On ontology-driven document clustering using core semantic features

Knowledge and Information Systems

Volumn 28, Issue 2, 2011, Pages 395-421

  Fodeh, Samah ^a   Punch, Bill ^b   Tan, Pang Ning ^b  

^a YALE UNIVERSITY   (United States)

^b MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Clustering; Dimensionality reduction; Information gain; Ontology; Semantic features

Indexed keywords

CLUSTER ANALYSIS; DIMENSIONALITY REDUCTION; INFORMATION RETRIEVAL; SEMANTICS;

CLUSTER FORMATIONS; CLUSTERING; DOCUMENT CLUSTERING; INFORMATION GAIN; SEMANTIC FEATURES; SEMANTIC KNOWLEDGE; TEXT CORPORA;

ONTOLOGY;

EID: 79961208617     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-010-0370-4     Document Type: Article

References (47)

1. Andrzejewski D, Zhu X, Craven M (2009) Incorporating domain knowledge into topic modeling via dirichlet forest priors. ICML 25-32.
2. Al Sumait L, Domeniconi C (2007) Local semantic kernels for text document clustering. In: SIAM international conference on data mining workshop on text mining.
3. Banerjee S, Ramanathan K, Gupta A (2007) Clustering short texts using Wikipedia. SIGIR 787-788.
4. Basu S, Banerjee A, Mooney RJ (2002) Semi-supervised clustering by seeding. ICML 19-26.
5. Bodner RC, Song F(1996) Knowledge-based approaches to query expansion in information retrieval. Adv Artif Intell 146-158.
6. CLUTO Family of Clustering Software Tools: http://glaros. dtc. umn. edu/gkhome/cluto/cluto/overview.
7. Craven M, DiPasquo D, Freitag D, McCallum A, Mitchell TM et al (2000) Learning to construct knowledge bases from the World Wide Web. Artif Intell 118: 69-113.
8. Chemudugunta C, Smyth P, Steyvers M (2008) Combining concept hierarchies and statistical topic models. CIKM 1469-1470.
9. Dhillon I, Mallela S, Modha D (2003) Information-theoretic co-clustering. KDD 89-98.
10. Farahat AK, Kamel MS (2010) Enhancing document clustering using hybrid models for semantic similarity. In: SIAM international conference on data mining workshop on text mining.
11. Fodeh SJ, Punch W, Tan PN (2009) Combining statistics and semantics via ensemble model for document clustering. SAC 1446-1450.
12. Gabrilovich E, Markovitch S (2006) Overcoming the brittleness bottleneck using Wikipedia: enhancing text categorization with encyclopedic knowledge. NCAI 21: 1301-1306.
13. Gabrilovich E, Markovitch S (2007) Computing semantic relatedness using Wikipedia-based explicit semantic analysis. IJCAI 1606-1611.
14. Hotho A, Staab S, Stumme G (2003) WordNet improves text document clustering. In: SIGIR 2003 semantic web workshop. 541-544.
15. Hu J, Fang L, Cao Y (2008) Enhancing text clustering by leveraging Wikipedia semantics. SIGIR 179-186.
16. Hu X, Sun N, Zhang C, Chua TS (2009) Exploiting internal and external semantics for the clustering of short texts using world knowledge. CIKM 919-928.
17. Ifrim G, Theobald M, Weikum G (2005) Learning word-to-concept mappings for automated text classification. In: Workshop on learning in web search (LWS 2005). 18-25.
18. Jiang JJ, Conrath DW (1997) Semantic similarity based on corpus statistics and lexical taxonomy. International Conference Research on Computational Linguistics (ROCLING X).
19. Jing L, Zhou L, Ng MK, Huang JZ (2006) Ontology-based distance measure for text clustering. In: SIAM SDM workshop on text mining.
20. Kandylas V, Upham SP, Ungar LH (2009) Finding cohesive clusters for analyzing knowledge communities. Knowl Inf Syst 17: 335-354.
21. Lang K (1995) NewsWeeder: learning to filter netnews. ICML 331-339.
22. Larsen B, Aone C (1999) Fast and effective text mining using linear-time document clustering. KDD 16-22.
23. Lewis D (1997) Reuters-21578 text categorization test collection. AT & T Labs Research.
24. Lin D (1998) An information-theoretic definition of similarity. ICML 1: 296-304.
25. Mandala R, Tokunaga T, Tanaka H (1999) Complementing WordNet with Roget's and Corpus-based Thesauri for information retrieval. In: The 9th conference of the European chapter of the association for computational linguistics. 94-101.
26. MeSH, National Library of Medicine Controlled Vocabulary: http://www. nlm. nih. gov/mesh.
27. Moravec P, Kolovrat M, Snasel V (2004) LSI vs. WordNet ontology in dimension reduction and information retrieval. DATESO 288-294.
28. Natural Language Toolkit: http://www. nltk. org.
29. Recupero D (2007) A new unsupervised method for Document Clustering by using WordNet Lexical and Conceptual Relations. SIGIR 10: 563-579.
30. Rosso P, Ferretti E, Jimenez D et al (2004) Text categorization and information retrieval using WordNet senses. In: 2nd Global WordNet international conference. 299-304.
31. Sedding J, Kazakov D (2004) WordNet-based text document clustering. In: 3rd workshop on Robust methods in analysis of natural language processing data. 104-113.
32. Siolas G, d'Alche Buc F (2004) Support vector machines based on a semantic kernel for text categorization. IJCNN'00 5: 205-209.
33. Slonim N, Tishby N (2000) Document clustering using word clusters via the information bottleneck method. SIGIR 208-215.
34. Steinbach M, Karypis G, Kumar V (2000) A comparison of document clustering techniques. In: Proceedings of KDD workshop on text mining 34: 35-36.
35. Tan PN, Steinbach M, Kumar V (2005) Introduction to data mining. Addison-Wesley Longman Publishing Co, Boston.
36. Termier A, Rousset MC, Sebag M (2001) Combining statistics and semantics for word and document clustering. IJCAI 1: 49-54.
37. The 20 Newsgroups data set: http://people. csail. mit. edu/jrennie/20Newsgroups/.
38. Vorhees E (1993) Using WordNet to disambiguate word senses for text retrieval. SIGIR 171-180.
39. Wang P, Hu J, Zeng HJ et al (2007) Improving text classification by using encyclopedia knowledge. ICDM 332-341.
40. Wang P, Domeniconi C (2008) Building semantic kernels for text classification using Wikipedia. KDD 713-721.
41. Wang Y, Hodges J (2006) Document clustering with semantic analysis. HICSS 3: 54c-54c.
42. Wikipedia: http://wikipedia. edu.
43. WordNet: http://wordnet. princeton. edu.
44. Wu Z, Palmer M Verb (1994) Semantics and lexical selection. MACL 133-138.
45. Xiong H, Steinbach M, Ruslim A et al (2009) Characterizing pattern preserving clustering. Knowl Inf Syst 19: 133-138.
46. Yoo I, Hu X, Song I (2006) Integration of semantic-based bipartite graph representation and mutual refinement strategy for biomedical literature clustering. KDD 791-796.
47. Zhong S, Ghosh J (2005) Generative model-based document clustering: a comparative study. Knowl Inf Syst 8: 374-384.