Compact Representation of Knowledge Bases in Inductive Logic Programming
Issue Title: Inductive Logic Programming (ILP) 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 be...
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| Vydáno v: | Machine learning Ročník 57; číslo 3; s. 305 - 333 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Dordrecht
Springer Nature B.V
01.12.2004
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| Témata: | |
| ISSN: | 0885-6125, 1573-0565 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Issue Title: Inductive Logic Programming (ILP) 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.[PUBLICATION ABSTRACT] |
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| Bibliografie: | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 |
| ISSN: | 0885-6125 1573-0565 |
| DOI: | 10.1023/B:MACH.0000039780.84966.04 |