A Method for Generating Comparison Tables From the Semantic Web

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING International Journal of Data Warehousing and Mining Pub Date : 2022-04-01 DOI:10.4018/ijdwm.298008
A. Giacometti, Béatrice Bouchou-Markhoff, Arnaud Soulet
{"title":"A Method for Generating Comparison Tables From the Semantic Web","authors":"A. Giacometti, Béatrice Bouchou-Markhoff, Arnaud Soulet","doi":"10.4018/ijdwm.298008","DOIUrl":null,"url":null,"abstract":"This paper presents Versus, which is the first automatic method for generating comparison tables from knowledge bases of the Semantic Web. For this purpose, it introduces the contextual reference level to evaluate whether a feature is relevant to compare a set of entities. This measure relies on contexts that are sets of entities similar to the compared entities. Its principle is to favor the features whose values for the compared entities are reference (or frequent) in these contexts. The proposal efficiently evaluates the contextual reference level from a public SPARQL endpoint limited by a fair-use policy. Using a new benchmark based on Wikidata, the experiments show the interest of the contextual reference level for identifying the features deemed relevant by users with high precision and recall. In addition, the proposed optimizations significantly reduce the number of required queries for properties as well as for inverse relations. Interestingly, this experimental study also show that the inverse relations bring out a large number of numerical comparison features.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Data Warehousing and Mining","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.4018/ijdwm.298008","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

Abstract

This paper presents Versus, which is the first automatic method for generating comparison tables from knowledge bases of the Semantic Web. For this purpose, it introduces the contextual reference level to evaluate whether a feature is relevant to compare a set of entities. This measure relies on contexts that are sets of entities similar to the compared entities. Its principle is to favor the features whose values for the compared entities are reference (or frequent) in these contexts. The proposal efficiently evaluates the contextual reference level from a public SPARQL endpoint limited by a fair-use policy. Using a new benchmark based on Wikidata, the experiments show the interest of the contextual reference level for identifying the features deemed relevant by users with high precision and recall. In addition, the proposed optimizations significantly reduce the number of required queries for properties as well as for inverse relations. Interestingly, this experimental study also show that the inverse relations bring out a large number of numerical comparison features.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种从语义网生成比较表的方法
本文提出了第一个基于语义网知识库自动生成比较表的方法Versus。为此,它引入了上下文引用级别,以评估一个特性是否与比较一组实体相关。此度量依赖于上下文,这些上下文是与被比较实体相似的实体集。其原则是优先考虑比较实体的值在这些上下文中是引用(或频繁)的特征。该建议有效地从受合理使用策略限制的公共SPARQL端点评估上下文引用级别。使用基于Wikidata的新基准,实验表明上下文参考水平对识别用户认为相关的特征具有较高的准确性和召回率。此外,建议的优化显著减少了属性和逆关系所需查询的数量。有趣的是,本实验研究还表明,反比关系带来了大量的数值比较特征。
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 去求助
来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
期刊最新文献
Fishing Vessel Type Recognition Based on Semantic Feature Vector Optimizing Cadet Squad Organizational Satisfaction by Integrating Leadership Factor Data Mining and Integer Programming Hybrid Inductive Graph Method for Matrix Completion A Fuzzy Portfolio Model With Cardinality Constraints Based on Differential Evolution Algorithms Dynamic Research on Youth Thought, Behavior, and Growth Law Based on Deep Learning Algorithm
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1