Compact Representation of Knowledge Bases in Inductive Logic Programming |
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Authors: | Struyf Jan Ramon Jan Bruynooghe Maurice Verbaeten Sofie Blockeel Hendrik |
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Affiliation: | (1) Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, B-3001 Leuven, Belgium |
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Abstract: | In many applications of Inductive Logic Programming (ILP), learning occurs from a knowledge base that contains a large number of examples. Storing such a knowledge base may consume a lot of memory. Often, there is a substantial overlap of information between different examples. To reduce memory consumption, we propose a method to represent a knowledge base more compactly. We achieve this by introducing a meta-theory able to build new theories out of other (smaller) theories. In this way, the information associated with an example can be built from the information associated with one or more other examples and redundant storage of shared information is avoided. We also discuss algorithms to construct the information associated with example theories and report on a number of experiments evaluating our method in different problem domains. |
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Keywords: | Inductive Logic Programming efficiency scalability knowledge bases compact representation |
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