Abstract
We propose a learning approach for integrating formal knowledge into statistical inference by exploiting ontologies as a semantically rich and fully formal representation of prior knowledge. The logical constraints deduced from ontologies can be utilized to enhance and control the learning task by enforcing description logic satisfiability in a latent multi-relational graphical model. To demonstrate the feasibility of our approach we provide experiments using real world social network data in form of a \(\mathcal{SHOIN}(D)\) ontology. The results illustrate two main practical advancements: First, entities and entity relationships can be analyzed via the latent model structure. Second, enforcing the ontological constraints guarantees that the learned model does not predict inconsistent relations. In our experiments, this leads to an improved predictive performance.
References
Horrocks, I., Patel-Schneider, P.F.: Reducing owl entailment to description logic satisfiability. Journal of Web Semantics, 17–29 (2003)
Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. The MIT Press, Cambridge (2007)
Xu, Z., Tresp, V., Yu, K., Kriegel, H.P.: Infinite hidden relational models. In: Proceedings of the 22nd International Conference on Uncertainity in Artificial Intelligence, UAI 2006 (2006)
Kemp, C., Tenenbaum, J.B., Griffiths, T.L., Yamada, T., Ueda, N.: Learning systems of concepts with an infinite relational model. In: Proc. 21st Conference on Artificial Intelligence (2006)
Ishwaran, H., James, L.: Gibbs sampling methods for stick breaking priors. Journal of the American Statistical Association 96(453), 161–173 (2001)
Lisi, F.A., Esposito, F.: On Ontologies as Prior Conceptual Knowledge in Inductive Logic Programming. In: ECML PKDD 2008 Workshop: Prior Conceptual Knowledge in Machine Learning and Knowledge Discovery PriCKL 2007 (2007)
Raedt, L.D., Kersting, K.: Probabilistic logic learning. SIGKDD Explor. Newsl. 5(1), 31–48 (2003)
Carbonetto, P., Kisynski, J., de Freitas, N., Poole, D.: Nonparametric bayesian logic. In: Proc. 21st UAI (2005)
Reckow, S., Tresp, V.: Integrating Ontological Prior Knowledge into Relational Learning. In: NIPS 2008 Workshop: Structured Input - Structured Output (2008)
Richardson, M., Domingos, P.: Markov logic networks. Journal of Machine Learning Research 62, 107–136 (2006)
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)
Fanizzi, N., d’Amato, C., Esposito, F.: Induction of classifiers through non-parametric methods for approximate classification and retrieval with ontologies. International Journal of Semantic Computing 2(3), 403–423 (2008)
Fanizzi, N., D’Amato, C., Esposito, F.: A multi-relational hierarchical clustering method for datalog knowledge bases. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds.) Foundations of Intelligent Systems. LNCS (LNAI), vol. 4994, pp. 137–142. Springer, Heidelberg (2008)
Xu, Z., Tresp, V., Rettinger, A., Kersting, K.: Social network mining with nonparametric relational models. In: Advances in Social Network Mining and Analysis - the Second SNA-KDD Workshop at KDD 2008 (2008)
Davidson, I., Ravi, S.S.: The complexity of non-hierarchical clustering with instance and cluster level constraints. Data Min. Knowl. Discov. 14(1), 25–61 (2007)
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Rettinger, A., Nickles, M., Tresp, V. (2009). Statistical Relational Learning with Formal Ontologies. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2009. Lecture Notes in Computer Science(), vol 5782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04174-7_19
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DOI: https://doi.org/10.1007/978-3-642-04174-7_19
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