Hierarchical topic-based communities construction for authors in a literature database
|Hierarchical topic-based communities construction for authors in a literature database|
|Author(s)||Wu C.-L., Koh J.-L.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Bibliographic database, Community Mining, Social Network (Extra: Bibliographic database, CiteSeer, Collaborative community, Community mining, Concept hierarchies, Consistency requirements, External sources, Literature database, Research papers, Research topics, Social Networks, Wikipedia, Database systems, Industrial engineering, Information services, Intelligent systems, Vocabulary control, Research)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of conference papers|
Hierarchical topic-based communities construction for authors in a literature database is a 2010 conference paper written in English by Wu C.-L., Koh J.-L. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
In this paper, given a set of research papers with only title and author information, a mining strategy is proposed to discover and organize the communities of authors according to both the co-author relationships and research topics of their published papers. The proposed method applies the CONGA algorithm to discover collaborative communities from the network constructed from the co-author relationship. To further group the collaborative communities of authors according to research interests, the CiteSeerX is used as an external source to discover the hidden hierarchical relationships among the topics covered by the papers. In order to evaluate whether the constructed topic-based collaborative community is semantically meaningful, the first part of evaluation is to measure the consistency between the terms appearing in the published papers of a topic-based collaborative community and the terms in the documents related to the specific topic retrieved from other external source. The experimental results show that 81.61% of the topic-based collaborative communities satisfy the consistency requirement. On the other hand, the accuracy of the discovered sub-concept relationship is verified by checking the Wikipedia categories. It is shown that 75.96% of the sub-concept terms are properly assigned in the concept hierarchy.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers. Cited 2 time(s)