Learning Logical Definitions from Relations
Machine Learning
More efficiency in multiple kernel learning
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KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
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ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
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ILP'07 Proceedings of the 17th international conference on Inductive logic programming
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Data Mining and Knowledge Discovery
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ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
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Proceedings of the 21st ACM international conference on Information and knowledge management
Rank prediction for semantically annotated resources
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Despite the increased awareness that exploiting the large amount of semantic data requires statistics-based inference capabilities, only little work can be found on this direction in the Semantic Web research. On semantic data, supervised approaches, particularly kernelbased Support Vector Machines (SVM), are promising. However, obtaining the right features to be used in kernels is an open problem because the amount of features that can be extracted from the complex structure of semantic data might be very large. Further, combining several kernels can help to deal with efficiency and data sparsity but creates the additional challenge of identifying and joining different subsets of features or kernels, respectively. In this work, we solve these two problems by employing the strategy of dynamic feature construction to compute a hypothesis, representing the relevant features for a set of examples. Then, a composite kernel is obtained from a set of clause kernels derived from components of the hypothesis. The learning of the hypothesis and kernel (s) is performed in an interleaving fashion. Based on experiments on real-world datasets, we show that the resulting relational kernel machine improves the SVM baseline.