Markov logic sets: Towards lifted information retrieval using pagerank and label propagation

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2011, Pages 447-452

  Neumann, Marion ^a   Ahmadi, Babak ^a   Kersting, Kristian ^a  

^a FRAUNHOFER IAIS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX OBJECTS; DATA SETS; LABEL PROPAGATION; MARKOV LOGIC; PAGERANK; RANDOM WALK; RELATIONAL LEARNING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; ATOMS; DATA PROCESSING; INFORMATION RETRIEVAL;

SEARCH ENGINES;

EID: 80055059427     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Ahmadi, B.; Kersting, K.; and Sanner, S. 2011. Multi-Evidence Lifted Message Passing, with Application to PageRank and the Kalman Filter. In Walsh, T., ed., Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI-11).
2. Allison, P. 2001. Pompeian Households: an Analysis of the Material Culture. Cotsen Institute of Archaeology.
3. Brin, S., and Page, L. 2006. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems 30:197-117.
4. Choi, J., and Amir, E. 2010. Lifted Inference for Relational Continuous Models. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI-10).
5. De Raedt, L. 2008. Logical and Relational Learning. Springer.
6. Getoor, L., and Taskar, B., eds. 2007. An Introduction to Statistical Relational Learning. MIT Press.
7. Ghahramani, Z., and Heller, K. 2005. Bayesian Sets. In Proc. Adv. in Neural Inform. Processing Systems (NIPS-05).
8. Hotho, A.; Jäschke, R.; Schmitz, C.; and Stumme, G. 2006. Information Retrieval in Folksonomies: Search and Ranking. In Proceedings of the 3rd European Semantic Web Conference (ESWC-06), 411-426.
9. Kersting, K.; Ahmadi, B.; and Natarajan, S. 2009. Counting Belief Propagation. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI-09).
10. Kimmig, A.; De Raedt, L.; and Toivonen, H. 2007. Probabilistic Explanation Based Learning. In Kok, J. N.; Koronacki, J.; de Mántaras, R. L.; Matwin, S.; Mladenic, D.; and Skowron, A., eds., ECML, volume 4701 of Lecture Notes in Computer Science, 176-187. Springer.
11. Kok, S., and Domingos, P. 2009. Learning Markov Logic Network Structure via Hypergraph Lifting. In Proc. of the International Conference on Machine Learning (ICML-09).
12. Lao, N., and Cohen, W. W. 2010. Relational retrieval using a combination of path-constrained random walks. Machine Learning 81(1):53-67.
13. Mihalkova, L., and Mooney, R. 2007. Bottom-Up Learning of Markov Logic Network Structure. In Proc. of the Intern. Conf. on Machine Learning (ICML-07).
14. Milch, B.; Zettlemoyer, L.; Kersting, K.; Haimes, M.; and Pack Kaelbling, L. 2008. Lifted Probabilistic Inference with Counting Formulas. In Proc. of the 23rd AAAI Conf. on Artificial Intelligence (AAAI-08).
15. Muggleton, S. 1996. Learning from Positive Data. In Proceedings of the Inductive Logic Programming Workshop (ILP-96), 358-376.
16. Neville, J., and Gallagher, B. 2009. Evaluating Statistical Tests for Within-Network Classifiers of Relational Data. In Proc. of the Intern. Conf. on Data Mining (ICDM-09).
17. Richardson, M., and Domingos, P. 2006. Markov Logic Networks. Machine Learning 62:107-136.
18. Shental, O.; Bickson, D.; Siegel, P. H.; Wolf, J. K.; and Dolev, D. 2008. Gaussian Belief Propagation Solver for Systems of Linear Equations. In Proc. of the IEEE Int. Symp. on Inform. Theory (ISIT).
19. Silva, R.; Heller, K.; Ghahramani, Z.; and Airoldi, E. 2010. Ranking Relations using Analogies in Biological and Information Networks. Annals of Applied Statistics.
20. Singla, P., and Domingos, P. 2008. Lifted First-Order Belief Propagation. In Proc. of the 23rd AAAI Conf. on Artificial Intelligence (AAAI-08), 1094-1099.
21. Zhou, D.; Bousquet, O.; Lal, T. N.; Weston, J.; and Schölkopf, B. 2003. Learning with Local and Global Consistency. In Thrun, S.; Saul, L. K.; and Schölkopf, B., eds., NIPS. MIT Press.