Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: 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, transfer learning, and abductive reasoning.

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  1. Natural Language Semantics Using Probabilistic Logic
    [Details] [PDF] [Slides (PPT)] [Slides (PDF)]
    I. Beltagy
    PhD Thesis, Department of Computer Science, The University of Texas at Austin, December 2016.
  2. Representing Meaning with a Combination of Logical and Distributional Models
    [Details] [PDF]
    I. Beltagy and Stephen Roller and Pengxiang Cheng and Katrin Erk and Raymond J. Mooney
    The special issue of Computational Linguistics on Formal Distributional Semantics, 42(4), 2016.
  3. On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics
    [Details] [PDF] [Slides (PPT)]
    I. Beltagy and Katrin Erk
    In Proceedings of the 11th International Conference on Computational Semantics (IWCS-2015), London, UK, April 2015.
  4. University of Texas at Austin KBP 2014 Slot Filling System: Bayesian Logic Programs for Textual Inference
    [Details] [PDF]
    Yinon Bentor and Vidhoon Viswanathan and Raymond Mooney
    In Proceedings of the Seventh Text Analysis Conference: Knowledge Base Population (TAC 2014), 2014.
  5. Natural Language Semantics using Probabilistic Logic
    [Details] [PDF] [Slides (PPT)]
    I. Beltagy
    October 2014. PhD proposal, Department of Computer Science, The University of Texas at Austin.
  6. UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic Logic
    [Details] [PDF]
    I. Beltagy and Stephen Roller and Gemma Boleda and and Katrin Erk and Raymond J. Mooney
    In The 8th Workshop on Semantic Evaluation (SemEval-2014), 796--801, Dublin, Ireland, August 2014.
  7. Efficient Markov Logic Inference for Natural Language Semantics
    [Details] [PDF] [Poster]
    I. Beltagy and Raymond J. Mooney
    In Proceedings of the Fourth International Workshop on Statistical Relational AI at AAAI (StarAI-2014), 9--14, Quebec City, Canada, July 2014.
  8. Semantic Parsing using Distributional Semantics and Probabilistic Logic
    [Details] [PDF] [Poster]
    I. Beltagy and Katrin Erk and Raymond Mooney
    In Proceedings of ACL 2014 Workshop on Semantic Parsing (SP-2014), 7--11, Baltimore, MD, June 2014.
  9. Probabilistic Soft Logic for Semantic Textual Similarity
    [Details] [PDF] [Poster]
    I. Beltagy and Katrin Erk and Raymond J. Mooney
    In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL-14), 1210--1219, Baltimore, MD, 2014.
  10. Plan Recognition Using Statistical Relational Models
    [Details] [PDF]
    Sindhu Raghavan and Parag Singla and Raymond J. Mooney
    In Sukthankar, G. and Geib, C. and Bui, H.H. and Pynadath, D. and Goldman, R.P., editors, Plan, Activity, and Intent Recognition: Theory and Practice, 57--85, Burlington, MA, 2014. Morgan Kaufmann.
  11. 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.
  12. Montague Meets Markov: Deep Semantics with Probabilistic Logical Form
    [Details] [PDF] [Slides (PPT)]
    I. Beltagy, Cuong Chau, Gemma Boleda, Dan Garrette, Katrin Erk, Raymond Mooney
    In Proceedings of the Second Joint Conference on Lexical and Computational Semantics (*Sem-2013), 11--21, Atlanta, GA, June 2013.
  13. A Formal Approach to Linking Logical Form and Vector-Space Lexical Semantics
    [Details] [PDF]
    Dan Garrette, Katrin Erk, Raymond J. Mooney
    In Harry Bunt, Johan Bos, and Stephen Pulman, editors, Computing Meaning, 27--48, Berlin, 2013. Springer.
  14. 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.
  15. Learning to "Read Between the Lines" using Bayesian Logic Programs
    [Details] [PDF] [Slides (PPT)]
    Sindhu Raghavan and Raymond J. Mooney and Hyeonseo Ku
    In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL-2012), 349--358, July 2012.
  16. Constraint Propagation for Efficient Inference in Markov Logic
    [Details] [PDF] [Slides (PDF)]
    Tivadar Papai, Parag Singla and Henry Kautz
    In Proceedings of 17th International Conference on Principles and Practice of Constraint Programming (CP 2011), Lecture Notes in Computer Science (LNCS), 691-705, September 2011.
  17. 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.
  18. Abductive Plan Recognition by Extending Bayesian Logic Programs
    [Details] [PDF] [Slides (PPT)]
    Sindhu Raghavan, Raymond J. Mooney
    In Proceedings of the European Conference on Machine Learning/Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2011), 629-644, September 2011.
  19. Abductive Markov Logic for Plan Recognition
    [Details] [PDF] [Slides (PPT)]
    Parag Singla and Raymond J. Mooney
    In Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI-2011), 1069-1075, August 2011.
  20. 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.
  21. 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.
  22. Online Max-Margin Weight Learning for Markov Logic Networks
    [Details] [PDF] [Slides (PPT)]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the Eleventh SIAM International Conference on Data Mining (SDM11), 642--651, Mesa, Arizona, USA, April 2011.
