Skip to main content
SpringerLink
Log in

Just Add Weights: Markov Logic for the Semantic Web

  • Conference paper
Book cover Uncertainty Reasoning for the Semantic Web I (URSW 2006, URSW 2007, URSW 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5327))

Abstract

In recent years, it has become increasingly clear that the vision of the Semantic Web requires uncertain reasoning over rich, first-order representations. Markov logic brings the power of probabilistic modeling to first-order logic by attaching weights to logical formulas and viewing them as templates for features of Markov networks. This gives natural probabilistic semantics to uncertain or even inconsistent knowledge bases with minimal engineering effort. Inference algorithms for Markov logic draw on ideas from satisfiability, Markov chain Monte Carlo and knowledge-based model construction. Learning algorithms are based on the conjugate gradient algorithm, pseudo-likelihood and inductive logic programming. Markov logic has been successfully applied to problems in entity resolution, link prediction, information extraction and others, and is the basis of the open-source Alchemy system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American 284(5), 34–43 (2001)

    Article  Google Scholar 

  2. Besag, J.: Statistical analysis of non-lattice data. The Statistician 24, 179–195 (1975)

    Article  Google Scholar 

  3. Braz, R., Amir, E., Roth, D.: Lifted first-order probabilistic inference. In: Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, Edinburgh, UK, pp. 1319–1325. Morgan Kaufmann, San Francisco (2005)

    Google Scholar 

  4. Collins, M.: Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, Philadelphia, PA, pp. 1–8. ACL (2002)

    Google Scholar 

  5. Damien, P., Wakefield, J., Walker, S.: Gibbs sampling for Bayesian non-conjugate and hierarchical models by auxiliary variables. Journal of the Royal Statistical Society, Series B 61 (1999)

    Google Scholar 

  6. De Raedt, L., Dehaspe, L.: Clausal discovery. Machine Learning 26, 99–146 (1997)

    Article  MATH  Google Scholar 

  7. Dean, M., Schreiber, G., Bechhofer, S., van Harmelen, F., Hendler, J., Horrocks, I., McGuinness, D.L., Patel-Schneider, P.F., Stein, L.A.: OWL web ontology language reference (2004), http://www.w3.org/TR/owl-ref/

  8. Della Pietra, S., Della Pietra, V., Lafferty, J.: Inducing features of random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 380–392 (1997)

    Article  Google Scholar 

  9. Dietterich, T., Bao, X.: Integrating multiple learning components through Markov logic. In: Proceedings of the Twenty-Third National Conference on Artificial Intelligence, Chicago, IL. AAAI Press, Menlo Park (2008)

    Google Scholar 

  10. Genesereth, M.R., Nilsson, N.J.: Logical Foundations of Artificial Intelligence. Morgan Kaufmann, San Mateo (1987)

    MATH  Google Scholar 

  11. Gilks, W.R., Richardson, S., Spiegelhalter, D.J. (eds.): Markov Chain Monte Carlo in Practice. Chapman and Hall, London (1996)

    MATH  Google Scholar 

  12. Grenager, T., Klein, D., Manning, C.D.: Unsupervised learning of field segmentation models for information extraction. In: Proceedings of the Forty-Third Annual Meeting on Association for Computational Linguistics, Ann Arbor, Michigan, pp. 371–378. Association for Computational Linguistics (2005)

    Google Scholar 

  13. Kautz, H., Selman, B., Jiang, Y.: A general stochastic approach to solving problems with hard and soft constraints. In: Gu, D., Du, J., Pardalos, P. (eds.) The Satisfiability Problem: Theory and Applications, pp. 573–586. American Mathematical Society, New York (1997)

    Google Scholar 

  14. Kok, S., Domingos, P.: Learning the structure of Markov logic networks. In: Proceedings of the Twenty-Second International Conference on Machine Learning, Bonn, Germany, pp. 441–448. ACM Press, New York (2005)

    Google Scholar 

  15. Kok, S., Domingos, P.: Statistical predicate invention. In: Proceedings of the Twenty-Fourth International Conference on Machine Learning, Corvallis, OR, pp. 433–440. ACM Press, New York (2007)

    Google Scholar 

  16. Kok, S., Sumner, M., Richardson, M., Singla, P., Poon, H., Lowd, D., Domingos, P.: The Alchemy system for statistical relational AI. Technical report, Department of Computer Science and Engineering, University of Washington, Seattle, WA (2007), http://alchemy.cs.washington.edu

  17. Kushmerick, N.: Wrapper induction: Efficiency and expressiveness. Artificial Intelligence 118(1-2), 15–68 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  18. Lavrač, N., Džeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood, Chichester (1994)

    MATH  Google Scholar 

  19. Lawrence, S., Bollacker, K., Giles, C.L.: Autonomous citation matching. In: Proceedings of the Third International Conference on Autonomous Agents. ACM Press, New York (1999)

    Google Scholar 

  20. Liu, D.C., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Mathematical Programming 45(3), 503–528 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  21. Lloyd, J.W.: Foundations of Logic Programming. Springer, Berlin (1987)

    Book  MATH  Google Scholar 

  22. Lowd, D., Domingos, P.: Efficient weight learning for Markov logic networks. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS, vol. 4702, pp. 200–211. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  23. Lunn, D.J., Thomas, A., Best, N., Spiegelhalter, D.: WinBUGS – a Bayesian modeling framework: concepts, structure, and extensibility. Statistics and Computing 10, 325–337 (2000)

    Article  Google Scholar 

  24. Matuszek, C., Witbrock, M.: Personal communication (2006)

    Google Scholar 

  25. Mihalkova, L., Mooney, R.: Bottom-up learning of Markov logic network structure. In: Proceedings of the Twenty-Fourth International Conference on Machine Learning, Corvallis, OR, pp. 625–632. ACM Press, New York (2007)

    Google Scholar 

  26. Møller, M.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6, 525–533 (1993)

    Article  Google Scholar 

  27. Ngo, L., Haddawy, P.: Answering queries from context-sensitive probabilistic knowledge bases. Theoretical Computer Science 171, 147–177 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  28. Nocedal, J., Wright, S.: Numerical Optimization. Springer, New York (2006)

    MATH  Google Scholar 

  29. Pasula, H., Marthi, B., Milch, B., Russell, S., Shpitser, I.: Identity uncertainty and citation matching. In: Advances in Neural Information Processing Systems 14. MIT Press, Cambridge (2002)

    Google Scholar 

  30. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)

    MATH  Google Scholar 

  31. Poole, D.: First-order probabilistic inference. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, Acapulco, Mexico, pp. 985–991. Morgan Kaufmann, San Francisco (2003)

    Google Scholar 

  32. Poon, H., Domingos, P.: Sound and efficient inference with probabilistic and deterministic dependencies. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence, Boston, MA, pp. 458–463. AAAI Press, Menlo Park (2006)

    Google Scholar 

  33. Poon, H., Domingos, P.: Joint inference in information extraction. In: Proceedings of the Twenty-Second National Conference on Artificial Intelligence, Vancouver, Canada, pp. 913–918. AAAI Press, Menlo Park (2007)

    Google Scholar 

  34. Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)

    Google Scholar 

  35. Richardson, M., Agrawal, R., Domingos, P.: Trust management for the Semantic Web. In: Fensel, D., Sycara, K.P., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  36. Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62, 107–136 (2006)

    Article  Google Scholar 

  37. Riedel, S., Klein, E.: Genic interaction extraction with semantic and syntactic chains. In: Proceedings of the Fourth Workshop on Learning Language in Logic, Bonn, Germany, pp. 69–74. IMLS (2005)

    Google Scholar 

  38. Robinson, J.A.: A machine-oriented logic based on the resolution principle. Journal of the ACM 12, 23–41 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  39. Roth, D.: On the hardness of approximate reasoning. Artificial Intelligence 82, 273–302 (1996)

    Article  MathSciNet  Google Scholar 

  40. Singla, P., Domingos, P.: Discriminative training of Markov logic networks. In: Proceedings of the Twentieth National Conference on Artificial Intelligence, Pittsburgh, PA, pp. 868–873. AAAI Press, Menlo Park (2005)

    Google Scholar 

  41. Singla, P., Domingos, P.: Entity resolution with Markov logic. In: Proceedings of the Sixth IEEE International Conference on Data Mining, Hong Kong, pp. 572–582. IEEE Computer Society Press, Los Alamitos (2006)

