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Graph-Based Regularization for Transductive Class-Membership Prediction

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Uncertainty Reasoning for the Semantic Web III (URSW 2012, URSW 2011, URSW 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8816))

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Abstract

Considering the increasing availability of structured machine processable knowledge in the context of the Semantic Web, only relying on purely deductive inference may be limiting. This work proposes a new method for similarity-based class-membership prediction in Description Logic knowledge bases. The underlying idea is based on the concept of propagating class-membership information among similar individuals; it is non-parametric in nature and characterized by interesting complexity properties, making it a potential candidate for large-scale transductive inference. We also evaluate its effectiveness with respect to other approaches based on inductive inference in SW literature.

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Notes

  1. 1.

    A matrix \(\mathbf {A}\) is SDD iff \(\mathbf {A}\) is symmetric (i.e. \(\mathbf {A}= \mathbf {A}^{T}\)) and \(\forall i : \mathbf {A}_{ii} \ge \sum _{i \ne j} |\mathbf {A}_{ij}|\).

  2. 2.

    From TONES Repository: http://owl.cs.manchester.ac.uk/repository/.

  3. 3.

    Using the methods available at http://lacam.di.uniba.it/~nico/research/ontologymining.html.

  4. 4.

    Pellet v2.3.0 – http://clarkparsia.com/pellet/.

  5. 5.

    http://www.aifb.kit.edu/web/Wissensmanagement/Portal, as of 21 Feb. 2012.

References

  1. Alexandrescu, A., Kirchhoff, K.: Data-driven graph construction for semi-supervised graph-based learning in NLP. In: Sidner, C., et al. (eds.) HLT-NAACL, pp. 204–211. The Association for Computational Linguistics (2007)

    Google Scholar 

  2. Belkin, M., Matveeva, I., Niyogi, P.: Regularization and semi-supervised learning on large graphs. In: Shawe-Taylor, J., Singer, Y. (eds.) COLT 2004. LNCS (LNAI), vol. 3120, pp. 624–638. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  3. Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006)

    MATH  MathSciNet  Google Scholar 

  4. Bloehdorn, S., Sure, Y.: Kernel methods for mining instance data in ontologies. In: Aberer, K., et al. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 58–71. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  5. Borgida, A., Walsh, T., Hirsh, H.: Towards measuring similarity in description logics. In: Horrocks, I., et al. (eds.) Description Logics. CEUR Workshop Proceedings, vol. 147. CEUR-WS.org (2005)

    Google Scholar 

  6. Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. MIT Press, Cambridge (2006)

    Google Scholar 

  7. Cohen, M.B., Kyng, R., Miller, G.L., Pachocki, J.W., Peng, R., Rao, A., Xu, S.C.: Solving sdd linear systems in nearly \(m{\rm {log}}^{\text{1/2 }}n\) time. In: Shmoys [29], pp. 343–352

    Google Scholar 

  8. d’Amato, C., Fanizzi, N., Esposito, F.: Query answering and ontology population: an inductive approach. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 288–302. Springer, Heidelberg (2008)

    Google Scholar 

  9. d’Amato, C., Fanizzi, N., Esposito, F.: A semantic similarity measure for expressive description logics. CoRR abs/0911.5043 (2009)

    Google Scholar 

  10. d’Amato, C., Staab, S., Fanizzi, N.: On the influence of description logics ontologies on conceptual similarity. In: Euzenat, J., Gangemi, A. (eds.) EKAW 2008. LNCS (LNAI), vol. 5268, pp. 48–63. Springer, Heidelberg (2008)

    Google Scholar 

  11. Fanizzi, N., d’Amato, C.: Inductive concept retrieval and query answering with semantic knowledge bases through kernel methods. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part I. LNCS (LNAI), vol. 4692, pp. 148–155. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  12. Fanizzi, N., d’Amato, C., Esposito, F.: ReduCE: a reduced coulomb energy network method for approximate classification. In: Aroyo, L., et al. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 323–337. Springer, Heidelberg (2009)

    Google Scholar 

  13. Fanizzi, N., d’Amato, C., Esposito, F.: Statistical learning for inductive query answering on OWL ontologies. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 195–212. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  14. Fanizzi, N., d’Amato, C., Esposito, F.: Towards learning to rank in description logics. In: Coelho, H., et al. (eds.) ECAI. Frontiers in Artificial Intelligence and Applications, vol. 215, pp. 985–986. IOS Press (2010)

    Google Scholar 

  15. Gurobi Optimization, Inc. Gurobi optimizer reference manual (2012)

    Google Scholar 

  16. Hu, B., Dasmahapatra, S., Lewis, P.: Semantic metrics. Int. J. Metadata Semant. Ontologies 2(4), 242–258 (2007)

    Article  Google Scholar 

  17. Janowicz, K., Wilkes, M.: SIM-DL\(_{A}\): a novel semantic similarity measure for description logics reducing inter-concept to inter-instance similarity. In: Aroyo, L., et al. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 353–367. Springer, Heidelberg (2009)

    Google Scholar 

  18. Kapoor, A., Qi, Y.A., Ahn, H., Picard, R.W.: Hyperparameter and kernel learning for graph based semi-supervised classification. In: Advances in Neural Information Processing Systems 18, Neural Information Processing Systems, NIPS 2005, Vancouver, British Columbia, Canada, 5–8 December (2005)