  23. Implementing Weighted Abduction in Markov Logic
    [Details] [PDF]
    James Blythe, Jerry R. Hobbs, Pedro Domingos, Rohit J. Kate, Raymond J. Mooney
    In Proceedings of the International Conference on Computational Semantics, 55--64, Oxford, England, January 2011.
  24. Integrating Logical Representations with Probabilistic Information using Markov Logic
    [Details] [PDF] [Slides (PDF)]
    Dan Garrette, Katrin Erk, Raymond Mooney
    In Proceedings of the International Conference on Computational Semantics, 105--114, Oxford, England, January 2011.
  25. Online Max-Margin Weight Learning with Markov Logic Networks
    [Details] [PDF] [Slides (PPT)]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), 32--37, Atlanta, GA, July 2010.
  26. Bayesian Abductive Logic Programs
    [Details] [PDF] [Slides (PPT)]
    Sindhu Raghavan and Raymond Mooney
    In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), 82--87, Atlanta, GA, July 2010.
  27. 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.
  28. Learning with Markov Logic Networks: Transfer Learning, Structure Learning, and an Application to Web Query Disambiguation
    [Details] [PDF]
    Lilyana Mihalkova
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, August 2009. 176 pages.
  29. Max-Margin Weight 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), Part 1, 564--579, Bled, Slovenia, September 2009.
  30. Learning to Disambiguate Search Queries from Short Sessions
    [Details] [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, 111--127, Bled, Slovenia, September 2009.
  31. Max-Margin Weight Learning for Markov Logic Networks
    [Details] [PDF]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the International Workshop on Statistical Relational Learning (SRL-09), Leuven, Belgium, July 2009.
  32. Speeding up Inference In Statistical Relational Learning by Clustering Similar Query Literals
    [Details] [PDF]
    Lilyana Mihalkova and Matthew Richardson
    In Proceedings of the 19th International Conference on Inductive Logic Programming (ILP-09), Leuven, Belgium, July 2009.
  33. Probabilistic Abduction using Markov Logic Networks
    [Details] [PDF] [Slides (PPT)]
    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.
  34. Transfer Learning from Minimal Target Data by Mapping across Relational Domains
    [Details] [PDF]
    Lilyana Mihalkova and Raymond Mooney
    In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-09), 1163--1168, Pasadena, CA, July 2009.
  35. Search Query Disambiguation from Short Sessions
    [Details] [PDF]
    Lilyana Mihalkova and Raymond Mooney
    In Beyond Search: Computational Intelligence for the Web Workshop at NIPS, 2008.
  36. 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.
  37. Transfer Learning by Mapping with Minimal Target Data
    [Details] [PDF]
    Lilyana Mihalkova and Raymond J. Mooney
    In Proceedings of the AAAI-08 Workshop on Transfer Learning For Complex Tasks, Chicago, IL, July 2008.
  38. Improving Learning of Markov Logic Networks using Transfer and Bottom-Up Induction
    [Details] [PDF]
    Lilyana Mihalkova
    Technical Report UT-AI-TR-07-341, Artificial Intelligence Lab, University of Texas at Austin, Austin, TX, May 2007.
  39. Learning for Information Extraction: From Named Entity Recognition and Disambiguation To Relation Extraction
    [Details] [PDF]
    Razvan Constantin Bunescu
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, August 2007. 150 pages. Also as Technical Report AI07-345, Artificial Intelligence Lab, University of Texas at Austin, August 2007.
  40. Mapping and Revising Markov Logic Networks for Transfer Learning
    [Details] [PDF]
    Lilyana Mihalkova, Tuyen N. Huynh, Raymond J. Mooney
    In Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI-07), 608-614, Vancouver, BC, July 2007.
  41. Bottom-Up Learning of Markov Logic Network Structure
    [Details] [PDF]
    Lilyana Mihalkova and Raymond J. Mooney
    In Proceedings of 24th International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.
  42. Statistical Relational Learning for Natural Language Information Extraction
    [Details] [PDF]
    Razvan Bunescu and Raymond J. Mooney
    In L. Getoor and B. Taskar, editors, Introduction to Statistical Relational Learning, 535-552, Cambridge, MA, 2007. MIT Press.
  43. Transfer Learning with Markov Logic Networks
    [Details] [PDF]
    Lilyana Mihalkova and Raymond Mooney
    In Proceedings of the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning, Pittsburgh, PA, June 2006.
  44. Learning for Collective Information Extraction
    [Details] [PDF]
    Razvan C. Bunescu
    Technical Report TR-05-02, Department of Computer Sciences, University of Texas at Austin, October 2005. Ph.D. proposal.
  45. Collective Information Extraction with Relational Markov Networks
    [Details] [PDF]
    Razvan Bunescu and Raymond J. Mooney
    In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), 439-446, Barcelona, Spain, July 2004.
  46. Relational Markov Networks for Collective Information Extraction
    [Details] [PDF]
    Razvan Bunescu and Raymond J. Mooney
    In Proceedings of the ICML-04 Workshop on Statistical Relational Learning and its Connections to Other Fields, Banff, Alberta, July 2004.
  47. A Comparison of Inference Techniques for Semi-supervised Clustering with Hidden Markov Random Fields
    [Details] [PDF]
    Mikhail Bilenko and Sugato Basu
    In Proceedings of the ICML-2004 Workshop on Statistical Relational Learning and its Connections to Other Fields (SRL-2004), Banff, Canada, July 2004.
  48. 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.