Abstract
A clustering method is presented which can be applied to relational knowledge bases (e.g. Datalog deductive databases). It can be used to discover interesting groups of resources through their (semantic) annotations expressed in the standard logic programming languages. The method exploits an effective and language-independent semi-distance measure for individuals., that is based on the resource semantics w.r.t. a number of dimensions corresponding to a committee of features represented by a group of concept descriptions (discriminating features). The algorithm is a fusion of the classic Bisecting k-Means with approaches based on medoids that are typically applied to relational representations. We discuss its complexity and potential applications to several tasks.
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Fanizzi, N., d’Amato, C., Esposito, F. (2008). 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. ISMIS 2008. Lecture Notes in Computer Science(), vol 4994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68123-6_15
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DOI: https://doi.org/10.1007/978-3-540-68123-6_15
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