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Web Stream Reasoning Using Probabilistic Answer Set Programming

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Book cover Web Reasoning and Rule Systems (RR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8741))

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Abstract

We propose a framework for reasoning about dynamic Web data, based on probabilistic Answer Set Programming (ASP). Our approach, which is prototypically implemented, allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities, and for learning of such weights from examples (parameter estimation). Knowledge as well as examples can be provided incrementally in the form of RDF data streams. Optionally, stream data can be configured to decay over time. With its hybrid combination of various contemporary AI techniques, our framework aims at prevalent challenges in relation to data streams and Linked Data, such as inconsistencies, noisy data, and probabilistic processing rules.

This research is sponsored by Science Foundation Ireland (SFI) grant No. SFI/12/RC/2289

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References

  1. Baral, C., Gelfond, M., Rushton, N.: Probabilistic reasoning with answer sets. Theory Pract. Log. Program. 9(1), 57–144 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  2. Barbieri, D.F., Braga, D., Ceri, S., Valle, E.D., Huang, Y., Tresp, V., Rettinger, A., Wermser, H.: Deductive and inductive stream reasoning for semantic social media analytics. IEEE Intelligent Systems, 32–41 (2010)

    Google Scholar 

  3. Barzilai, J., Borwein, J.M.: Two point step size gradient methods. IMA J. Numer. Anal. (1988)

    Google Scholar 

  4. Corapi, D., Sykes, D., Inoue, K., Russo, A.: Probabilistic rule learning in nonmonotonic domains. In: Leite, J., Torroni, P., Ågotnes, T., Boella, G., van der Torre, L. (eds.) CLIMA XII 2011. LNCS, vol. 6814, pp. 243–258. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  5. Cussens, J.: Parameter estimation in stochastic logic programs. In: Machine Learning (2000)

    Google Scholar 

  6. Do, T.M., Loke, S.W., Liu, F.: Answer set programming for stream reasoning. In: Butz, C., Lingras, P. (eds.) Canadian AI 2011. LNCS, vol. 6657, pp. 104–109. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  7. Getoor, L.: Learning probabilistic relational models. In: Choueiry, B.Y., Walsh, T. (eds.) SARA 2000. LNCS (LNAI), vol. 1864, pp. 322–1309. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  8. Gebser, M., Grote, T., Kaminski, R., Obermeier, P., Sabuncu, O., Schaub, T.: Answer set programming for stream reasoning. CoRR abs/1301.1392 (2013)

    Google Scholar 

  9. Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proc. of the 5th Int’l Conference on Logic Programming, vol. 161 (1988)

    Google Scholar 

  10. Kersting, K., Raedt, L.D.: Bayesian logic programs. In: Proceedings of the 10th International Conference on Inductive Logic Programming (2000)

    Google Scholar 

  11. Laskey, K.B., Costa, P.C.: Of klingons and starships: Bayesian logic for the 23rd century. In: Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (2005)

    Google Scholar 

  12. Le-Phuoc, D., Dao-Tran, M., Xavier Parreira, J., Hauswirth, M.: A native and adaptive approach for unified processing of linked streams and linked data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 370–388. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  13. Lee, J., Palla, R.: System f2lp – computing answer sets of first-order formulas. In: Erdem, E., Lin, F., Schaub, T. (eds.) LPNMR 2009. LNCS, vol. 5753, pp. 515–521. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  14. Margara, A., Urbani, J., van Harmelen, F., Bal, H.: Streaming the web: Reasoning over dynamic data. In: Web Semantics: Science, Services and Agents on the World Wide Web (2014)

    Google Scholar 

  15. Mileo, A., Abdelrahman, A., Policarpio, S., Hauswirth, M.: StreamRule: A nonmonotonic stream reasoning system for the semantic web. In: Faber, W., Lembo, D. (eds.) RR 2013. LNCS, vol. 7994, pp. 247–252. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  16. Mileo, A., Nickles, M.: Probabilistic inductive answer set programming by model sampling and counting. In: 1st Int’l Workshop on Learning and Nonmonotonic Reasoning (2013)

    Google Scholar 

  17. Muggleton, S.: Learning stochastic logic programs. Electron. Trans. Artif. Intell. 4(B), 141–153 (2000)

    MathSciNet  Google Scholar 

  18. Ng, R.T., Subrahmanian, V.S.: Stable semantics for probabilistic deductive databases. Inf. Comput. 110(1), 42–83 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  19. Nickles, M., Mileo, A.: Probabilistic inductive logic programming based on answer set programming. In: Procs. 15th International Workshop on Non-Monotonic Reasoning (2014)

    Google Scholar 

  20. Poole, D.: The independent choice logic for modelling multiple agents under uncertainty. Artificial Intelligence 94, 7–56 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  21. De Raedt, L., Kersting, K.: Probabilistic inductive logic programming. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.) Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911, pp. 1–27. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  22. Raedt, L.D., Kimmig, A., Toivonen, H.: Problog: A probabilistic prolog and its application in link discovery. In: IJCAI, pp. 2462–2467 (2007)

    Google Scholar 

  23. Ré, C., Letchner, J., Balazinksa, M., Suciu, D.: Event queries on correlated probabilistic streams. In: Procs. 2008 SIGMOD International Conference on Management of Data (2008)

    Google Scholar 

  24. Richardson, M., Domingos, P.: Markov logic networks. Mach. Learning 62(1-2) (2006)

    Google Scholar 

  25. Saad, E., Pontelli, E.: Hybrid probabilistic logic programming with non-monotoic negation. In: Twenty First International Conference on Logic Programming (2005)

    Google Scholar 

  26. Sato, T., Kameya, Y.: Prism: a language for symbolic-statistical modeling. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI 1997) (1997)

    Google Scholar 

  27. Thimm, M., Kern-Isberner, G.: On probabilistic inference in relational conditional logics. Logic Journal of the IGPL 20(5), 872–908 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  28. Valle, E.D., Ceri, S., van Harmelen, F., Fensel, D.: It’s a streaming world! reasoning upon rapidly changing information. IEEE Intelligent Systems 24(6), 83–89 (2009)

    Article  Google Scholar 

  29. Wasserkrug, S., Gal, A., Etzion, O., Turchin, Y.: Complex event processing over uncertain data. In: Procs. 2nd International Conference on Distributed Event-based Systems (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. INSIGHT Centre for Data Analytics, National University of Ireland, Galway, Ireland

    Matthias Nickles & Alessandra Mileo

  2. Department of Information Technology, National University of Ireland, Galway, Ireland

    Matthias Nickles

Authors
  1. Matthias Nickles
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  2. Alessandra Mileo
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Systems, Birkbeck, University of London, Malet Street,, WC1E 7HX, London, UK

    Roman Kontchakov

  2. LIRMM, 161, rue ADA, Montpellier Cedex 5, 34395, France

    Marie-Laure Mugnier

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Nickles, M., Mileo, A. (2014). Web Stream Reasoning Using Probabilistic Answer Set Programming. In: Kontchakov, R., Mugnier, ML. (eds) Web Reasoning and Rule Systems. RR 2014. Lecture Notes in Computer Science, vol 8741. Springer, Cham. https://doi.org/10.1007/978-3-319-11113-1_16

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  • DOI: https://doi.org/10.1007/978-3-319-11113-1_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11112-4

  • Online ISBN: 978-3-319-11113-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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