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.
People
Shruti Bhosale Formerly affiliated Masters Student shruti [at] cs utexas edu
Publications
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Online Inference-Rule Learning from Natural-Language Extractions 2013
Sindhu Raghavan and Raymond J. Mooney, In Proceedings of the 3rd Statistical Relational AI (StaRAI-13) workshop at AAAI '13, July 2013.
Bayesian Logic Programs for Plan Recognition and Machine Reading 2012
Sindhu Raghavan, PhD Thesis, Department of Computer Science, University of Texas at Austin. 170.
Extending Bayesian Logic Programs for Plan Recognition and Machine Reading 2011
Sindhu V. Raghavan, Technical Report, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Improving the Accuracy and Scalability of Discriminative Learning Methods for Markov Logic Networks 2011
Tuyen N. Huynh, PhD Thesis, Department of Computer Science, University of Texas at Austin.
159 pages.
Online Structure Learning for Markov Logic Networks 2011
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), Vol. 2, pp. 81-96, September 2011.
Discriminative Learning with Markov Logic Networks 2009
Tuyen N. Huynh, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Discriminative Structure and Parameter Learning for Markov Logic Networks 2008
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
Safe Formulas in the General Theory of Stable Models (preliminary report) 2008
Joohyung Lee, Vladimir Lifschitz, and Ravi Palla, In International Conference on Logic Programming (ICLP) 2008.
Twelve Definitions of a Stable Model 2008
Vladimir Lifschitz, In Proceedings of International Conference on Logic Programming (ICLP), pp. 37-51 2008.
A Characterization of Strong Equivalence for Logic Programs with Variables 2007
Vladimir Lifschitz, David Pearce and Agustin Valverde, In Procedings of International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR) 2007.
Learning Semantic Parsers: An Important But Under-Studied Problem 2004
Raymond J. Mooney, In Papers from the AAAI 2004 Spring Symposium on Language Learning: An Interdisciplinary Perspective, pp. 39--44, Stanford, CA, March 2004.
Relational Data Mining with Inductive Logic Programming for Link Discovery 2004
Raymond J. Mooney, P. Melville, L. R. Tang, J. Shavlik, I. Dutra and D. Page, Data Mining: Next Generation Challenges and Future DirectionsKargupta, H., Joshi, A., Sivakumar K., and Yesha, Y. (Eds.) (2004), pp. 239--254. AAAI Press.
Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction 2003
Mary Elaine Califf and Raymond J. Mooney, Journal of Machine Learning Research (2003), pp. 177-210.
Integrating Top-down and Bottom-up Approaches in Inductive Logic Programming: Applications in Natural Language Processing and Relational Data Mining 2003
Lappoon R. Tang, PhD Thesis, Department of Computer Sciences, University of Texas.
Scaling Up ILP to Large Examples: Results on Link Discovery for Counter-Terrorism 2003
Lappoon R. Tang, Raymond J. Mooney, and Prem Melville, In Proceedings of the KDD-2003 Workshop on Multi-Relational Data Mining (MRDM-2003), pp. 107--121, Washington DC, August 2003.
Relational Data Mining with Inductive Logic Programming for Link Discovery 2002
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.
Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing 2001
Lappoon R. Tang and Raymond J. Mooney, In Proceedings of the 12th European Conference on Machine Learning, pp. 466-477, Freiburg, Germany 2001.
Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing 2000
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), pp. 133-141, Hong Kong, October 2000.
Relational Learning of Pattern-Match Rules for Information Extraction 1999
Mary Elaine Califf and Raymond J. Mooney, In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), pp. 328-334, Orlando, FL, July 1999.
Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming 1998
Mary Elaine Califf and Raymond J. Mooney, New Generation Computing, Vol. 16, 3 (1998), pp. 263-281.
An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions 1998
Lappoon R. Tang, Mary Elaine Califf, and Raymond J. Mooney, Technical Report AI 98-271, Artificial Intelligence Lab, University of Texas at Austin.
Relational Learning of Pattern-Match Rules for Information Extraction 1998
Mary Elaine Califf and Raymond J. Mooney, In Proceedings of AAAI Spring Symposium on Applying Machine Learning to Discourse Processing, pp. 6-11, Standford, CA, March 1998.
Relational Learning Techniques for Natural Language Information Extraction 1998
Mary Elaine Califf, PhD Thesis, Department of Computer Sciences, University of Texas. 142 pages. Also appears as Artificial Intelligence Laboratory Technical Report AI 98-276.
Using Multi-Strategy Learning to Improve Planning Efficiency and Quality 1998
Tara A. Estlin, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin.
An Inductive Logic Programming Method for Corpus-based Parser Construction 1997
John M. Zelle and Raymond J. Mooney, unpublished. Unpublished Technical Note.
Applying ILP-based Techniques to Natural Language Information Extraction: An Experiment in Relational Learning 1997
Mary Elaine Califf and Raymond J. Mooney, In Workshop Notes of the IJCAI-97 Workshop on Frontiers of Inductive Logic Programming, pp. 7--11, Nagoya, Japan, August 1997.
Learning to Improve both Efficiency and Quality of Planning 1997
Tara A. Estlin and Raymond J. Mooney, In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pp. 1227-1232, Nagoya, Japan 1997.
Learning to Parse Natural Language Database Queries into Logical Form 1997
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.
Relational Learning of Pattern-Match Rules for Information Extraction 1997
Mary Elaine Califf and Raymond J. Mooney, In Proceedings of the ACL Workshop on Natural Language Learning, pp. 9-15, Madrid, Spain, July 1997.
