从HTML表中提取关联数据

Ahmed Ktob, Zhoujun Li, D. Bouchiha
{"title":"从HTML表中提取关联数据","authors":"Ahmed Ktob, Zhoujun Li, D. Bouchiha","doi":"10.1109/CIC.2017.00018","DOIUrl":null,"url":null,"abstract":"The web plays a crucial role in our daily life. Its openness allows users to access data around the clock. Recently, data has become more exploitable by machines due to the newly introduced mechanism of linked data, which improves the quality of published data on the web dramatically. Therefore, we have attempted to benefit from the investment, regarding data, which already exist on the web, particularly web applications, to generate linked data. To achieve this, we suggested a set of transformation rules to extract data from HTML tables then convert them into RDF (Resource Description Framework) triples. Our hypothesis is based on a direct conversion of relational data into RDF triples proposed by the W3C Consortium. The suggested extraction process of RDF triples is automatic; however, it remains manual when it comes to primary and foreign keys detection. Simultaneously, we have developed a tool, called HTML2RDF, which accomplishes the extraction process. Results obtained by HTML2RDF were promising. However, their quality remains dependent on the proper determination of primary and foreign keys.","PeriodicalId":156843,"journal":{"name":"2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Extracting Linked Data from HTML Tables\",\"authors\":\"Ahmed Ktob, Zhoujun Li, D. Bouchiha\",\"doi\":\"10.1109/CIC.2017.00018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The web plays a crucial role in our daily life. Its openness allows users to access data around the clock. Recently, data has become more exploitable by machines due to the newly introduced mechanism of linked data, which improves the quality of published data on the web dramatically. Therefore, we have attempted to benefit from the investment, regarding data, which already exist on the web, particularly web applications, to generate linked data. To achieve this, we suggested a set of transformation rules to extract data from HTML tables then convert them into RDF (Resource Description Framework) triples. Our hypothesis is based on a direct conversion of relational data into RDF triples proposed by the W3C Consortium. The suggested extraction process of RDF triples is automatic; however, it remains manual when it comes to primary and foreign keys detection. Simultaneously, we have developed a tool, called HTML2RDF, which accomplishes the extraction process. Results obtained by HTML2RDF were promising. However, their quality remains dependent on the proper determination of primary and foreign keys.\",\"PeriodicalId\":156843,\"journal\":{\"name\":\"2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIC.2017.00018\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIC.2017.00018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

网络在我们的日常生活中起着至关重要的作用。它的开放性允许用户全天候访问数据。最近,由于新引入的关联数据机制,数据变得更容易被机器利用,这极大地提高了网络上发布数据的质量。因此,我们试图从数据方面的投资中获益,这些数据已经存在于网络上,特别是网络应用程序,以生成关联数据。为了实现这一点,我们建议使用一组转换规则从HTML表中提取数据,然后将其转换为RDF(资源描述框架)三元组。我们的假设是基于将关系数据直接转换为W3C联盟提出的RDF三元组。建议的RDF三元组提取过程是自动的;但是,当涉及到主键和外键检测时,它仍然是手动的。同时,我们开发了一个名为HTML2RDF的工具来完成提取过程。通过HTML2RDF得到的结果是有希望的。但是,它们的质量仍然取决于主键和外键的正确确定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extracting Linked Data from HTML Tables
The web plays a crucial role in our daily life. Its openness allows users to access data around the clock. Recently, data has become more exploitable by machines due to the newly introduced mechanism of linked data, which improves the quality of published data on the web dramatically. Therefore, we have attempted to benefit from the investment, regarding data, which already exist on the web, particularly web applications, to generate linked data. To achieve this, we suggested a set of transformation rules to extract data from HTML tables then convert them into RDF (Resource Description Framework) triples. Our hypothesis is based on a direct conversion of relational data into RDF triples proposed by the W3C Consortium. The suggested extraction process of RDF triples is automatic; however, it remains manual when it comes to primary and foreign keys detection. Simultaneously, we have developed a tool, called HTML2RDF, which accomplishes the extraction process. Results obtained by HTML2RDF were promising. However, their quality remains dependent on the proper determination of primary and foreign keys.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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