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Conference proceeding
Exploiting Wikipedia as external knowledge for document clustering
Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 389-396
28 Jun 2009
Abstract
In traditional text clustering methods, documents are represented as "bags of words" without considering the semantic information of each document. For instance, if two documents use different collections of core words to represent the same topic, they may be falsely assigned to different clusters due to the lack of shared core words, although the core words they use are probably synonyms or semantically associated in other forms. The most common way to solve this problem is to enrich document representation with the background knowledge in an ontology. There are two major issues for this approach: (1) the coverage of the ontology is limited, even for WordNet or Mesh, (2) using ontology terms as replacement or additional features may cause information loss, or introduce noise. In this paper, we present a novel text clustering method to address these two issues by enriching document representation with Wikipedia concept and category information. We develop two approaches, exact match and relatedness-match, to map text documents to Wikipedia concepts, and further to Wikipedia categories. Then the text documents are clustered based on a similarity metric which combines document content information, concept information as well as category information. The experimental results using the proposed clustering framework on three datasets (20-newsgroup, TDT2, and LA Times) show that clustering performance improves significantly by enriching document representation with Wikipedia concepts and categories.
Metrics
Details
- Title
- Exploiting Wikipedia as external knowledge for document clustering
- Creators
- Xiaohua Hu - Drexel UniversityXiaodan Zhang - Drexel UniversityCaimei Lu - Drexel UniversityE Park - University of Missouri–Kansas CityXiaohua Zhou - Drexel UniversityACM
- Publication Details
- Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 389-396
- Series
- KDD '09
- Publisher
- Association for Computing Machinery (ACM)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Web of Science ID
- WOS:000270922000038
- Scopus ID
- 2-s2.0-70350637781
- Other Identifier
- 991019170359704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence