Abstract
We describe a method for learning functions that can predict the ranking of resources in knowledge bases expressed in Description Logics. The method relies on a kernelized version of the Perceptron Ranking algorithm which is suitable for batch but also online problems settings. The usage of specific kernel functions that encode the similarity between individuals in the context of knowledge bases allows the application of the method to ontologies in the standard representations for the Semantic Web. An extensive experimentation reported in this paper proves the effectiveness of the method at the task of ranking the answers to queries, expressed by class descriptions when applied to real ontologies describing simple and complex domains.
This work was partially funded by the MBLab FAR Project (MIUR DM19410). A short version of this paper has been accepted to ECAI2010 [6].
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Fanizzi, N., d’Amato, C., Esposito, F. (2010). Learning to Rank Individuals in Description Logics Using Kernel Perceptrons. In: Hitzler, P., Lukasiewicz, T. (eds) Web Reasoning and Rule Systems. RR 2010. Lecture Notes in Computer Science, vol 6333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15918-3_14
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DOI: https://doi.org/10.1007/978-3-642-15918-3_14
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