ACM Home Page
Please provide us with feedback. Feedback
Probabilistic logic learning
Full text PdfPdf (1.98 MB)
Source ACM SIGKDD Explorations Newsletter archive
Volume 5 ,  Issue 1  (July 2003) table of contents
COLUMN: Multi Relational Data Mining (MRDM) table of contents
Pages: 31 - 48  
Year of Publication: 2003
ISSN:1931-0145
Authors
Luc De Raedt  Albert-Ludwigs-University, Georges-Koehler-Allee, Freiburg, Germany
Kristian Kersting  Albert-Ludwigs-University, Georges-Koehler-Allee, Freiburg, Germany
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 8,   Downloads (12 Months): 99,   Citation Count: 10
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues   peer to peer  

Tools and Actions: Review this Article  
Save this Article to a Binder    Display Formats: BibTex  EndNote ACM Ref   
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/959242.959247
What is a DOI?

ABSTRACT

The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning. A rich variety of different formalisms and learning techniques have been developed. This paper provides an introductory survey and overview of the state-of-the-art in probabilistic logic learning through the identification of a number of important probabilistic, logical and learning concepts.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
J. Allen. Natural Language Understanding. Benjamin/Cummings Series in Computer Science. Benjamin/Cummings Publishing Company, 1987.
2
 
3
 
4
F. Bacchus. Using first-order probability logic for the construction of bayesian networks. In D. Heckerman and A. Mamdani, editors, Proceedings of the Ninth Annual Conference on Uncertainty in Artificial Intelligence (UAI-93), pages 219--226, Providence, Washington, DC, USA, 1993. Morgan Kaufmann.
 
5
 
6
J. S. Breese. Construction of Belief and decision networks. Computational Intelligence, 8(4):624--647, 1992.
 
7
J. S. Breese, R. P. Goldman, and M. P. Wellman. Introduction to the special section on knowledge-based construction of probabilistic and decision models. Cybernetics, 24(11):1577--1579, 1994.
 
8
 
9
K. L. Clark and F. G. McCabe. PROLOG: A Language for Implementing Expert Systems. In J. E. Hayes, D. Michie, and Y. H. Pao, editors, Machine Intelligence, volume 10, pages 455--470. Ellis Horwood, Chichester, 1982.
 
10
 
11
 
12
J. Cussens. Loglinear models for first-order probabilistic reasoning. In K. B. Laskey and H. Prade, editors, Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-99), pages 126--133, Stockholm, Sweden, 1999. Morgan Kaufmann.
 
13
 
14
 
15
J. Cussens. Statistical aspects of stochastic logic programs. In T. Jaakkola and T. Richardson, editors, Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics 2001, pages 181--186, Key West, Florida, USA, 2001. Morgan Kaufmann.
 
16
 
17
 
18
 
19
T. Dean and K. Kanazawa. Probabilistic temporal reasoning. In T. M. Mitchell and R. G. Smith, editors, Proceedings of the Seventh National Conference on Artificial Intelligence (AAAI-88), pages 524--528, St. Paul, MN, USA, 1988. AAAI Press / The MIT Press.
 
20
A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc., B 39 :1--39, 1977.
 
21
 
22
R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis: Probabilistic models of proteins and nucleic acids. Cambridge University Press, 1998.
 
23
 
24
A. Eisele. Towards probabilistic extensions of contraint-based grammars. In J. Dörne, editor, Computational Aspects of Constraint-Based Linguistics Decription-II. DYNA-2 deliverable R1.2.B, 1994.
 
25
William Feller. An Introduction to Probability Theory and its Applications: Volume 1. Wiley Series in Probability and Mathematical Statistics. John Wiley & Sons, 3rd edition, 1968.
 
26
 
27
 
28
P. Frasconi, M. Gori, and A. Sperduti. A general framework for adaptive processing of data structures. IEEE Transactions on Neural Networks, 9(5):768--786, 1998.
 
29
N. Friedman. The Bayesian Structural EM Algorithm. In G. F. Cooper and S. Moral, editors, Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-98), pages 129--138, Madison, Wisconsin, USA, 1998. Morgan Kaufmann.
 
30
 
31
 
32
L. Getoor, N. Friedman, and D. Koller. Learning Structured Statistical Models from Relational Data. Linköping Electronic Articles in Computer and Information Science, 7(13), 2002.
 
33
L. Getoor, N. Friedman, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In S. Džeroski and N. Lavrač, editors, Relational Data Mining. Springer-Verlag, 2001.
 
34
 
35
L. Getoor, D. Koller, and B. Taskar. Statistical Models for Relational Data. In S. Džeroski and L. De Raedt, editors, Workshop Notes of the KDD-02 Workshop on Multi-Relational Data Mining (MRDM-O2), 2002.
 
36
L. Getoor, D. Koller, B. Taskar, and N. Friedman. Learning probabilistic relational models with structural uncertainty. In L. Getoor and D. Jensen, editors, Proceedings of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data, pages 13--20, 2000.
 
37
L. Getoor, E. Segal, B. Taskar, and D. Koller. Probabilistic Models of Text and Link Structure for Hypertext Classification. In Workshop Notes of IJCAI-01 Workshop on 'Text Learning: Beyond Supervision', Washington, USA, 2001.
38
 
39
 
40
 
41
P. Haddawy. Generating Bayesian networks from probabilistic logic knowledge bases. In R. Löpez de Mántaras and D. Poole, editors, Proceedings of the Tenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-1994), pages 262--269, Seattle, Washington, USA, 1994. Morgan Kaufmann.
 
42
P. Haddawy, J. W. Helwig, L. Ngo, and R. A. Krieger. Clinical Simulation using Context-Sensitive Temporal Probability Models. In Proceedings of the Nineteenth Annual Symposium on Computer Applications an Medical Care (SCAMC-95), 1995.
 
43
 
44
D. Heckerman. A Tutorial on Learning with Bayesian Networks. Technical Report MSR-TR-95-06, Microsoft Research, 1995.
 
45
M. Jaeger. Relational Bayesian networks. In D. Geiger and P. P. Shenoy, editors, Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), pages 266--273, Providence, Rhode Island, USA, 1997. Morgan Kaufmann.
 
46
 
47
 
48
 
49
 
50
 
51
 
52
 
53
 
54
K. Kersting and L. De Raedt. Principles of Learning Bayesian Logic Programs. Technical Report 174, University of Freiburg, Institute for Computer Science, June 2002. (submitted).
 
55
K. Kersting, T. Raiko, S. Kramer, and L. De Raedt. Towards discovering structural signatures of protein folds based on logical hidden markov models. In R. B. Altman, A. K. Dunker, L. Hunter, T. A. Jung, and T. E. Klein, editors, Proceedings of the Pacific Symposium on Biocomputing, pages 192--203, Kauai, Hawaii, USA, 2003. World Scientific.
 
56
K. Kersting, T. Raiko, and L. De Raedt. A Structural GEM for Learning Logical Hidden Markov Models. In S. Džeroski, L. De Raedt, and S. Wrobel, editors, Workshop Notes of the KDD-03 Workshop on Multi-Relational Data Mining (MRDM-03), 2003. (to appear).
 
57
 
58
D. Koller, A. Levy, and A. Pfeffer. P-classic: A tractable probabilistic description logic. In Proceedings of the Fourteenth National Conference on AI, pages 390--397, Providence, Rhode Island, August 1997.
 
59
D. Koller and A. Pfeffer. Learning probabilities for noisy first-order rules. In Proceedings of the Fifteenth Joint Conference on Artificial Intelligence (IJCAI-97), pages 1316--1321, Nagoya, Japan, 1997.
 
60
D. Koller and A. Pfeffer. Object-oriented Bayesian networks. In D. Geiger and P. P. Shenoy, editors, Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), pages 302--313, Providence, Rhode Island, USA, 1997. Morgan Kaufmann.
 
61
 
62
N. Lachiche and P. Flach. 1BC2: A True First-Order Bayesian Classifier. In S. Matwin and C. Sammut, editors, Proceedings of the Twelfth International Conference on Inductive Logic Prgramming (ILP-02), volume 2583 of LNCS, pages 133--148, Sydney, Australia, 2002. Springer.
 
63
 
64
 
65
G. J. McKachlan and T. Krishnan. The EM Algorithm and Extensions. John Eiley & Sons, Inc., 1997.
 
66
S. Muggleton. Stochastic logic programs. In L. De Raedt, editor, Advances in Inductive Logic Programming. IOS Press, 1996.
 
