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Minage de règles rapide, exact et exhaustif dans de larges bases de connaissances

Abstract : The Semantic Web has quickly become a constellation of large and interconnected entity-centric Knowledge Bases. These KBs contain domain-specific knowledge that can be used for multiple application such as question answering or automatic reasoning. But in order to take full advantage of this data, it is essential to understand the schema and the patterns of the KB. A simple and expressive manner to describe the dependencies in a KB is to use rules. Thus it is crucial to be able to perform rule mining at scale.In this thesis, we introduce novel approaches and optimizations designed to speed up the process of rule mining on large Knowledge Bases. We present two algorithms that implements these optimizations: the AMIE 3 algorithm (the successor of the exact rule mining algorithm AMIE+) and the Pathfinder algorithm, a novel algorithm specialized in mining path rules. These two algorithms are exhaustive with regard to the parameters provided by the user, they compute the quality measures of each rule exactly and efficiently scale to large KB and longer rules.
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Submitted on : Wednesday, May 26, 2021 - 6:35:39 PM
Last modification on : Tuesday, October 19, 2021 - 11:14:15 AM
Long-term archiving on: : Friday, August 27, 2021 - 8:49:05 PM


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  • HAL Id : tel-03237830, version 1



Jonathan Lajus. Minage de règles rapide, exact et exhaustif dans de larges bases de connaissances. Artificial Intelligence [cs.AI]. Institut Polytechnique de Paris, 2021. English. ⟨NNT : 2021IPPAT002⟩. ⟨tel-03237830⟩



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