Advertisement

Combining Information Extraction, Deductive Reasoning and Machine Learning for Relation Prediction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7295)

Abstract

Three common approaches for deriving or predicting instantiated relations are information extraction, deductive reasoning and machine learning. Information extraction uses subsymbolic unstructured sensory information, e.g. in form of texts or images, and extracts statements using various methods ranging from simple classifiers to the most sophisticated NLP approaches. Deductive reasoning is based on a symbolic representation and derives new statements from logical axioms. Finally, machine learning can both support information extraction by deriving symbolic representations from sensory data, e.g., via classification, and can support deductive reasoning by exploiting regularities in structured data. In this paper we combine all three methods to exploit the available information in a modular way, by which we mean that each approach, i.e., information extraction, deductive reasoning, machine learning, can be optimized independently to be combined in an overall system. We validate our model using data from the YAGO2 ontology, and from Linked Life Data and Bio2RDF, all of which are part of the Linked Open Data (LOD) cloud.

Keywords

Sensory Information Information Extraction Link Prediction Deductive Reasoning Link Open Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download to read the full conference paper text

References

  1. 1.
    Berendt, B., Hotho, A., Stumme, G.: Towards Semantic Web Mining. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 264–278. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Biemann, C.: Ontology learning from text: A survey of methods. LDV Forum 20(2) (2005)Google Scholar
  3. 3.
    Buitelaar, P., Cimiano, P.: Ontology Learning and Population: Bridging the Gap between Text and Knowledge. IOS Press (2008)Google Scholar
  4. 4.
    Cimiano, P.: Ontology Learning and Population from Text: Algorithms, Evaluation and Applications. Springer (2006)Google Scholar
  5. 5.
    Cimiano, P., Hotho, A., Staab, S.: Comparing conceptual, divise and agglomerative clustering for learning taxonomies from text. In: Proceedings of the 16th Eureopean Conference on Artificial Intelligence, ECAI 2004 (2004)Google Scholar
  6. 6.
    Cimiano, P., Staab, S.: Learning concept hierarchies from text with a guided agglomerative clustering algorithm. In: Proceedings of the ICML 2005 Workshop on Learning and Extending Lexical Ontologies with Machine Learning Methods (2005)Google Scholar
  7. 7.
    Cumby, C.M., Roth, D.: On kernel methods for relational learning. In: ICML (2003)Google Scholar
  8. 8.
    D’Amato, C., Fanizzi, N., Esposito, F.: Non-parametric statistical learning methods for inductive classifiers in semantic knowledge bases. In: IEEE International Conference on Semantic Computing - ICSC 2008 (2008)Google Scholar
  9. 9.
    Fanizzi, N., d’Amato, C., Esposito, F.: DL-FOIL Concept Learning in Description Logics. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS (LNAI), vol. 5194, pp. 107–121. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Fensel, D., van Harmelen, F., Andersson, B., Brennan, P., Cunningham, H., Della Valle, E., Fischer, F., Huang, Z., Kiryakov, A., Lee, T.K.-I., Schooler, L., Tresp, V., Wesner, S., Witbrock, M., Zhong, N.: Towards larkc: A platform for web-scale reasoning. In: ICSC, pp. 524–529 (2008)Google Scholar
  11. 11.
    Gärtner, T., Lloyd, J.W., Flach, P.A.: Kernels and distances for structured data. Machine Learning 57(3) (2004)Google Scholar
  12. 12.
    Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis, 2nd edn. Chapman and Hall/CRC Texts in Statistical Science (2003)Google Scholar
  13. 13.
    Getoor, L., Diehl, C.P.: Link mining: a survey. SIGKDD Explorations (2005)Google Scholar
  14. 14.
    Grobelnik, M., Mladenic, D.: Knowledge discovery for ontology construction. In: Davies, J., Studer, R., Warren, P. (eds.) Semantic Web Technologies. Wiley (2006)Google Scholar
  15. 15.
    Huang, Y., Tresp, V., Bundschus, M., Rettinger, A., Kriegel, H.-P.: Multivariate Prediction for Learning on the Semantic Web. In: Frasconi, P., Lisi, F.A. (eds.) ILP 2010. LNCS, vol. 6489, pp. 92–104. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Jakob, N., Müller, M.-C., Weber, S.H., Gurevych, I.: Beyond the stars: Exploiting free-text user reviews for improving the accuracy of movie recommendations. In: TSA 2009 - 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement (2009)Google Scholar
  17. 17.
    Jarvelin, K., Kekalainen, J.: IR evaluation methods for retrieving highly relevant documents. In: SIGIR 2000 (2000)Google Scholar
  18. 18.
    Kann, M.G.: Advances in translational bioinformatics: computational approaches for the hunting of disease genes. Briefing in Bioinformatics 11 (2010)Google Scholar
  19. 19.
    Kiefer, C., Bernstein, A., Locher, A.: Adding Data Mining Support to SPARQL via Statistical Relational Learning Methods. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 478–492. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  20. 20.
    Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML (2001)Google Scholar
  21. 21.
    Lehmann, J.: Dl-learner: Learning concepts in description logics. JMLR (2009)Google Scholar
  22. 22.
    Lisi, F.A., Esposito, F.: An ilp perspective on the semantic web. In: Semantic Web Applications and perspectives (2005)Google Scholar
  23. 23.
    Maedche, A., Staab, S.: Semi-automatic engineering of ontologies from text. In: Proceedings of the 12th International Conference on Software Engineering and Knowledge Engineering (2000)Google Scholar
  24. 24.
    Maedche, A., Staab, S.: Ontology Learning. In: Handbook on Ontologies 2004. Springer (2004)Google Scholar
  25. 25.
    Mika, P.: Social Networks and the Semantic Web. Springer (2007)Google Scholar
  26. 26.
    Paaß, G., Kindermann, J., Leopold, E.: Learning prototype ontologies by hierachical latent semantic analysis. In: Knowledge Discovery and Ontologies (2004)Google Scholar
  27. 27.
    Popescul, A., Ungar, L.H.: Statistical relational learning for link prediction. In: Workshop on Learning Statistical Models from Relational Data (2003)Google Scholar
  28. 28.
    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. LNCS, vol. 5782, pp. 286–301. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  29. 29.
    Sarawagi, S.: Information extraction. Foundations and Trends in Databases 1(3), 261–377 (2008)CrossRefGoogle Scholar
  30. 30.
    Sowa, J.F.: Ontology, metadata, and semiotics. In: International Conference on Computational Science (2000)Google Scholar
  31. 31.
    Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B 61, 611–622 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  32. 32.
    Tresp, V., Yu, K.: Learning with dependencies between several response variables. In: Tutorial at ICML 2009 (2009)Google Scholar
  33. 33.
    Vishwanathan, S.V.N., Schraudolph, N., Kondor, R.I., Borgwardt, K.: Graph kernels. Journal of Machine Learning Research - JMLR (2008)Google Scholar
  34. 34.
    Völker, J., Haase, P., Hitzler, P.: Learning expressive ontologies. In: Buitelaar, P., Cimiano, P. (eds.) Ontology Learning and Population: Bridging the Gap between Text and Knowledge. IOS Press (2008)Google Scholar
  35. 35.
    Xu, Z., Kersting, K., Tresp, V.: Multi-relational learning with gaussian processes. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence, IJCAI 2009 (2009)Google Scholar
  36. 36.
    Yu, K., Chu, W., Yu, S., Tresp, V., Xu, Z.: Stochastic relational models for discriminative link prediction. In: Advances in Neural Information Processing Systems, NIPS 2006 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Siemens AG, Corporate TechnologyMunichGermany
  2. 2.Ludwig Maximilian University of MunichMunichGermany

Personalised recommendations

Cite paper