UT ML Group: Publication: Statistical Relational Learning

Statistical Relational Learning (SRL), studies techniques that combine the strengths of relational learning (e.g. inductive logic programming) and probabilistic learning (e.g. Bayesian networks). By combining the power of logic and probability, such systems can perform robust and accurate reasoning and learning about complex relational data. See the book: Introduction to Statistical Relational Learning. Our work in the area has primarily focused on applications of SRL methods to problems in natural language processing and transfer learning

Publications

  1. Learning with Markov Logic Networks: Transfer Learning, Structure Learning, and an Application to Web Query Disambiguation [Abstract] [PDF]
    Lilyana Mihalkova
    Ph.D. thesis, Department of Computer Sciences, University of Texas at Austin, August 2009.
    176 pages.

  2. Max-Margin Weight Learning for Markov Logic Networks [Abstract] [PDF] [Slide]
    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), Part 1, pp. 564-579, Bled, Slovenia, September 2009.

  3. Learning to Disambiguate Search Queries from Short Sessions [Abstract] [PDF]
    Lilyana Mihalkova and Raymond Mooney
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Part 2, pp. 111-127, Bled, Slovenia, September 2009.

  4. Max-Margin Weight Learning for Markov Logic Networks [Abstract] [PDF]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the International Workshop on Statistical Relational Learning (SRL-09), Leuven, Belgium, July 2009.

  5. Speeding up Inference In Statistical Relational Learning by Clustering Similar Query Literals [Abstract] [PDF]
    Lilyana Mihalkova and Matthew Richardson
    In Proceedings of the 19th International Conference on Inductive Logic Programming (ILP-09), Leuven, Belgium, July 2009.

  6. Probabilistic Abduction using Markov Logic Networks [Abstract] [PDF]
    Rohit J. Kate and Raymond J. Mooney
    In Proceedings of the IJCAI-09 Workshop on Plan, Activity, and Intent Recognition (PAIR-09), Pasadena, CA, July 2009.

  7. Transfer Learning from Minimal Target Data by Mapping across Relational Domains [Abstract] [PDF]
    Lilyana Mihalkova and Raymond Mooney
    In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-09), pp. 1163--1168, Pasadena, CA, July 2009.

  8. Search Query Disambiguation from Short Sessions [Abstract] [PDF]
    Lilyana Mihalkova and Raymond Mooney
    In Beyond Search: Computational Intelligence for the Web Workshop at NIPS 2008.

  9. Discriminative Structure and Parameter Learning for Markov Logic Networks [Abstract] [PDF] [Slide]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the 25th International Conference on Machine Learning (ICML) , Helsinki, Finland, July 2008.

  10. Transfer Learning by Mapping with Minimal Target Data [Abstract] [PDF]
    Lilyana Mihalkova and Raymond J. Mooney
    In Proceedings of the AAAI-08 Workshop on Transfer Learning For Complex Tasks , Chicago, IL, July 2008.

  11. Improving Learning of Markov Logic Networks using Transfer and Bottom-Up Induction [Abstract] [PDF]
    Lilyana Mihalkova
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, May 2007.
    49 pages.
    Also appears as Technical Report UT-AI-TR-07-341, Artificial Intelligence Lab, University of Texas at Austin, May 2007.

  12. Learning for Information Extraction: From Named Entity Recognition and Disambiguation To Relation Extraction [Abstract] [PDF]
    Razvan Constantin Bunescu
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, August 2007.
    150 pages.
    Also appears as Technical Report AI07-345, Artificial Intelligence Lab, University of Texas at Austin, August 2007.

  13. Mapping and Revising Markov Logic Networks for Transfer Learning [Abstract] [PDF]
    Lilyana Mihalkova, Tuyen Huynh, Raymond J. Mooney
    In Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI-2007), Vancouver, BC, pp. 608-614, July 2007.

  14. Bottom-Up Learning of Markov Logic Network Structure [Abstract] [PDF]
    Lilyana Mihalkova and Raymond J. Mooney
    In Proceedings of the 24th Annual International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.

  15. Statistical Relational Learning for Natural Language Information Extraction [Abstract] [PDF]
    Razvan Bunescu and Raymond J. Mooney
    Introduction to Statistical Relational Learning, Getoor, L. and Taskar, B. (Eds.), pp. 535-552, MIT Press, Cambridge, MA, 2007.

  16. Transfer Learning with Markov Logic Networks [Abstract] [PDF]
    Lilyana Mihalkova and Raymond Mooney
    In Proceedings of the ICML Workshop on Structural Knowledge Transfer for Machine Learning, Pittsburgh, PA, July 2006.

  17. Learning for Collective Information Extraction [Abstract] [PDF]
    Razvan C. Bunescu
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, October 2004.
    45 pages.
    Also appears as Technical Report TR-05-02, Artificial Intelligence Lab, University of Texas at Austin, February 2005.

  18. Collective Information Extraction with Relational Markov Networks [Abstract] [PDF]
    Razvan Bunescu and Raymond J. Mooney
    Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-2004), pp. 439-446, Barcelona, Spain, July 2004.

  19. Relational Markov Networks for Collective Information Extraction [Abstract] [PDF]
    Razvan Bunescu and Raymond J. Mooney
    Proceedings of the ICML-2004 Workshop on Statistical Relational Learning and its Connections to Other Fields (SRL-2004), Banff, Canada, July 2004.

  20. A Comparison of Inference Techniques for Semi-supervised Clustering with Hidden Markov Random Fields [Abstract] [PDF]
    Mikhail Bilenko and Sugato Basu
    Proceedings of the ICML-2004 Workshop on Statistical Relational Learning and its Connections to Other Fields (SRL-2004), Banff, Canada, July 2004.

  21. 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

mooney@cs.utexas.edu