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Learning probabilistic Description logic concepts: under different Assumptions on missing knowledge

Published: 26 March 2012 Publication History

Abstract

Knowledge available through Semantic Web standards can be missing, generally because of the adoption of the Open World Assumption. We present a Statistical Relational Learning system for learning terminological naïve Bayesian classifiers, which estimate the probability that an individual belongs to a target concept given its membership to a set of Description Logic concepts. During the learning process, we consistently handle the lack of knowledge that may be introduced by the adoption of the Open World Assumption, depending on the varying nature of the missing knowledge itself.

References

[1]
T. Berners-Lee, J. Hendler, and O. Lassila. The semantic web. Scientific American, 284(5): 34--43, May 2001.
[2]
V. Bicer, T. Tran, and A. Gossen. Relational kernel machines for learning from graph-structured rdf data. In ESWC2011.
[3]
R. Caruana and A. Niculescu-Mizil. An empirical comparison of supervised learning algorithms. In ICML2006.
[4]
G. Corani and M. Zaffalon. Learning reliable classifiers from small or incomplete data sets: The naive credal classifier 2. Journal of Machine Learning Research, 9: 581--621, 2008.
[5]
C. d'Amato, N. Fanizzi, and F. Esposito. Query answering and ontology population: an inductive approach. In ESWC2008.
[6]
S. R. de Morais and A. Aussem. Exploiting data missingness in bayesian network modeling. In IDA2009.
[7]
P. Domingos, D. Lowd, S. Kok, H. Poon, M. Richardson, and P. Singla. Just add weights: Markov logic for the semantic web. In URSW2008, pages 1--25.
[8]
P. Domingos and M. J. Pazzani. On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning, 29(2--3): 103--130, 1997.
[9]
N. Fanizzi, C. D'Amato, and F. Esposito. Learning with kernels in description logics. In ILP2008.
[10]
N. Fanizzi, C. D'Amato, and F. Esposito. Reduce: A reduced coulomb energy network method for approximate classification. In ESWC2009.
[11]
N. Friedman et al. Bayesian network classifiers. In Machine Learning, pages 131--163, 1997.
[12]
L. Getoor and B. Taskar. Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning). The MIT Press, 2007.
[13]
C. Kiefer, A. Bernstein, and A. Locher. Adding data mining support to sparql via statistical relational learning methods. In ESWC2008.
[14]
D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.
[15]
K. J. Laskey and K. B. Laskey. Uncertainty reasoning for the world wide web: Report on the urw3-xg incubator group. In URSW2008.
[16]
J. Lehmann et al. Concept learning in description logics using refinement operators. Mach. Learn., 78: 203--250.
[17]
J. E. O. Luna and F. G. Cozman. An algorithm for learning with probabilistic description logics. In URSW2009.
[18]
M. Ramoni and P. Sebastiani. Robust learning with missing data. Mach. Learn., 45: 147--170, October 2001.
[19]
M. Richardson and P. Domingos. Markov logic networks. Mach. Learn., 62: 107--136, February 2006.
[20]
D. B. Rubin. Inference and missing data. Biometrika, 63(3): 581--592, 1976.
[21]
V. Tresp, M. Bundschus, Y. Huang, and A. Rettinger. Materializing and querying learned knowledge. In IRMLeS2009.
[22]
V. Tresp, M. Bundschus, A. Rettinger, and Y. Huang. Towards machine learning on the semantic web. In URSW2008.
[23]
M. Zaffalon, G. Corani, and D. Mauá. Utility-based accuracy measures to empirically evaluate credal classifiers. In ISIPTA 2011, pages 401--410, Innsbruck.

Cited By

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  • (2024)PN-OWL: A two-stage algorithm to learn fuzzy concept inclusions from OWL 2 ontologiesFuzzy Sets and Systems10.1016/j.fss.2024.109048(109048)Online publication date: Jun-2024
  • (2015)pFOIL-DLProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695707(345-352)Online publication date: 13-Apr-2015
  • (2014)Learning Probabilistic Description LogicsUncertainty Reasoning for the Semantic Web III10.1007/978-3-319-13413-0_4(63-78)Online publication date: 30-Nov-2014
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cover image ACM Conferences
SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing
March 2012
2179 pages
ISBN:9781450308571
DOI:10.1145/2245276
  • Conference Chairs:
  • Sascha Ossowski,
  • Paola Lecca
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 26 March 2012

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SAC 2012: ACM Symposium on Applied Computing
March 26 - 30, 2012
Trento, Italy

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SAC '12 Paper Acceptance Rate 270 of 1,056 submissions, 26%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2024)PN-OWL: A two-stage algorithm to learn fuzzy concept inclusions from OWL 2 ontologiesFuzzy Sets and Systems10.1016/j.fss.2024.109048(109048)Online publication date: Jun-2024
  • (2015)pFOIL-DLProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695707(345-352)Online publication date: 13-Apr-2015
  • (2014)Learning Probabilistic Description LogicsUncertainty Reasoning for the Semantic Web III10.1007/978-3-319-13413-0_4(63-78)Online publication date: 30-Nov-2014
  • (2013)Learning Probabilistic Description LogicsRevised Selected Papers of the ISWC International Workshops on Uncertainty Reasoning for the Semantic Web III - Volume 881610.5555/2788478.2788482(63-78)Online publication date: 21-Oct-2013

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