An accurate toponym-matching measure based on approximate string matching

Author:

Kılınç Deniz1

Affiliation:

1. Department of Software Engineering, Faculty of Technology, Celal Bayar University, Turkey

Abstract

Approximate string matching (ASM) is a challenging problem, which aims to match different string expressions representing the same object. In this paper, detailed experimental studies were conducted on the subject of toponym matching, which is a new domain where ASM can be performed, and the creation of a single string-matching measure that can perform toponym matching process regardless of the language was attempted. For this purpose, an ASM measure called DAS, which comprises name similarity, word similarity and sentence similarity phases, was created. Considering the experimental results, the retrieval performance and system accuracy of DAS were much better than those of other well-known five measures that were compared on toponym test datasets. In addition, DAS had the best metric values of mean average precision in six languages, and precision/recall graphs confirm this result.

Publisher

SAGE Publications

Subject

Library and Information Sciences,Information Systems

Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Market Size Estimation Model at the Product Level based on Text Mining Approach;2023 IEEE International Conference on Big Data (BigData);2023-12-15

2. CLGLIAM: contrastive learning model based on global and local semantic interaction for address matching;Applied Intelligence;2023-10-26

3. Comparative Study of Anemia Classification Algorithms for International and Newly CBC Datasets;International Journal of Online and Biomedical Engineering (iJOE);2023-05-16

4. Locality sensitive blocking (LSB): A robust blocking technique for data deduplication;Journal of Information Science;2022-09-16

5. Soft Integration of Geo-Tagged Data Sets in J-CO-QL+;ISPRS International Journal of Geo-Information;2022-09-13

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3