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.
People
Shruti Bhosale Formerly affiliated Masters Student shruti [at] cs utexas edu
Hyeonseo Ku Masters Alumni yorq [at] cs utexas edu
Heath Vinicombe Formerly affiliated Masters Student vini [at] cs utexas edu
Publications
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Natural Language Semantics Using Probabilistic Logic 2016
I. Beltagy, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Representing Meaning with a Combination of Logical and Distributional Models 2016
I. Beltagy, Stephen Roller, Pengxiang Cheng, Katrin Erk, and Raymond J. Mooney, The special issue of Computational Linguistics on Formal Distributional Semantics, Vol. 42, 4 (2016).
On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics 2015
I. Beltagy and Katrin Erk, In Proceedings of the 11th International Conference on Computational Semantics (IWCS-2015), London, UK, April 2015.
Efficient Markov Logic Inference for Natural Language Semantics 2014
I. Beltagy and Raymond J. Mooney, In Proceedings of the Fourth International Workshop on Statistical Relational AI at AAAI (StarAI-2014), pp. 9--14, Quebec City, Canada, July 2014.
Natural Language Semantics using Probabilistic Logic 2014
I. Beltagy, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Plan Recognition Using Statistical Relational Models 2014
Sindhu Raghavan, Parag Singla, and Raymond J. Mooney, In Plan, Activity, and Intent Recognition: Theory and Practice, Sukthankar, G. and Geib, C. and Bui, H.H. and Pynadath, D. and Goldman, R.P. (Eds.), pp. 57--85, Burlington, MA 2014. Morgan Kauf...
Probabilistic Soft Logic for Semantic Textual Similarity 2014
I. Beltagy, Katrin Erk, and Raymond J. Mooney, In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL-14), pp. 1210--1219, Baltimore, MD 2014.
Semantic Parsing using Distributional Semantics and Probabilistic Logic 2014
I. Beltagy, Katrin Erk, and Raymond Mooney, In Proceedings of ACL 2014 Workshop on Semantic Parsing (SP-2014), pp. 7--11, Baltimore, MD, June 2014.
University of Texas at Austin KBP 2014 Slot Filling System: Bayesian Logic Programs for Textual Inference 2014
Yinon Bentor, Vidhoon Viswanathan, and Raymond Mooney , In Proceedings of the Seventh Text Analysis Conference: Knowledge Base Population (TAC 2014) 2014.
UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic Logic 2014
I. Beltagy, Stephen Roller, Gemma Boleda, and Katrin Erk, and Raymond J. Mooney, In The 8th Workshop on Semantic Evaluation (SemEval-2014), pp. 796--801, Dublin, Ireland, August 2014.
A Formal Approach to Linking Logical Form and Vector-Space Lexical Semantics 2013
Dan Garrette, Katrin Erk, Raymond J. Mooney, In Computing Meaning, Harry Bunt, Johan Bos, and Stephen Pulman (Eds.), Vol. 4, pp. 27--48, Berlin 2013. Springer.
Montague Meets Markov: Deep Semantics with Probabilistic Logical Form 2013
I. Beltagy, Cuong Chau, Gemma Boleda, Dan Garrette, Katrin Erk, Raymond Mooney, Proceedings of the Second Joint Conference on Lexical and Computational Semantics (*Sem-2013) (2013), pp. 11--21.
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.
Learning to "Read Between the Lines" using Bayesian Logic Programs 2012
Sindhu Raghavan, Raymond J. Mooney, and Hyeonseo Ku, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL-2012) (2012), pp. 349--358.
Abductive Markov Logic for Plan Recognition 2011
Parag Singla and Raymond J. Mooney, Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI-2011) (2011), pp. 1069-1075.
Abductive Plan Recognition by Extending Bayesian Logic Programs 2011
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), Vol. 2, pp. 629-644, September 2011.
Constraint Propagation for Efficient Inference in Markov Logic 2011
Tivadar Papai, Parag Singla and Henry Kautz, In Proceedings of 17th International Conference on Principles and Practice of Constraint Programming (CP 2011), 6876, pp. 691-705, September 2011.
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.
