Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: 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.

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  1. Online Inference-Rule Learning from Natural-Language Extractions
    [Details] [PDF] [Poster]
    Sindhu Raghavan and Raymond J. Mooney
    In Proceedings of the 3rd Statistical Relational AI (StaRAI-13) workshop at AAAI '13, July 2013.
  2. Bayesian Logic Programs for Plan Recognition and Machine Reading
    [Details] [PDF] [Slides (PPT)]
    Sindhu Raghavan
    PhD Thesis, Department of Computer Science, University of Texas at Austin, December 2012. 170.
  3. Online Structure Learning for Markov Logic Networks
    [Details] [PDF] [Slides (PPT)]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2011), 81-96, September 2011.
  4. Extending Bayesian Logic Programs for Plan Recognition and Machine Reading
    [Details] [PDF] [Slides (PPT)]
    Sindhu V. Raghavan
    Technical Report, PhD proposal, Department of Computer Science, The University of Texas at Austin, May 2011.
  5. Improving the Accuracy and Scalability of Discriminative Learning Methods for Markov Logic Networks
    [Details] [PDF] [Slides (PPT)]
    Tuyen N. Huynh
    PhD Thesis, Department of Computer Science, University of Texas at Austin, May 2011.
    159 pages.
  6. Discriminative Learning with Markov Logic Networks
    [Details] [PDF] [Slides (PPT)]
    Tuyen N. Huynh
    October 2009. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  7. Discriminative Structure and Parameter Learning for Markov Logic Networks
    [Details] [PDF] [Slides (PPT)]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
  8. Learning Semantic Parsers: An Important But Under-Studied Problem
    [Details] [PDF]
    Raymond J. Mooney
    In Papers from the AAAI 2004 Spring Symposium on Language Learning: An Interdisciplinary Perspective, 39--44, Stanford, CA, March 2004.
  9. Relational Data Mining with Inductive Logic Programming for Link Discovery
    [Details] [PDF]
    Raymond J. Mooney, P. Melville, L. R. Tang, J. Shavlik, I. Dutra and D. Page
    Kargupta, H., Joshi, A., Sivakumar K., and Yesha, Y., editors, Data Mining: Next Generation Challenges and Future Directions:239--254, Menlo Park, CA, 2004. AAAI Press.
  10. Integrating Top-down and Bottom-up Approaches in Inductive Logic Programming: Applications in Natural Language Processing and Relational Data Mining
    [Details] [PDF]
    Lappoon R. Tang
    PhD Thesis, Department of Computer Sciences, University of Texas, Austin, TX, August 2003.
  11. Scaling Up ILP to Large Examples: Results on Link Discovery for Counter-Terrorism
    [Details] [PDF]
    Lappoon R. Tang, Raymond J. Mooney, and Prem Melville
    In Proceedings of the KDD-2003 Workshop on Multi-Relational Data Mining (MRDM-2003), 107--121, Washington DC, August 2003.
  12. Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction
    [Details] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    Journal of Machine Learning Research:177-210, 2003.
  13. Relational Data Mining with Inductive Logic Programming for Link Discovery
    [Details] [PDF]
    Raymond J. Mooney, Prem Melville, Lappoon R. Tang, Jude Shavlik, Inês de Castro Dutra, David Page, and Vítor Santos Costa
    In Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, Baltimore, MD, November 2002.
  14. Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing
    [Details] [PDF]
    Lappoon R. Tang and Raymond J. Mooney
    In Proceedings of the 12th European Conference on Machine Learning, 466-477, Freiburg, Germany, 2001.
  15. Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing
    [Details] [PDF]
    Lappoon R. Tang and Raymond J. Mooney
    In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora(EMNLP/VLC-2000), 133-141, Hong Kong, October 2000.
  16. Relational Learning of Pattern-Match Rules for Information Extraction
    [Details] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), 328-334, Orlando, FL, July 1999.
  17. Relational Learning Techniques for Natural Language Information Extraction
    [Details] [PDF]
    Mary Elaine Califf
    PhD Thesis, Department of Computer Sciences, University of Texas, Austin, TX, August 1998. 142 pages. Also appears as Artificial Intelligence Laboratory Technical Report AI 98-276.
  18. An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions
    [Details] [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.
  19. Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming
    [Details] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    New Generation Computing, 16(3):263-281, 1998.
  20. Using Multi-Strategy Learning to Improve Planning Efficiency and Quality
    [Details] [PDF]
    Tara A. Estlin
    PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 1998.
  21. Relational Learning of Pattern-Match Rules for Information Extraction
    [Details] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    In Proceedings of AAAI Spring Symposium on Applying Machine Learning to Discourse Processing, 6-11, Standford, CA, March 1998.
  22. Relational Learning Techniques for Natural Language Information Extraction
    [Details] [PDF]
    Mary Elaine Califf
    1997. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  23. Learning to Improve both Efficiency and Quality of Planning
    [Details] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), 1227-1232, Nagoya, Japan, 1997.
  24. Applying ILP-based Techniques to Natural Language Information Extraction: An Experiment in Relational Learning
    [Details] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    In Workshop Notes of the IJCAI-97 Workshop on Frontiers of Inductive Logic Programming, 7--11, Nagoya, Japan, August 1997.
