus on object matching with individual attributes such as spatial, name, or other attributes, which avoids the difficulty of integrating those attributes, but at the cost of an ineffective matching. In this study, we propose an approach for matching instances by integrating heterogeneous attributes with the allocation of suitable attribute weights via information entropy. First, a normalized similarity formula is developed, which can simplify the calculation of spatial attribute similarity. Second, sound-based and word segmentation-based methods are adopted to eliminate the semantic ambiguity when there is a lack of a normative coding standard in geospatial data to express the name attribute. Third, category mapping is established to address the heterogeneity among different classifications. Finally, to address the non-linear characteristic of attribute similarity, the weights of the attributes are calculated by the entropy of the attributes. Experiments demonstrate that the Entropy-Weighted Approach (EWA) has good performance both in terms of precision and recall for instance matching from different data sets.

入藏号：WOS:000371827800020

文献类型：Article

语种：English

作者关键词：geospatial data, instance matching (POI matching), entropy, word segmentation, category mapping

扩展关键词：SEMANTIC SIMILARITY; MATHEMATICAL-THEORY; INFORMATION; CONFLATION; ONTOLOGIES; COMMUNICATION; INTEGRATION; VECTOR; WEB

通讯作者地址：Xing, XY, Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址：lilin@whu.edu.cn; xiaoyu_xing@whu.edu.cn; xiahui@whu.edu.cn; xy.huang@whu.edu.cn

地址：

[Li, Lin; Xing, Xiaoyu; Xia, Hui; Huang, Xiaoying] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Li, Lin] Wuhan Univ, Geospatial Informat Sci Collaborat Innovat Ctr, Luoyu Rd 129, Wuhan 430079, Peoples R China.

研究方向：Physics

ISSN：1099-4300

影响因子（2014）：1.502