General
A. Kimmig, G. Van den Broeck, and L. De Raedt. An algebraic Prolog for reasoning about possible worlds. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, pages 209-214. AAAI Press, 2011. PDF
A. Kimmig, B. Demoen, L. De Raedt, V. Santos Costa, and R. Rocha. On the Implementation of the Probabilistic Logic Programming Language ProbLog. In M. Garcia de la Banda and E. Pontelli, Theory and Practice of Logic Programming, 2011(11), pages 235-262. DOI 10.1017/S1471068410000566 PDF
M. Bruynooghe, T. Mantadelis, A. Kimmig, B. Gutmann, J. Vennekens, G. Janssens, and L. De Raedt. ProbLog technology for inference in a probabilistic first order logic. In H. Coelho, R. Studer, and M. Woolridge, European Conference on Artificial Intelligence (ECAI 2010), pages 719-714. IOS Press 2010. DOI 10.3233/978-1-60750-606-5-719 PDF
L. De Raedt, A. Kimmig, B. Gutmann, K. Kersting, V. Santos Costa, and H. Toivonen. Probabilistic Inductive Querying Using ProbLog. In S. Dzeroski, B. Goethals, and P. Panov, Inductive Databases and Constraint-Based Data Mining. Springer, 2010. DOI 10.1007/978-1-4419-7738-0_10 accompanying Tech Report
L. De Raedt, B. Demoen, D. Fierens, B. Gutmann, G. Janssens, A. Kimmig, N. Landwehr, T. Mantadelis, W. Meert, R. Rocha, V. Santos Costa, I. Thon, and J. Vennekens. Towards digesting the alphabet-soup of statistical relational learning, NIPS 2008 Workshop Probabilistic Programming, Whistler, Canada. 2008 PDF
A. Kimmig, V. Santos Costa, R. Rocha, B. Demoen, and L. De Raedt. On the Efficient Execution of ProbLog Programs, Proceedings 24th International Conference on Logic Programming (ICLP 2008), Udine, Italy, Lecture Notes in Computer Science, volume 5366, pages 175-189, 2008. DOI 10.1007/978-3-540-89982-2_22 PDF
L. De Raedt, A. Kimmig, and H. Toivonen, ProbLog: A probabilistic Prolog and its application in link discovery, IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, pages 2462-2467, 2007. PDF
Inference
J. Renkens, G. Van den Broeck, S. Nijssen. k-optimal: A novel approximate inference algorithm for ProbLog. Springer New York LLC, Series: Machine Learning, volume 86, issue 3, p. 215-231. PDF
T. Mantadelis and G. Janssens, Nesting probabilistic inference. In International Colloquium on Implementation of Constraint and LOgic Programming Systems (CICLOPS 2011). PDF
D. Fierens, G. Van den Broeck, I. Thon, B. Gutmann, and L. De Raedt. Inference in probabilistic logic programs using weighted CNF's. In Proceedings of the Twenty-Seventh Annual Conference on Uncertainty in Artificial Intelligence (UAI-11), pages 211-220. AUAI Press, 2011. PDF accompanying Tech Report
B. Gutmann, I. Thon, A. Kimmig, M. Bruynooghe, and L. De Raedt. The magic of logical inference in probabilistic programming. Theory and Practice of Logic Programming, 2011(11), pages 663-680. DOI 10.1017/S1471068411000238 PDF
T. Mantadelis, R. Rocha, A. Kimmig, and G. Janssens. Preprocessing Boolean formulae for BDDs in a probabilistic context, Logics in Artificial Intelligence, 12th European Conference, JELIA 2010, Proceedings vol:6341 issue:12 pages:260-272, 2010. PDF
D. Sht. Shterionov, A. Kimmig, T. Mantadelis and G. Janssens. DNF Sampling for ProbLog Inference, International Colloquium on Implementation of Constraint and Logic Programming Systems (CICLOPS 2010), Edinburgh, Scotland, 2010. PDF
T. Mantadelis and G. Janssens. Dedicated tabling for a probabilistic setting, International Conference on Logic Programming (ICLP 2010), Edinburgh, Scotland, 2010. PDF
T. Mantadelis and G. Janssens. Variable compression in ProbLog, In Logic for Programming, Artificial Intelligence and Reasoning (LPAR), Lecture Notes in Computer Science/ARCoSS 6397 pages 504-518, Springer, 2010. DOI 10.1007/978-3-642-16242-8_36i PDF accompanying Tech Report
B. Gutmann, M. Jaeger and L. De Raedt. Extending ProbLog with continuous distributions. In P. Frasconi and F. Lisi, editors, Proceedings of the 20th International Conference on Inductive Logic Programming (ILP-10), volume 6489 of LNCS (Lecture Notes in Computer Science), pages 76--91. Springer Berlin / Heidelberg, 2011. DOI 10.1007/978-3-642-21295-6_12 PDF
Learning
B. Gutmann, I. Thon, and L. De Raedt. Learning the parameters of probabilistic logic programs from interpretations. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2011), volume 6911 of LNCS (Lecture Notes in Computer Science), pages 581-596. Springer Berlin/Heidelberg, 2011. DOI 10.1007/978-3-540-87479-9_49 PDF accompanying Tech Report
B. Gutmann, A. Kimmig, L. De Raedt, and K. Kersting. Parameter Learning in Probabilistic Databases: A Least Squares Approach, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008), Antwerp, Belgium, Lecture Notes in Computer Science, volume 5211, pages 473-488, 2008. DOI 10.1007/978-3-540-87479-9_49 PDF Video accompanying Tech Report
A. Kimmig, L. De Raedt, and H. Toivonen, Probabilistic explanation based learning, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2007), Warsaw, Poland, Lecture notes in computer science, volume 4701, pages 176-187, 2007. DOI 10.1007/978-3-540-74958-5_19 PDF Video
L. De Raedt, K. Kersting, A. Kimmig, K. Revoredo, and H. Toivonen. Compressing probabilistic Prolog programs. Machine Learning 70(2-3):155-168, 2008. DOI 10.1007/s10994-007-5030-x PDF
Decision-Theoretic ProbLog
G. Van den Broeck, I. Thon, M. van Otterlo, L. De Raedt. DTProbLog: A decision-theoretic probabilistic Prolog. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2010), Atlanta, Georgia, USA, July 2010. PDF
I. Thon, B. Gutmann, G. Van den Broeck. Probabilistic programming for planning problems. Statistical Relational AI workshop. Atlanta, Georgia, USA, July 2010. PDF
Local Query Mining
A. Kimmig, L. De Raedt. Local query mining in a probabilistic Prolog. International Joint Conference on Artificial Intelligence 2009. PDF
Ph.D. Theses
I. Thon. Stochastic Relational Processes and Models: Learning and Reasoning. Katholieke Universiteit Leuven, Belgium. October 2011. PDF
B. Gutmann. On Continuous Distributions and Parameter Estimation in Probabilistic Logic Programs. Katholieke Universiteit Leuven, Belgium. October 2011. PDF
A. Kimmig. A Probabilistic Prolog and its Applications. Katholieke Universiteit Leuven, Belgium. November 2010. PDF
Datasets and Applications
D. Shterionov, G. Janssens. Data acquisition and modeling for learning and reasoning in probabilistic logic environment. Proceedings of 15th Portuguese Conference on Artificial Intelligence (EPIA 2011), Lisbon, Portugal, October 2011. PDF
A. Kimmig, F. Costa. Link and node prediction in metabolic networks with probabilistic logic. Lecture Notes in Artificial Intelligence vol:7250, Bisociative Knowledge Discovery pages:407-426, Springer, 2012 PDF