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Instance-based probabilistic reasoning in the semantic web

Published: 20 April 2009 Publication History

Abstract

Most of the approaches for dealing with uncertainty in the Semantic Web rely on the principle that this uncertainty is already asserted. In this paper, we propose a new approach to learn and reason about uncertainty in the Semantic Web. Using instance data, we learn the uncertainty of an OWL ontology, and use that information to perform probabilistic reasoning on it. For this purpose, we use Markov logic, a new representation formalism that combines logic with probabilistic graphical models.

References

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T. Berners-Lee, J. Hendler, and O. Lassila, "The Semantic Web," Scientific American, vol. 284, 2001, pp. 28--37.
[2]
T. Lukasiewicz and U. Straccia, "Managing Uncertainty and Vagueness in Description Logics for the Semantic Web," Web Semantics Sci Serv Agents World Wide Web, 2008.
[3]
A. Tversky and D. Kahneman, "Judgment under Uncertainty: Heuristics and Biases," Science, vol. 185, Sep. 1974, pp. 1124--1131.
[4]
P. Domingos, S. Kok, D. Lowd, H. Poon, M. Richardson, and P. Singla, "Markov Logic," Probabilistic Inductive Logic Programming, 2008, pp. 92--117.
[5]
F. Baader, D. Calvanese, D.L. McGuinness, D. Nardi, and P.F. Patel-Schneider, The Description Logic Handbook: Theory, Implementation, and Applications, Cambridge University Press, 2007.

Cited By

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  • (2020)Markov Logic Network-Based Group Activity Recognition in Smart BuildingsSmart and Sustainable Cities and Buildings10.1007/978-3-030-37635-2_32(459-467)Online publication date: 12-May-2020
  • (2018)Applying Knowledge-Based Reasoning for Information Fusion in Intelligence, Surveillance, and ReconnaissanceMultisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System10.1007/978-3-319-90509-9_7(119-139)Online publication date: 5-Jul-2018
  • (2017)Scaling Up Markov Logic Probabilistic Inference for Social GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.262525129:2(433-445)Online publication date: 1-Feb-2017
  • Show More Cited By

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Published In

cover image ACM Conferences
WWW '09: Proceedings of the 18th international conference on World wide web
April 2009
1280 pages
ISBN:9781605584874
DOI:10.1145/1526709

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2009

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Author Tags

  1. markov logic
  2. probabilistic reasoning
  3. semantic web

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Cited By

View all
  • (2020)Markov Logic Network-Based Group Activity Recognition in Smart BuildingsSmart and Sustainable Cities and Buildings10.1007/978-3-030-37635-2_32(459-467)Online publication date: 12-May-2020
  • (2018)Applying Knowledge-Based Reasoning for Information Fusion in Intelligence, Surveillance, and ReconnaissanceMultisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System10.1007/978-3-319-90509-9_7(119-139)Online publication date: 5-Jul-2018
  • (2017)Scaling Up Markov Logic Probabilistic Inference for Social GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.262525129:2(433-445)Online publication date: 1-Feb-2017
  • (2015)Markov Logic Network Based Social Relation Inference for Personalized Social SearchNew Trends in Computational Collective Intelligence10.1007/978-3-319-10774-5_19(195-202)Online publication date: 2015
  • (2013)LinkProbeProceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)10.1109/ICDE.2013.6544833(290-301)Online publication date: 8-Apr-2013
  • (2013)Probabilistic spatio-temporal inference for motion event understandingNeurocomputing10.1016/j.neucom.2012.12.058122(24-32)Online publication date: Dec-2013
  • (2011)Using the Web to Validate Lexico-Semantic RelationsProgress in Artificial Intelligence10.1007/978-3-642-24769-9_43(597-609)Online publication date: 2011

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