Abstract
Inductive logic programming (ILP) [12] is concerned with the induction of theories from specific examples and background knowledge, using first-order logic representations for all the three ingredients. In its early days some twenty years ago, ILP was perceived as a means for automatic synthesis of logic programs, i.e. Horn clausal theories. Current research views ILP algorithms mainly in the context of machine learning [14] and data mining [1]. ILP has enriched both of the two fieds significantly by providing them with formalisms and algorithms for learning (or ‘mining’) complex pieces of knowledge from non-trivially structured data such as relational databases.
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Železný, F., Kuželka, O. (2010). Taming the Complexity of Inductive Logic Programming. In: van Leeuwen, J., Muscholl, A., Peleg, D., Pokorný, J., Rumpe, B. (eds) SOFSEM 2010: Theory and Practice of Computer Science. SOFSEM 2010. Lecture Notes in Computer Science, vol 5901. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11266-9_11
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