Implementing Weighted Abduction in Markov Logic 2011
James Blythe, Jerry R. Hobbs, Pedro Domingos, Rohit J. Kate, Raymond J. Mooney, In Proceedings of the International Conference on Computational Semantics, pp. 55--64, Oxford, England, January 2011.
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.
Integrating Logical Representations with Probabilistic Information using Markov Logic 2011
Dan Garrette, Katrin Erk, Raymond Mooney, In Proceedings of the International Conference on Computational Semantics, pp. 105--114, Oxford, England, January 2011.
Online Max-Margin Weight Learning for Markov Logic Networks 2011
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the Eleventh SIAM International Conference on Data Mining (SDM11), pp. 642--651, Mesa, Arizona, USA, April 2011.
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.
Bayesian Abductive Logic Programs 2010
Sindhu Raghavan and Raymond Mooney, In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), pp. 82--87, Atlanta, GA, July 2010.
Online Max-Margin Weight Learning with Markov Logic Networks 2010
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), pp. 32--37, Atlanta, GA, July 2010.
Discriminative Learning with Markov Logic Networks 2009
Tuyen N. Huynh, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Learning to Disambiguate Search Queries from Short Sessions 2009
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.
Learning with Markov Logic Networks: Transfer Learning, Structure Learning, and an Application to Web Query Disambiguation 2009
Lilyana Mihalkova, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 176 pages.
Max-Margin Weight Learning for Markov Logic Networks 2009
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.
Max-Margin Weight Learning for Markov Logic Networks 2009
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the International Workshop on Statistical Relational Learning (SRL-09), Leuven, Belgium, July 2009.
Probabilistic Abduction using Markov Logic Networks 2009
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.
Speeding up Inference In Statistical Relational Learning by Clustering Similar Query Literals 2009
Lilyana Mihalkova and Matthew Richardson, In Proceedings of the 19th International Conference on Inductive Logic Programming (ILP-09), Leuven, Belgium, July 2009.
Transfer Learning from Minimal Target Data by Mapping across Relational Domains 2009
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.
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.
Search Query Disambiguation from Short Sessions 2008
Lilyana Mihalkova and Raymond Mooney, In Beyond Search: Computational Intelligence for the Web Workshop at NIPS 2008.
Transfer Learning by Mapping with Minimal Target Data 2008
Lilyana Mihalkova and Raymond J. Mooney, Proceedings of the AAAI-08 Workshop on Transfer Learning For Complex Tasks (2008).
Bottom-Up Learning of Markov Logic Network Structure 2007
Lilyana Mihalkova and Raymond J. Mooney, In Proceedings of 24th International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.
Improving Learning of Markov Logic Networks using Transfer and Bottom-Up Induction 2007
Lilyana Mihalkova, Technical Report UT-AI-TR-07-341, Artificial Intelligence Lab, University of Texas at Austin.
Learning for Information Extraction: From Named Entity Recognition and Disambiguation To Relation Extraction 2007
Razvan Constantin Bunescu, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 150 pages. Also as Technical Report AI07-345, Artificial Intelligence Lab, University of Texas at Austin, August 2007.
Mapping and Revising Markov Logic Networks for Transfer Learning 2007
Lilyana Mihalkova, Tuyen N. Huynh, Raymond J. Mooney, In Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI-07), pp. 608-614, Vancouver, BC, July 2007.
Statistical Relational Learning for Natural Language Information Extraction 2007
Razvan Bunescu and Raymond J. Mooney, In Introduction to Statistical Relational Learning, L. Getoor and B. Taskar (Eds.), pp. 535-552, Cambridge, MA 2007. MIT Press.
Transfer Learning with Markov Logic Networks 2006
Lilyana Mihalkova and Raymond Mooney, In Proceedings of the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning, Pittsburgh, PA, June 2006.
Learning for Collective Information Extraction 2005
Razvan C. Bunescu, Technical Report TR-05-02, Department of Computer Sciences, University of Texas at Austin. Ph.D. proposal.
A Comparison of Inference Techniques for Semi-supervised Clustering with Hidden Markov Random Fields 2004
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.
Collective Information Extraction with Relational Markov Networks 2004
Razvan Bunescu and Raymond J. Mooney, In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), pp. 439-446, Barcelona, Spain, July 2004.
Relational Markov Networks for Collective Information Extraction 2004
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.
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.
Labs
Machine Learning