Abstract
We consider the problem of predicting instantiated binary relations in a multi-relational setting and exploit both intrarelational correlations and contextual information. For the modular combination we discuss simple heuristics, additive models and an approach that can be motivated from a hierarchical Bayesian perspective. In the concrete examples we consider models that exploit contextual information both from the database and from contextual unstructured information, e.g., information extracted from textual documents describing the involved entities. By using low-rank approximations in the context models, the models perform latent semantic analyses and can generalize across specific terms, i.e., the model might use similar latent representations for semantically related terms. All the approaches we are considering have unique solutions. They can exploit sparse matrix algebra and are thus highly scalable and can easily be generalized to new entities. We evaluate the effectiveness of nonlinear interaction terms and reduce the number of terms by applying feature selection. For the optimization of the context model we use an alternating least squares approach. We experimentally analyze scalability. We validate our approach using two synthetic data sets and using two data sets derived from the Linked Open Data (LOD) cloud.
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Keywords
- Singular Value Decomposition
- Contextual Information
- Context Model
- Combination Scheme
- Latent Semantic Analysis
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.
References
Bell, R.M., Koren, Y., Volinsky, C.: All together now: A perspective on the netflix prize. Chance (2010)
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of Machine Learning Research (2012)
Bloehdorn, S., Sure, Y.: Kernel methods for mining instance data in ontologies. In: ESWC (2007)
Candes, E.J., Recht, B.: Exact matrix completion via convex optimization. Computing Research Repository - CORR (2008)
Cumby, C.M., Roth, D.: On kernel methods for relational learning. In: ICML (2003)
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)
Gärtner, T., Lloyd, J.W., Flach, P.A.: Kernels and distances for structured data. Machine Learning (2004)
Getoor, L., Diehl, C.P.: Link mining: a survey. SIGKDD Explorations (2005)
Getoor, L., Friedman, N., Koller, D., Pfeffer, A., Taskar, B.: Probabilistic relational models. In: Introduction to Statistical Relational Learning (2007)
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)
Järvelin, K., Kekäläinen, J.: IR evaluation methods for retrieving highly relevant documents. In: SIGIR 2000 (2000)
Jiang, X., Huang, Y., Nickel, M., Tresp, V.: Combining Information Extraction, Deductive Reasoning and Machine Learning for Relation Prediction. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 164–178. Springer, Heidelberg (2012)
Jiang, X., Tresp, V., Huang, Y., Nickel, M., Kriegel, H.-P.: Link Prediction in Multi-relational Graphs using Additive Models (submitted, 2012)
Kann, M.G.: Advances in translational bioinformatics: computational approaches for the hunting of disease genes. In: Briefings in Bioinformatics (2010)
Kemp, C., Tenenbaum, J.B., Griffiths, T.L., Yamada, T., Ueda, N.: Learning systems of concepts with an infinite relational model. In: AAAI (2006)
Koller, D., Pfeffer, A.: Probabilistic frame-based systems. In: AAAI (1998)
Landwehr, N., Passerini, A., De Raedt, L., Frasconi, P.: kFOIL: Learning simple relational kernels. In: AAAI (2006)
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)
Muggleton, S.H., Lodhi, H., Amini, A., Sternberg, M.J.E.: Support Vector Inductive Logic Programming. In: Hoffmann, A., Motoda, H., Scheffer, T. (eds.) DS 2005. LNCS (LNAI), vol. 3735, pp. 163–175. Springer, Heidelberg (2005)
Nickel, M., Tresp, V., Kriegel, H.-P.: A three-way model for collective learning on multi-relational data. In: ICML (2011)
Nickel, M., Tresp, V., Kriegel, H.-P.: Factorizing YAGO: scalable machine learning for linked data. In: WWW (2012)
Popescul, A., Ungar, L.H.: Statistical relational learning for link prediction. In: Workshop on Learning Statistical Models from Relational Data (2003)
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)
Richardson, M., Domingos, P.: Markov logic networks. In: Machine Learning (2006)
Takacs, G., Pilaszy, I., Nemeth, B., Tikk, D.: On the gravity recommendation system. In: Proceedings of KDD Cup 2007 (2007)
Taskar, B., Wong, M.F., Abbeel, P., Koller, D.: Link prediction in relational data. In: NIPS (2003)
Vishwanathan, S.V.N., Schraudolph, N., Kondor, R.I., Borgwardt, K.: Graph kernels. Journal of Machine Learning Research - JMLR (2008)
Xu, Z., Kersting, K., Tresp, V.: Multi-relational learning with gaussian processes. In: IJCAI (2009)
Xu, Z., Tresp, V., Yu, K., Kriegel, H.-P.: Infinite hidden relational models. In: UAI (2006)
Yu, K., Chu, W., Yu, S., Tresp, V., Xu, Z.: Stochastic relational models for discriminative link prediction. In: NIPS (2006)
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Jiang, X., Tresp, V., Huang, Y., Nickel, M., Kriegel, HP. (2012). Scalable Relation Prediction Exploiting Both Intrarelational Correlation and Contextual Information. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33460-3_44
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DOI: https://doi.org/10.1007/978-3-642-33460-3_44
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