Understanding Graph Structure of Wikipedia for Query Expansion

Joan Guisado-Gámez, Arnau Prat-Pérez
{"title":"Understanding Graph Structure of Wikipedia for Query Expansion","authors":"Joan Guisado-Gámez, Arnau Prat-Pérez","doi":"10.1145/2764947.2764953","DOIUrl":null,"url":null,"abstract":"Knowledge bases are very good sources for knowledge extraction, the ability to create knowledge from structured and unstructured sources and use it to improve automatic processes as query expansion. However, extracting knowledge from unstructured sources is still an open challenge [9]. In this respect, understanding the structure of knowledge bases can provide significant benefits for the effectiveness of such purpose. In particular, Wikipedia has become a very popular knowledge base in the last years because it is a general encyclopedia that has a large amount of information and thus, covers a large amount of different topics. In this piece of work, we analyze how articles and categories of Wikipedia relate to each other and how these relationships can support a query expansion technique. In particular, we show that the structures in the form of dense cycles with a minimum amount of categories tend to identify the most relevant information.","PeriodicalId":144860,"journal":{"name":"Proceedings of the GRADES'15","volume":"177 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the GRADES'15","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2764947.2764953","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

Knowledge bases are very good sources for knowledge extraction, the ability to create knowledge from structured and unstructured sources and use it to improve automatic processes as query expansion. However, extracting knowledge from unstructured sources is still an open challenge [9]. In this respect, understanding the structure of knowledge bases can provide significant benefits for the effectiveness of such purpose. In particular, Wikipedia has become a very popular knowledge base in the last years because it is a general encyclopedia that has a large amount of information and thus, covers a large amount of different topics. In this piece of work, we analyze how articles and categories of Wikipedia relate to each other and how these relationships can support a query expansion technique. In particular, we show that the structures in the form of dense cycles with a minimum amount of categories tend to identify the most relevant information.
理解维基百科的图结构用于查询扩展
知识库是非常好的知识提取源,能够从结构化和非结构化源中创建知识,并使用它来改进查询扩展等自动化过程。然而,从非结构化资源中提取知识仍然是一个开放的挑战。在这方面,理解知识库的结构可以为实现这一目的的有效性提供显著的好处。特别是,维基百科在过去几年中已经成为一个非常受欢迎的知识库,因为它是一个具有大量信息的通用百科全书,因此涵盖了大量不同的主题。在这篇文章中,我们分析了维基百科的文章和分类是如何相互关联的,以及这些关系如何支持查询扩展技术。特别是,我们表明,具有最少类别的密集循环形式的结构倾向于识别最相关的信息。
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