Extracting Term Relationships from Wikipedia

被引:0
作者
Mathiak, Brigitte [1 ]
Pena, Victor Manuel Martinez [2 ]
Wira-Alam, Andias [1 ]
机构
[1] GESIS Leibniz Inst Social Sci, Cologne, Germany
[2] Univ Koblenz Landau, Inst Web Sci & Technol, Mainz, Germany
来源
WEB INFORMATION SYSTEMS AND TECHNOLOGIES, WEBIST 2012 | 2013年 / 140卷
关键词
Relationship extraction; Wikipedia; Ontology matching;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
When looking at the relationship between two terms, we are not only interested on how much they are related, but how we may explain this relationship to the user. This is an open problem in ontology matching, but also in other tasks, from information retrieval to lexicography. In this paper, we propose a solution based on snippets taken from Wikipedia. These snippets are found by looking for connectors between the two terms, e. g. the terms themselves, but also terms that occur often in both articles or terms that link to both articles. With a user study, we establish that this is particularly useful when dealing with not well known relationships, but well-known concepts. The users were learning more about the relationship and were able to grade it accordingly. On real life data, there are some issues with near synonyms, which are not detected well and terms from different communities, but aside from that we get usable and useful explanations of the term relationships.
引用
收藏
页码:267 / 280
页数:14
相关论文
共 22 条
  • [1] [Anonymous], TECHNICAL REPORT
  • [2] [Anonymous], 2004, SIGMOD, DOI DOI 10.1145/1007568.1007612
  • [3] [Anonymous], 2006, AAAI
  • [4] Bernstein P.A., 2007, SIGMOD 07, P1, DOI DOI 10.1145/1247480.1247482
  • [5] Chai XY, 2008, PROC VLDB ENDOW, V1, P773
  • [6] Dillman D A., 2001, The web questionnaire challenge to survey methodologists, P159
  • [7] Placing search in context: The concept revisited
    Finkelstein, L
    Gabrilovich, E
    Matias, Y
    Rivlin, E
    Solan, Z
    Wolfman, G
    Ruppin, E
    [J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS, 2002, 20 (01) : 116 - 131
  • [8] Gabrilovich E, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1606
  • [9] Islam A., 2008, ACM Transactions on Knowledge Discovery from Data (TKDD), V2, P10, DOI [10.1145/1376815.1376819, DOI 10.1145/1376815.1376819]
  • [10] Leacock C, 1998, COMPUT LINGUIST, V24, P147