Relational Learning Techniques for Natural Language Information Extraction 1997
Mary Elaine Califf, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Update by Means of Inference Rules 1997
Teodor Przymusinski and Hudson Turner, Journal of Logic Programming, Vol. 30, 2 (1997), pp. 125-143.
Advantages of Decision Lists and Implicit Negative in Inductive Logic Programming 1996
Mary Elaine Califf and Raymond J. Mooney, Technical Report, Artificial Intelligence Lab, University of Texas at Austin.
Comparative Results on Using Inductive Logic Programming for Corpus-based Parser Construction 1996
John M. Zelle and Raymond J. Mooney, In Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, Stefan Wermter and Ellen Riloff and Gabriela Scheler (Eds.), pp. 355-369, Berlin 1996. Spri...
Inductive Logic Programming for Natural Language Processing 1996
Raymond J. Mooney, In Inductive Logic Programming: Selected papers from the 6th International Workshop, Stephen Muggleton (Eds.), pp. 3-22, Berlin 1996. Springer Verlag.
Integrating EBL and ILP to Acquire Control Rules for Planning 1996
Tara A. Estlin and Raymond J. Mooney, Proceedings of the Third International Workshop on Multi-Strategy Learning (MSL-96) (1996), pp. 271--279.
Integrating Explanation-Based and Inductive Learning Techniques to Acquire Search-Control for Planning 1996
Tara A. Estlin, Technical Report AI96-250, Department of Computer Sciences, University of Texas.
Learning the Past Tense of English Verbs Using Inductive Logic Programming 1996
Raymond J. Mooney and Mary Elaine Califf, In Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, {S. Wermter, E. Riloff} and G. Scheler (Eds.), pp. 370-384, Berlin 1996. Springer.
Learning to Parse Database Queries using Inductive Logic Programming 1996
John M. Zelle and Raymond J. Mooney, In AAAI/IAAI, pp. 1050-1055, Portland, OR, August 1996. AAAI Press/MIT Press.
Multi-Strategy Learning of Search Control for Partial-Order Planning 1996
Tara A. Estlin and Raymond J. Mooney, In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 843-848, Portland, OR, August 1996.
A Comparison of Two Methods Employing Inductive Logic Programming for Corpus-based Parser Constuction 1995
John M. Zelle and Raymond J. Mooney, In Working Notes of the IJCAI-95 Workshop on New Approaches to Learning for Natural Language Processing, pp. 79--86, Montreal, Quebec, Canada, August 1995.
Automated Refinement of First-Order Horn-Clause Domain Theories 1995
Bradley L. Richards and Raymond J. Mooney, Machine Learning, Vol. 19, 2 (1995), pp. 95-131.
Inducing Logic Programs without Explicit Negative Examples 1995
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), pp. 403-416, Leuven, Belgium 1995.
Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs 1995
Raymond J. Mooney and Mary Elaine Califf, Journal of Artificial Intelligence Research, Vol. 3 (1995), pp. 1-24.
SLDNF, Constructive Negation and Grounding 1995
Vladimir Lifschitz, In Proceedings ICLP-95, pp. 581-595 1995.
Using Inductive Logic Programming to Automate the Construction of Natural Language Parsers 1995
John M. Zelle, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin.
Combining Top-Down And Bottom-Up Techniques In Inductive Logic Programming 1994
John M. Zelle, Raymond J. Mooney, and Joshua B. Konvisser, In Proceedings of the Eleventh International Workshop on Machine Learning (ML-94), pp. 343--351, Rutgers, NJ, July 1994.
Inducing Deterministic Prolog Parsers From Treebanks: A Machine Learning Approach 1994
John M. Zelle and Raymond J. Mooney, Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94) (1994), pp. 748--753.
Integrating ILP and EBL 1994
Raymond J. Mooney and John M. Zelle, Sigart Bulletin (special issue on Inductive Logic Programmming), Vol. 5, 1 (1994), pp. 12-21.
Language Independence and Language Tolerance in Logic Programs 1994
Norman McCain and Hudson Turner, In Proceedings Eleventh Int'l Conf. on Logic Programming, Van Hentenryck, Pascal (Eds.), pp. 38-57 1994.
Signed Logic Programs 1994
Hudson Turner, In Proceedings ILPS-94, pp. 61-75 1994.
A Monotonicity Theorem for Extended Logic Programs 1993
Hudson Turner, In Proceedings Tenth Int'l Conf. on Logic Programming, pp. 567-585 1993.
Combining FOIL and EBG to Speed-Up Logic Programs 1993
John M. Zelle and Raymond J. Mooney, In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1106-1111 1993. San Francisco, CA: Morgan Kaufmann.
Learning Semantic Grammars With Constructive Inductive Logic Programming 1993
John M. Zelle and Raymond J. Mooney, In Proceedings of the 11th National Conference on Artificial Intelligence, pp. 817-822 1993. Menlo Park, CA: AAAI Press.
Learning Relations by Pathfinding 1992
Bradley L. Richards and Raymond J. Mooney, In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pp. 50-55, San Jose, CA, July 1992.
Speeding-up Logic Programs by Combining EBG and FOIL 1992
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.
First-Order Theory Revision 1991
Bradley L. Richards and Raymond J. Mooney, In Proceedings of the Eighth International Machine Learning Workshop, pp. pp. 447-451, Evanston, IL, June 1991.
Labs
Machine Learning