Research on Faceted Search Method for Water Data Catalogue Service

Jun Feng, Shengqiu Kong, B. Du, Jiamin Lu
{"title":"Research on Faceted Search Method for Water Data Catalogue Service","authors":"Jun Feng, Shengqiu Kong, B. Du, Jiamin Lu","doi":"10.1109/CSCloud.2017.38","DOIUrl":null,"url":null,"abstract":"Traditionally the data retrieval is achieved by searching the metadata with keywords, though it is often difficult for ordinary users to express professional and precise query demands in the water industry. Regarding this issue, this paper introduces an exploratory retrieval method called faceted search by gradually recommending relevant facets to the users. Firstly, a unified modeling algorithm is proposed to construct the unified metadata model in XML for heterogeneous water metadata. Based on this model, candidate facet terms can be extracted and filtered, in order to retrieve the various water metadata uniformly. At last, a facet recommendation algorithm is proposed to help the users to sharpen their queries by prompting less but more accurate facets as the search gets deeper, after excluding those \"irrelevant facets\" and \"redundant facets\". The experimental results demonstrate that our facet recommendation algorithm can significantly improve the retrieval precision on querying the water data.","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSCloud.2017.38","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Traditionally the data retrieval is achieved by searching the metadata with keywords, though it is often difficult for ordinary users to express professional and precise query demands in the water industry. Regarding this issue, this paper introduces an exploratory retrieval method called faceted search by gradually recommending relevant facets to the users. Firstly, a unified modeling algorithm is proposed to construct the unified metadata model in XML for heterogeneous water metadata. Based on this model, candidate facet terms can be extracted and filtered, in order to retrieve the various water metadata uniformly. At last, a facet recommendation algorithm is proposed to help the users to sharpen their queries by prompting less but more accurate facets as the search gets deeper, after excluding those "irrelevant facets" and "redundant facets". The experimental results demonstrate that our facet recommendation algorithm can significantly improve the retrieval precision on querying the water data.
面向水数据目录服务的分面搜索方法研究
传统的数据检索是通过对元数据进行关键字搜索来实现的,但在水务行业,普通用户往往难以表达专业、精确的查询需求。针对这一问题,本文介绍了一种探索性检索方法,即面搜索,通过逐步向用户推荐相关的面。首先,提出了一种统一建模算法,对异构水元数据在XML中构建统一元数据模型;基于该模型,可以提取和过滤候选facet项,从而统一检索各种水元数据。最后,提出了一种facet推荐算法,在排除“无关facet”和“冗余facet”后,随着搜索的深入,通过提示更少但更准确的facet来帮助用户锐化查询。实验结果表明,我们的面推荐算法可以显著提高水数据查询的检索精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信