Learning systems of concepts with an infinite relational model

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2006, Pages 381-388

  Kemp, Charles ^a   Tenenbaum, Joshua B ^a   Griffiths, Thomas L ^b   Yamada, Takeshi ^c   Ueda, Naonori ^c  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b BROWN UNIVERSITY   (United States)

^c NTT Communication Science Laboratories   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN MODEL; KINSHIP SYSTEMS; LEARNING ONTOLOGIES; RELATIONAL MODEL;

ARTIFICIAL INTELLIGENCE; KNOWLEDGE ACQUISITION; MATHEMATICAL MODELS; RELATIONAL DATABASE SYSTEMS; SEMANTICS;

LEARNING SYSTEMS;

EID: 33750696648     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Antoniak, C. 1974. Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. The Annals of Statistics 2:1152-1174.
2. Davis, E. 1990. Representations of commonsense knowledge.
3. Denham, W. 1973. The detection of patterns in Alyawarra nonverbal behavior. Ph.D. Dissertation, University of Washington.
4. Getoor, L.; Friedman, N.; Koller, D.; and Taskar, B. 2002. Learning probabilistic models of link structure. Journal of Machine Learning Research 3:679-707.
5. Hofmann, T., and Puzicha, J. 1999. Latent class models for collaborative filtering. In Proc. 16th International Joint Conference on Artificial Intelligence.
6. Hubert, L., and Arabie, P. 1985. Comparing partitions. Journal of Classification 2:193-218.
7. Jain, S., and Neal, R. M. 2004. A split-merge Markov chain Monte Carlo procedure for the Dirichlet Process mixture model. Journal of Computational and Graphical Statistics 13:158-182.
8. Kok, S., and Domingos, P. 2005. Learning the structure of Markov logic networks. In Proc. 22nd International Conference on Machine Learning.
9. Kubica, J.; Moore, A.; Schneider, J.; and Yang, Y. 2002. Stochastic link and group detection. In Proc. 17th National Conference on Artificial Intelligence.
10. McCray, A. T.; Burgun, A.; and Bodenreider, O. 2001. Aggregating UMLS semantic types for reducing conceptual complexity. In Medinfo, volume 10, 216-20.
11. McCray, A. T. 2003. An upper level ontology for the biomedical domain. Comparative and Functional Genomics 4:80-84.
12. Minsky, M. 1975. A framework for representing knowledge. In Winston, P., ed., The psychology of computer vision.
13. Muggleton, S., and De Raedt, L. 1994. Inductive logic programming: theory and methods. Journal of Logic Programming 19,20:629-679.
14. Nowicki, K., and Snijders, T. A. B. 2001. Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association 96:1077-1087.
15. Osherson, D. N.; Stern, J.; Wilkie, O.; Stob, M.; and Smith, E.E. 1991. Default probability. Cognitive Science 15:251-269.
16. Pitman, J. 2002. Combinatorial stochastic processes. Notes for Saint Flour Summer School.
17. Rasmussen, C. 2000. The infinite Gaussian mixture model. In Advances in Neural Information Processing Systems 12.
18. Rummel, R. J. 1999. Dimensionality of Nations project: attributes of nations and behavior of nation dyads, 1950-1965. ICPSR data file.
19. Taskar, B.; Segal, E.; and Koller, D. 2001. Probabilistic classification and clustering in relational data. In Proc. 18th International Joint Conference on Artificial Intelligence, volume 15.
20. Wang, X.; Mohanty, N.; and McCallum, A. 2005. Group and topic discovery from relations and text. In Proc. KDD workshop on link discovery.
21. Wolfe, A. P., and Jensen, D. 2004. Playing multiple roles: discovering overlapping roles in social networks. In Proc. ICML workshop on statistical relational learning and Its connections to other fields.