Learning rules for conceptual structure on the Web

Journal of Intelligent Information Systems

Volumn 22, Issue 3, 2004, Pages 237-256

  Han, Hyoil ^a   Elmasri, Ramez ^a  

^a UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

Author keywords

Information extraction; Knowledge discovery; Ontology learning; Relational rule learning

Indexed keywords

INFORMATION EXTRACTION; KNOWLEDGE DISCOVERY; ONTOLOGY LEARNING; RELATIONAL RULE LEARNING;

DATA MINING; HTML; KNOWLEDGE ENGINEERING; LOGIC PROGRAMMING; WORLD WIDE WEB;

INFORMATION RETRIEVAL;

EID: 3543097395     PISSN: 09259902     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:JIIS.0000019278.84222.b7     Document Type: Article

References (23)

1. Agichtein, E. and Gravano, L. (2001). Snowball: Extracting Relations from Large Plain-Text Collections. In Proceedings of the 5th ACM International Conference on Digital Libraries.
2. Brill, E. (1995). Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part of Speech Tagging. Computational Linguistics.
3. Bray, T., Paoli, J., Sperberg-McQueen, C.M., and Maler, E. (2000). Extensible Markup Language (XML) 1.0, W3C Recommendation.
4. Brin, S. (1998). Extracting Patterns and Relations from the World Wide Web. ACM WebDB Workshop.
5. Califf, ME. (1998). Relational Learning Techniques for Natural Language Information Extraction. PhD Thesis, The University of Texas at Austin, TX.
6. Craven, M., Dipasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam, K., and Slattery, S. (1999). Learning to Construct Knowledge Bases from the World Wide Web. Artificial Intelligence.
7. Craven, M. and Slattery, S. (2001). Relational Learning with Statistical Predicate Invention: Better Models for Hypertext. Machine Learning, 43, 97-119.
8. Elmasri, R., and Navathe, S.B. (2000). Fundamentals of Database Systems. Addison-Wesley.
9. Embley, D.W., Kai, Y., and Xu, L. (2001). Recognizing Target-Ontology-Applicable Multiple-Record Web Documents. In Proceedings of the 20th International Conference on Conceptual Modeling.
10. Embley, D.W., Campbell, D.M., Jiang, Y.S., Ng, Y., Smith, R.D., Liddle, S.W., and Quass, D.W. (1998). A Conceptual-Modeling Approach to Extracting Data from the Web. In Proceedings of the 2nd International Conference on Conceptual Modeling.
11. Florescu, D., Levy, A., and Mendelzon, A. (1998). Database Techniques for the World Wide Web: A Survey. ACM SIGMOD RECORD, 27(3).
12. Freitag, D. (1998). Machine Learning for Information Extraction in Informal Domains. PhD Thesis, Carnegie Mellon University, PA.
13. Han, H. (2002). Conceptual Modeling and Ontology Extraction for Web Information. PhD Thesis, The University of Texas at Arlington, TX.
14. Kosala, R. and Blockeel, H. (2000). Web Mining Research: A Survey. SIGKDD Explorations, 2, 1-15.
15. Liu, H. and Motoda, H. (1998). Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers.
16. Maedche, A. and Staab, S. (2000). Discovering Conceptual Relations from Text. Proceedings of the 14th European Conference on Artificial Intelligence, Amsterdam.
17. Manning, C. and Schutze, H. (1999). Foundations of Statistical Natural Language Processing. The MIT Press.
18. Mitchell, T. (1997). Machine Learning. WCB/McGraw-Hill.
19. Montgomery, D.C. and Runger, G.C. (1994), Applied Statistics and Probability for Engineers. John Wiley Sons, Inc.
20. Muggleton, S. (2001). CProgol4.4: A Tutorial Introduction. In Inductive Logic Programming and Knowledge Discovery in Databases. Springer-Verlag.
21. Nienhuys-Cheng, S. and Wold, R. (1997). Foundations of Inductive Logic Programming. Springer.
22. Parson, R. and Muggleton, S. (1998). An Experiment with Browers That Learn, Machine Intelligence, Vol. 15.
23. Raggett, D., Hors, A.L., and Jacobs, I. (1999). HTML 4.01 Specification, W3C Recommendation.