67
S. Muggleton. Learning stochastic logic programs. Electronic Transactions in Artificial Intelligence, 4(041), 2000.
 
68
S. Muggleton. Learning structure and parameters of stochastic logic programs. In S. Matwin and C. Sammut, editors, Proceedings of the Twelfth International Conference on Inductive Logic Prgramming (ILP-02), volume 2583 of LNCS, pages 198--206, Sydney, Australia, 2002. Springer.
 
69
S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19(20):629--679, 1994.
 
70
S. H. Muggleton. Learning stochastic logic programs. In L. Getoor and D. Jensen, editors, Working Notes of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data (SRL-00), pages 36--41, Austin, Texas, 2000. AAAI Press.
 
71
 
72
L. Ngo and P. Haddawy. A Knowledge-Based Model Construction Approach to Medical Decision Making. In Proceedings of the Twentieth American Medical Informatics Association Annual Fall Symposium (AMIA-96), Washington DC, USA, 1996.
 
73
 
74
 
75
 
76
H. Pasula, B. Marthi, B. Milch, S. Russell, and I. Shpitser. Identity Uncertainty and Citation Matching. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15. MIT Press, 2003.
 
77
H. Pasula and S. Russell. Approximate inference for first-order probabilistic languages. In B. Nebel, editor, Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pages 741--748, Seattle, Washington, USA, 2001. Morgan Kaufmann.
 
78
 
79
 
80
A. Pfeffer, D. Koller, B. Milch, and K. T. Takusagawa. Spook: A system for probabilistic object-oriented knowledge representation. In Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-1999), 1999.
 
81
 
82
 
83
 
84
L. R. Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77(2):257--286, 1989.
 
85
L. R. Rabiner and B. H. Juang. An introduction to hidden Markov models. IEEE ASSP Magazine, pages 4--15, January 1986.
 
86
 
87
V. Santos Costa, D. Page, M. Qazi, and J. Cussens. CLP(BN): Constraint Logic Programming for Probabilistic Knowledge. In Proceedings of the Nineteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-03), Mexico, 2003. Morgan Kaufman. (to appear).
 
88
T. Sato. A Statistical Learning Method for Logic Programs with Distribution Semantics. In L. Sterling, editor, Proceedings of the Twelfth International Conference on Logic Programming (ICLP-1995), pages 715--729, Tokyo, Japan, 1995. MIT Press.
 
89
 
90
T. Sato and Y. Kameya. PRISM: A Symbolic-Statistical Modeling Language. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pages 1330--1339, Nagoya, Japan, 1997. Morgan Kaufmann.
 
91
T. Sato and Y. Kameya. A Viterbi-like algorithm and EM learning for statistical abduction. In R. Dybowski, J. Myers, and S. Parsons, editors, Workshop Notes of UAI-00 Workshop on Fusion of Domain Knowledge with Data for Decision Support, Stanford, CA, USA, 2000.
 
92
T. Sato and Y. Kameya. Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research, 15:391--454, 2001.
93
 
94
E. Segal, A. Battle, and D. Koller. Decomposing Gene Expression into Cellular Processes. In R. B. Altman, A. K. Dunker, L. Hunter, T. A. Jung, and T. E. Klein, editors, Proceedings of the Pacific Symposium on Biocomputing, pages 89--100, Kauai, Hawaii, USA, 2003. World Scientific.
 
95
E. Segal, B. Taskar, A. Gasch, N. Friedman, and D. Koller. Rich Probabilistic Models for Gene Expression. Bioinformatics, 17:S243--S252, 2001.
 
96
 
97
E. Y. Shapiro. Logic Programs with Uncertainties: A Tool for Implementing Expert Systems. In A. Bundy, editor, Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-1983), pages 529--532, Karlsruhe, Germany, 1983. William Kaufmann.
 
98
B. Taskar, E. Segal, and D. Koller. Probabilistic clustering in relational data. In B. Nebel, editor, Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pages 870--87, Seattle, Washington, USA, 2001. Morgan Kaufmann.
 
99
 
100
S. Wrobel. First Order Theory Refinement. In L. De Raedt, editor, Advances in Inductive Logic Programming. IOS Press, 1996.

CITED BY  10
 
 
 
 
 

Collaborative Colleagues:
Luc De Raedt: colleagues
Kristian Kersting: colleagues

Peer to Peer - Readers of this Article have also read: