UT ML Group: Inductive Logic Programming

Inductive logic programming (ILP) studies the learning of (Prolog) logic programs and other relational knowledge from examples. Most machine learning algorithms are restricted to finite, propositional, feature-based representations of examples and concepts and cannot learn complex relational and recursive knowledge. ILP allows learning with much richer representations. Our work has focussed on applications of ILP to various problems in natural language and theory refinement for logic programs.

Publications

  1. Learning Semantic Parsers: An Important But Under-Studied Problem [Abstract] [PDF]
    Raymond J. Mooney
    Papers from the AAAI 2004 Spring Symposium on Language Learning: An Interdisciplinary Perspective, pp. 39-44, Stanford, CA, March 2004.

  2. Relational Data Mining with Inductive Logic Programming for Link Discovery [Abstract] [PDF]
    Mooney, R.J., Melville, P., Tang L. R., Shavlik J., Dutra I., Page D.
    Kargupta, H., Joshi, A., Sivakumar K., and Yesha, Y. (Eds.), Data Mining: Next Generation Challenges and Future Directions , pp. 239--254, AAAI Press, Menlo Park, CA, 2004.

  3. Integrating Top-down and Bottom-up Approaches in Inductive Logic Programming: Applications in Natural Language Processing and Relational Data Mining [Abstract] [PDF]
    Lappoon R. Tang
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, August 2003.
    219 pages

  4. Scaling Up ILP to Large Examples: Results on Link Discovery for Counter-Terrorism [Abstract] [PDF]
    Lappoon R. Tang, Raymond J. Mooney, and Prem Melville
    Proceedings of the KDD-2003 Workshop on Multi-Relational Data Mining (MRDM-2003), pp.107-121, Washington DC, August 2003.

  5. Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction [Abstract] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    Journal of Machine Learning Research, 4, (2003), pp. 177-210.

  6. Relational Data Mining with Inductive Logic Programming for Link Discovery [Abstract] [PDF]
    Raymond J. Mooney, Prem Melville, Lappoon R. Tang, Jude Shavlik, Inês de Castro Dutra, David Page, and Vítor Santos Costa
    Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, Baltimore, MD, November 2002.

  7. Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing [Abstract] [PDF]
    Lappoon R. Tang and Raymond J. Mooney
    Proceedings of the 12th European Conference on Machine Learning (ECML-2001), pp. 466-477, Freiburg, Germany, September 2001.

  8. Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing [Abstract] [PDF]
    Lappoon R. Tang and Raymond J. Mooney
    Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-2000), pp. 133 - 141, Hong Kong, October 2000

  9. Relational Learning of Pattern-Match Rules for Information Extraction [Abstract] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), Orlando, FL, pp. 328-334, July 1999.

  10. Relational Learning Techniques for Natural Language Information Extraction [Abstract] [PDF]
    Mary Elaine Califf
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, August 1998.
    142 pages.
    Also appears as Technical Report AI 98-276, Artificial Intelligence Lab, University of Texas at Austin.

  11. An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions [Abstract] [PDF]
    Lappoon R. Tang, Mary Elaine Califf, and Raymond J. Mooney
    Technical Report AI 98-271, Artificial Intelligence Lab, University of Texas at Austin, May 1998.

  12. Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming [Abstract] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    New Generation Computing, 16, 3, p. 263-281 (1998).

  13. Using Multi-Strategy Learning to Improve Planning Efficiency and Quality [Abstract] [PDF]
    Tara A. Estlin
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, May 1998.
    128 pages
    Also appears as Technical Report AI 98-269, Artificial Intelligence Lab, University of Texas at Austin.

  14. Relational Learning of Pattern-Match Rules for Information Extraction [Abstract] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    Proceedings of AAAI Spring Symposium on Applying Machine Learning to Discourse Processing, pp. 6-11, Standford, CA, March 1998.

  15. Relational Learning Techniques for Natural Language Information Extraction [Abstract] [PDF]
    Mary Elaine Califf
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, 1997.
    27 pages

  16. Learning to Improve both Efficiency and Quality of Planning [Abstract] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pp. 1227-1232, Nagoya, Japan, August 1997.

  17. Applying ILP-based Techniques to Natural Language Information Extraction: An Experiment in Relational Learning [Abstract] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    Workshop Notes of the IJCAI-97 Workshop on Frontiers of Inductive Logic Programming, pp. 7-11, Nagoya, Japan, August 1997.

  18. Learning to Parse Natural Language Database Queries into Logical Form [Abstract] [PDF]
    Cynthia A. Thompson Raymond J. Mooney, and Lappoon R. Tang
    Proceedings of the ML-97 Workshop on Automata Induction, Grammatical Inference, and Language Acquisition, Nashville, TN, July 1997.

  19. Relational Learning of Pattern-Match Rules for Information Extraction [Abstract] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    Proceedings of the ACL Workshop on Natural Language Learning, pp. 9-15, Madrid, Spain, July 1997.

  20. An Inductive Logic Programming Method for Corpus-based Parser Construction [Abstract] [PDF]
    John M. Zelle and Raymond J. Mooney
    Unpublished Technical Note, January 1997

  21. Inductive Logic Programming for Natural Language Processing [Abstract] [PDF]
    Raymond J. Mooney
    Inductive Logic Programming: Selected Papers from the 6th International Workshop, S. Muggleton (Ed.), pp.3-22, Springer Verlag, Berlin, 1997.
    Also appears in Proceedings of the 6th International Inductive Logic Programming Workshop, pp. 205-224, Stockholm, Sweden, August 1996.

  22. Integrating Explanation-Based and Inductive Learning Techniques to Acquire Search-Control for Planning [Abstract] [PDF]
    Tara A. Estlin
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, September 1996.
    Also appears as Technical Report AI 96-250, Artificial Intelligence Lab, University of Texas at Austin.

  23. Learning to Parse Database Queries using Inductive Logic Programming [Abstract] [PDF]
    John M. Zelle and Raymond J. Mooney
    Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 1050-1055, Portland, OR, August 1996.

  24. Multi-Strategy Learning of Search Control for Partial-Order Planning [Abstract] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 843-848, Portland, OR, August 1996.

  25. Integrating EBL and ILP to Acquire Control Rules for Planning [Abstract] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    Proceedings of the Third International Workshop on Multi-Strategy Learning (MSL-96), pp. 271-279, Harpers Ferry, WV, May 1996.

  26. Advantages of Decision Lists and Implicit Negative in Inductive Logic Programming [Abstract] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    Technical Report, Artificial Intelligence Lab, University of Texas at Austin, January 1996.

  27. Comparative Results on Using Inductive Logic Programming for Corpus-based Parser Construction [Abstract] [PDF]
    John M. Zelle and Raymond J. Mooney
    Symbolic, Connectionist, and Statistical Approaches to Learning for Natural Language Processing, S. Wermter, E. Riloff and G. Scheler (Eds.), Spring Verlag, 1996.

  28. Learning the Past Tense of English Verbs Using Inductive Logic Programming [Abstract] [PDF]
    Raymond J. Mooney and Mary Elaine Califf
    Symbolic, Connectionist, and Statistical Approaches to Learning for Natural Language Processing, S. Wermter, E. Riloff and G. Scheler (Eds.), Spring Verlag, 1996.

  29. Inducing Logic Programs without Explicit Negative Examples [Abstract] [PDF]
    John M. Zelle, Cynthia A. Thompson, Mary Elaine Califf, and Raymond J. Mooney
    Proceedings of the Fifth International Workshop on Inductive Logic Programming, pp. 403-416, Leuven, Belguim, Sepetember 1995.

  30. Using Inductive Logic Programming to Automate the Construction of Natural Language Parsers [Abstract] [PDF]
    John M. Zelle
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, August 1995.
    204 pages
    Also appears as Technical Report AI 96-249, Department of Computer Sciences, University of Texas at Austin

  31. A Comparison of Two Methods Employing Inductive Logic Programming for Corpus-based Parser Constuction [Abstract] [PDF]
    John M. Zelle and Raymond J. Mooney
    Working Notes of the IJCAI-95 Workshop on New Approaches to Learning for Natural Language Processing, pp.79-86, Montreal, Quebec, Canada, August 1995.

  32. Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs [Abstract] [PDF]
    Raymond J. Mooney and Mary Elaine Califf
    Journal of Artificial Intelligence Research, 3 (1995) pp. 1-24.

  33. Automated Refinement of First-Order Horn-Clause Domain Theories [Abstract] [PDF]
    Bradley L. Richards and Raymond J. Mooney
    Machine Learning 19,2 (1995), pp. 95-131.

  34. Combining Top-Down And Bottom-Up Techniques In Inductive Logic Programming [Abstract] [PDF]
    John M. Zelle, Raymond J. Mooney, and Joshua B. Konvisser
    Proceedings of the Eleventh International Workshop on Machine Learning (ML-94), pp. 343-351, Rutgers, NJ, July 1994.

  35. Inducing Deterministic Prolog Parsers From Treebanks: A Machine Learning Approach [Abstract] [PDF]
    John M. Zelle and Raymond J. Mooney
    Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), pp. 748-753, Seattle, WA, July 1994.

  36. Integrating ILP and EBL [Abstract] [PDF]
    Raymond J. Mooney and John M. Zelle
    SIGART Bulletin, Volume 5, Number 1, pp. 12-21, January 1994.

  37. Combining FOIL and EBG to Speed-Up Logic Programs [Abstract] [PDF]
    John M. Zelle and Raymond J. Mooney
    Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), pp. 1106-1111, Chambéry, France, July 1993.

  38. Learning Semantic Grammars With Constructive Inductive Logic Programming [Abstract] [PDF]
    John M. Zelle and Raymond J. Mooney
    Proceedings of the Eleventh National Conference of the American Association for Artificial Intelligence (AAAI-93), pp. 817-822, Washington, D.C. July 1993.

  39. Speeding-up Logic Programs by Combining EBG and FOIL [Abstract] [PDF]
    John M. Zelle and Raymond J. Mooney
    Proceedings of the 1992 Machine Learning Workshop on Knowledge Compilation and Speedup Learning, Aberdeen, Scotland, July 1992.

  40. Learning Relations by Pathfinding [Abstract] [PDF]
    Bradley L. Richards and Raymond J. Mooney
    Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pp. 50-55, San Jose, CA, July 1992.

mooney@cs.utexas.edu