  25. Learning to Parse Natural Language Database Queries into Logical Form
    [Details] [PDF]
    Cynthia A. Thompson, Raymond J. Mooney, and Lappoon R. Tang
    In Proceedings of the ML-97 Workshop on Automata Induction, Grammatical Inference, and Language Acquisition, Nashville, TN, July 1997.
  26. Relational Learning of Pattern-Match Rules for Information Extraction
    [Details] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    In Proceedings of the ACL Workshop on Natural Language Learning, 9-15, Madrid, Spain, July 1997.
  27. An Inductive Logic Programming Method for Corpus-based Parser Construction
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    January 1997. Unpublished Technical Note.
  28. Inductive Logic Programming for Natural Language Processing
    [Details] [PDF]
    Raymond J. Mooney
    In Stephen Muggleton, editors, Inductive Logic Programming: Selected papers from the 6th International Workshop, 3-22, Berlin, 1996. Springer Verlag.
  29. Integrating Explanation-Based and Inductive Learning Techniques to Acquire Search-Control for Planning
    [Details] [PDF]
    Tara A. Estlin
    Technical Report AI96-250, Department of Computer Sciences, University of Texas, Austin, TX, 1996.
  30. Learning to Parse Database Queries using Inductive Logic Programming
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In AAAI/IAAI, 1050-1055, Portland, OR, August 1996. AAAI Press/MIT Press.
  31. Multi-Strategy Learning of Search Control for Partial-Order Planning
    [Details] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), 843-848, Portland, OR, August 1996.
  32. Integrating EBL and ILP to Acquire Control Rules for Planning
    [Details] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    In Proceedings of the Third International Workshop on Multi-Strategy Learning (MSL-96), 271--279, Harpers Ferry, WV, May 1996.
  33. Advantages of Decision Lists and Implicit Negative in Inductive Logic Programming
    [Details] [PDF]
    Mary Elaine Califf and Raymond J. Mooney
    Technical Report, Artificial Intelligence Lab, University of Texas at Austin, January 1996.
  34. Comparative Results on Using Inductive Logic Programming for Corpus-based Parser Construction
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Stefan Wermter and Ellen Riloff and Gabriela Scheler, editors, Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, 355-369, Berlin, 1996. Springer.
  35. Learning the Past Tense of English Verbs Using Inductive Logic Programming
    [Details] [PDF]
    Raymond J. Mooney and Mary Elaine Califf
    In {S. Wermter, E. Riloff} and G. Scheler, editors, Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, 370-384, Berlin, 1996. Springer.
  36. Inducing Logic Programs without Explicit Negative Examples
    [Details] [PDF]
    John M. Zelle, Cynthia A. Thompson, Mary Elaine Califf, and Raymond J. Mooney
    In Proceedings of the Fifth International Workshop on Inductive Logic Programming (ILP-95), 403-416, Leuven, Belgium, 1995.
  37. Using Inductive Logic Programming to Automate the Construction of Natural Language Parsers
    [Details] [PDF]
    John M. Zelle
    PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 1995.
  38. A Comparison of Two Methods Employing Inductive Logic Programming for Corpus-based Parser Constuction
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Working Notes of the IJCAI-95 Workshop on New Approaches to Learning for Natural Language Processing, 79--86, Montreal, Quebec, Canada, August 1995.
  39. Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs
    [Details] [PDF]
    Raymond J. Mooney and Mary Elaine Califf
    Journal of Artificial Intelligence Research, 3:1-24, 1995.
  40. Automated Refinement of First-Order Horn-Clause Domain Theories
    [Details] [PDF]
    Bradley L. Richards and Raymond J. Mooney
    Machine Learning, 19(2):95-131, 1995.
  41. Combining Top-Down And Bottom-Up Techniques In Inductive Logic Programming
    [Details] [PDF]
    John M. Zelle, Raymond J. Mooney, and Joshua B. Konvisser
    In Proceedings of the Eleventh International Workshop on Machine Learning (ML-94), 343--351, Rutgers, NJ, July 1994.
  42. Inducing Deterministic Prolog Parsers From Treebanks: A Machine Learning Approach
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), 748--753, Seattle, WA, July 1994.
  43. Integrating ILP and EBL
    [Details] [PDF]
    Raymond J. Mooney and John M. Zelle
    Sigart Bulletin (special issue on Inductive Logic Programmming), 5(1):12-21, 1994.
  44. Combining FOIL and EBG to Speed-Up Logic Programs
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Proceedings of the 13th International Joint Conference on Artificial Intelligence, 1106-1111, 1993. San Francisco, CA: Morgan Kaufmann.
  45. Learning Semantic Grammars With Constructive Inductive Logic Programming
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Proceedings of the 11th National Conference on Artificial Intelligence, 817-822, 1993. Menlo Park, CA: AAAI Press.
  46. Speeding-up Logic Programs by Combining EBG and FOIL
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Proceedings of the 1992 Machine Learning Workshop on Knowledge Compilation and Speedup Learning, Aberdeen, Scotland, July 1992.
  47. Learning Relations by Pathfinding
    [Details] [PDF]
    Bradley L. Richards and Raymond J. Mooney
    In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), 50-55, San Jose, CA, July 1992.
  48. First-Order Theory Revision
    [Details] [PDF]
    Bradley L. Richards and Raymond J. Mooney
    In Proceedings of the Eighth International Machine Learning Workshop, pp. 447-451, Evanston, IL, June 1991.