    Chapter  Google Scholar 

  42. Singla, P., Domingos, P.: Memory-efficient inference in relational domains. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence, Boston, MA. AAAI Press, Menlo Park (2006)

    Google Scholar 

  43. Singla, P., Domingos, P.: Markov logic in infinite domains. In: Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, Vancouver, Canada, pp. 368–375. AUAI Press (2007)

    Google Scholar 

  44. Singla, P., Domingos, P.: Lifted first-order belief propagation. In: Proceedings of the Twenty-Third National Conference on Artificial Intelligence, Chicago, IL. AAAI Press, Menlo Park (2008)

    Google Scholar 

  45. Srinivasan, A.: The Aleph manual. Technical report, Computing Laboratory, Oxford University (2000)

    Google Scholar 

  46. Wang, J., Domingos, P.: Hybrid Markov logic networks. In: Proceedings of the Twenty-Third National Conference on Artificial Intelligence, Chicago, IL. AAAI Press, Menlo Park (2008)

    Google Scholar 

  47. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)

    Book  MATH  Google Scholar 

  48. Wei, W., Erenrich, J., Selman, B.: Towards efficient sampling: Exploiting random walk strategies. In: Proceedings of the Nineteenth National Conference on Artificial Intelligence, San Jose, CA. AAAI Press, Menlo Park (2004)

    Google Scholar 

  49. Wellman, M., Breese, J.S., Goldman, R.P.: From knowledge bases to decision models. Knowledge Engineering Review 7 (1992)

    Google Scholar 

  50. Wellner, B., McCallum, A., Peng, F., Hay, M.: An integrated, conditional model of information extraction and coreference with application to citation matching. In: Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence, Banff, Canada, pp. 593–601. AUAI Press (2004)

    Google Scholar 

  51. Wu, F., Weld, D.: Automatically refining the Wikipedia infobox ontology. In: 17th International World Wide Web Conference (WWW 2008), Beijing,China (2008)

    Google Scholar 

  52. Yedidia, J.S., Freeman, W.T., Weiss, Y.: Generalized belief propagation. In: Leen, T., Dietterich, T., Tresp, V. (eds.) Advances in Neural Information Processing Systems 13, pp. 689–695. MIT Press, Cambridge (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195-2350, U.S.A.

    Pedro Domingos, Daniel Lowd, Stanley Kok, Hoifung Poon & Parag Singla

  2. Microsoft Research, Redmond, WA 98052

    Matthew Richardson

Authors
  1. Pedro Domingos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Lowd
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stanley Kok
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hoifung Poon
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matthew Richardson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Parag Singla
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. George Mason University, 4400 University Drive, Fairfax, 22030, Virginia, USA

    Paulo Cesar G. da Costa

  2. Dipartimento di Informatica,, Università degli Studi di Bari, Via Orabona, 4, 70125, Bari, Italy

    Claudia d’Amato

  3. Dipartimento di Informatica, Università degli Studi di Bari, Via Orabona, 4, 70125, Bari, Italy

    Nicola Fanizzi

  4. George Mason University, VA 22030, Fairfax, USA

    Kathryn B. Laskey

  5. MIRTE Corporation, M/S H305, 7515 Colshire Drive, VA 22102-7508, McLean, USA

    Kenneth J. Laskey

  6. Computing Laboratory, Oxford University, Wolfson Building, Parks Road, Oxford OX1 3QD, UK

    Thomas Lukasiewicz

  7. University of Bath, BA2 7AY, Bath, UK

    Matthias Nickles

  8. Convera, Inc., 1921 Gallows Road, Suite 200, VA 22182, Vienna, USA

    Michael Pool

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Domingos, P., Lowd, D., Kok, S., Poon, H., Richardson, M., Singla, P. (2008). Just Add Weights: Markov Logic for the Semantic Web. In: da Costa, P.C.G., et al. Uncertainty Reasoning for the Semantic Web I. URSW URSW URSW 2006 2007 2005. Lecture Notes in Computer Science(), vol 5327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89765-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-89765-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89764-4

  • Online ISBN: 978-3-540-89765-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation