LRD : Latent Relation Discovery for vector space expansion and information retrieval

Alexandre Gonçalves, Jianhan Zhu, Dawei Song, Victoria Uren, Roberto Pacheco

Research output: Chapter in Book/Published conference outputConference publication


In this paper, we propose a text mining method called LRD (latent relation discovery), which extends the traditional vector space model of document representation in order to improve information retrieval (IR) on documents and document clustering. Our LRD method extracts terms and entities, such as person, organization, or project names, and discovers relationships between them by taking into account their co-occurrence in textual corpora. Given a target entity, LRD discovers other entities closely related to the target effectively and efficiently. With respect to such relatedness, a measure of relation strength between entities is defined. LRD uses relation strength to enhance the vector space model, and uses the enhanced vector space model for query based IR on documents and clustering documents in order to discover complex relationships among terms and entities. Our experiments on a standard dataset for query based IR shows that our LRD method performed significantly better than traditional vector space model and other five standard statistical methods for vector expansion.
Original languageEnglish
Title of host publicationAdvances in web-age information management
Subtitle of host publication7th International Conference, WAIM 2006, Hong Kong, China, June 17-19, 2006. Proceedings
EditorsJeffrey Xu Yu, Masaru Kitsuregawa, Hong Va Leong
Place of PublicationBerlin (DE)
Number of pages12
ISBN (Electronic)978-3-540-35226-6
ISBN (Print)978-3-540-35225-9
Publication statusPublished - 2006
Event7th International Conference on Web-Age Information Management - Hong Kong, China
Duration: 17 Jun 200619 Jun 2006

Publication series

NameLecture notes in computer science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Web-Age Information Management
Abbreviated titleWAIM 2006
CityHong Kong


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