Exploiting Wikipedia as external knowledge for document clustering

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.

REFERENCES

• 1: Somnath Banerjee , Krishnan Ramanathan , Ajay Gupta, Clustering short texts using wikipedia, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277909]
• 2: Evgeniy Gabrilovich , Shaul Markovitch, Overcoming the brittleness bottleneck using wikipedia: enhancing text categorization with encyclopedic knowledge, proceedings of the 21st national conference on Artificial intelligence, p.1301-1306, July 16-20, 2006, Boston, Massachusetts
• 3: Gabrilovich, E. and Markovitch, S. 2007. Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In Proceedings of the 20th International Joint Conference on Artificial Intelligence. (Hyderabad, India, January 6-12, 2007). 1606--1611.
• 4: Hotho, A., Staab, S.and Stumme, G. 2003. Wordnet improves text document clustering. In Proceedings of Semantic Web Workshop, the 26th annual International ACM SIGIR Conference. (Toronto, Canada, Jul. 28-Aug.1, 2003).
• 5: Andreas Hotho , Alexander Maedche , Steffen Staab, Text Clustering Based on Good Aggregations, Proceedings of the 2001 IEEE International Conference on Data Mining, p.607-608, November 29-December 02, 2001
• 6: Jian Hu , Lujun Fang , Yang Cao , Hua-Jun Zeng , Hua Li , Qiang Yang , Zheng Chen, Enhancing text clustering by leveraging Wikipedia semantics, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, July 20-24, 2008, Singapore, Singapore [doi>10.1145/1390334.1390367]
• 7: Yinghao Li , Wing Pong Robert Luk , Kei Shiu Edward Ho , Fu Lai Korris Chung, Improving weak ad-hoc queries using wikipedia asexternal corpus, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277914]
• 8: Milne, D. 2007. Computing Semantic Relatedness using Wikipedia Link Structure. In Proceedings of the 5th New Zealand Computer Science Research Student Conference. (Hamilton, New Zealand, April 10-13, 2007).
• 9: Xuan-Hieu Phan , Le-Minh Nguyen , Susumu Horiguchi, Learning to classify short and sparse text & web with hidden topics from large-scale data collections, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367510]
• 10: Steinbach, M., Karypis, G. and Kumar, V. 2000. A Comparison of document clustering techniques. Technical Report. Department of Computer Science and Engineering, University of Minnesota.
• 11: Pu Wang , Carlotta Domeniconi, Building semantic kernels for text classification using wikipedia, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2008, Las Vegas, Nevada, USA [doi>10.1145/1401890.1401976]
• 12: Illhoi Yoo , Xiaohua Hu , Il-Yeol Song, Integration of semantic-based bipartite graph representation and mutual refinement strategy for biomedical literature clustering, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA [doi>10.1145/1150402.1150505]
• 13: Zhang, X., Jing, L., Hu, X., et al. A Comparative Study of Ontology Based Term Similarity Measures on Document Clustering. In Proceedings of 12th International conference on Database Systems for Advanced Applications. (Bangkok, Thailand, April 9-12, 2007).115--126.
• 14: Zhao, Y. and Karypis, G. 2001. Criterion functions for document clustering: experiments and analysis, Technical Report. Department of Computer Science, University of Minnesota.
• 15: Shi Zhong , Joydeep Ghosh, Generative model-based document clustering: a comparative study, Knowledge and Information Systems, v.8 n.3, p.374-384, September 2005 [doi>10.1007/s10115-004-0194-1]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Algorithms

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Similarity measures

General Terms:
Algorithms, Experimentation

Keywords:
Wikipedia, document representation, text clustering