A Statistical Approach to Incremental Induction of First-Order Hierarchical Knowledge Bases

Knowledge bases play an important role in many forms of artificial intelligence research. A simple approach to producing such knowledge is as a database of ground literals. However, this method is neither compact nor computationally tractable for learning or performance systems to use. In this paper...

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Bibliographic Details
Published inInductive Logic Programming Vol. 5194; pp. 279 - 296
Main Authors Zelezny, Filip, Lavrac, Nada
Format Book Chapter
LanguageEnglish
Published Germany Springer Berlin / Heidelberg 2008
Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Ground Fact
Horn Clause
Hypothesis Complexity
Inductive Logic Programming
Recursive Rule
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Summary:Knowledge bases play an important role in many forms of artificial intelligence research. A simple approach to producing such knowledge is as a database of ground literals. However, this method is neither compact nor computationally tractable for learning or performance systems to use. In this paper, we present a statistical method for incremental learning of a hierarchically structured, first-order knowledge base. Our approach uses both rules and ground facts to construct succinct rules that generalize the ground literals. We demonstrate that our approach is computationally efficient and scales well to domains with many relations.
ISBN:9783540859277
3540859276
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-540-85928-4_22