    Google Scholar 

  19. Kersting, K., Raedt, L.D.: Bayesian logic programming: theory and tool. In: Getoor, L., Taskar, B. (eds.) An Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)

    Google Scholar 

  20. Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)

    Google Scholar 

  21. Lasserre, J., Bishop, C.M.: Generative or discriminative? Getting the best of both worlds. Bayesian Stat. 8, 3–24 (2007)

    MathSciNet  Google Scholar 

  22. Lösch, U., Bloehdorn, S., Rettinger, A.: Graph kernels for RDF data. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 134–148. Springer, Heidelberg (2012)

    Google Scholar 

  23. Ochoa-Luna, J.E., Cozman, F.G.: An algorithm for learning with probabilistic description logics. In: Bobillo, F., et al. (eds.) URSW, pp. 63–74 (2009)

    Google Scholar 

  24. Peng, R., Spielman, D.A.: An efficient parallel solver for sdd linear systems. In: Shmoys [29], pp. 333–342

    Google Scholar 

  25. Rettinger, A., Lösch, U., Tresp, V., d’Amato, C., Fanizzi, N.: Mining the semantic web - statistical learning for next generation knowledge bases. Data Mining and Knowledge Discovery - Special Issue on Web Mining (2012)

    Google Scholar 

  26. Rettinger, A., Nickles, M., Tresp, V.: Statistical relational learning with formal ontologies. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part II. LNCS, vol. 5782, pp. 286–301. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  27. Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, New York (2004)

    Book  Google Scholar 

  28. Shervashidze, N., Schweitzer, P., van Leeuwen, E.J., Mehlhorn, K., Borgwardt, K.M.: Weisfeiler-Lehman graph kernels. J. Mach. Learn. Res. 12, 2539–2561 (2011)

    MATH  MathSciNet  Google Scholar 

  29. Shmoys, D.B. (ed.): Symposium on Theory of Computing, STOC 2014. ACM, New York, 31 May–03 June 2014

    Google Scholar 

  30. Spielman, D.A.: Algorithms, graph theory, and linear equations in Laplacian matrices. In: Proceedings of the International Congress of Mathematicians 2010 (ICM 2010), pp. 2698–2722 (2010)

    Google Scholar 

  31. Vapnik, V.N.: Statistical Learning Theory, 1st edn. Wiley, New York (1998)

    MATH  Google Scholar 

  32. Zhang, X., Lee, W.S.: Hyperparameter learning for graph based semi-supervised learning algorithms. In: Schölkopf, B., et al. (eds.) Advances in Neural Information Processing Systems 19, Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 4–7, 2006, pp. 1585–1592. MIT Press (2006)

    Google Scholar 

  33. Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: Thrun, S., et al. (eds.) Advances in Neural Information Processing Systems 16, Neural Information Processing Systems, NIPS 2003, Vancouver and Whistler, British Columbia, Canada, 8–13 December 2003. MIT Press (2003)

    Google Scholar 

  34. Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation. Technical report, CMU CALD tech report CMU-CALD-02 (2002)

    Google Scholar 

  35. Zhu, X.: Semi-supervised learning literature survey. Technical report, 1530, Computer Sciences, University of Wisconsin-Madison (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. LACAM Laboratory – Dipartimento di Informatica, Università degli Studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy

    Pasquale Minervini, Claudia d’Amato, Nicola Fanizzi & Floriana Esposito

Authors
  1. Pasquale Minervini
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  2. Claudia d’Amato
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  3. Nicola Fanizzi
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  4. Floriana Esposito
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Corresponding author

Correspondence to Pasquale Minervini .

Editor information

Editors and Affiliations

  1. University of Zaragoza, Zaragoza, Spain

    Fernando Bobillo

  2. Universidade de Brasília, Brasília, Brazil

    Rommel N. Carvalho

  3. George Mason University, Fairfax, Virginia, USA

    Paulo C.G. Costa

  4. Università degli Studi di Bari, Bari, Italy

    Claudia d'Amato

  5. Università degli Studi di Bari, Bari, Italy

    Nicola Fanizzi

  6. George Mason University, Fairfax, Virginia, USA

    Kathryn B. Laskey

  7. MITRE Corporation, McLean, Virginia, USA

    Kenneth J. Laskey

  8. University of Oxford, Oxford, United Kingdom

    Thomas Lukasiewicz

  9. National University of Ireland, Galway, Ireland

    Matthias Nickles

  10. Goldman Sachs, Washington, District of Columbia, USA

    Michael Pool

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Minervini, P., d’Amato, C., Fanizzi, N., Esposito, F. (2014). Graph-Based Regularization for Transductive Class-Membership Prediction. In: Bobillo, F., et al. Uncertainty Reasoning for the Semantic Web III. URSW URSW URSW 2012 2011 2013. Lecture Notes in Computer Science(), vol 8816. Springer, Cham. https://doi.org/10.1007/978-3-319-13413-0_11

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  • DOI: https://doi.org/10.1007/978-3-319-13413-0_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13412-3

  • Online ISBN: 978-3-319-13413-0

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