Fast Approximate A-Box Consistency Checking Using Machine Learning

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)

Abstract

Ontology reasoning is typically a computationally intensive operation. While soundness and completeness of results is required in some use cases, for many others, a sensible trade-off between computation efforts and correctness of results makes more sense. In this paper, we show that it is possible to approximate a central task in reasoning, i.e., A-box consistency checking, by training a machine learning model which approximates the behavior of that reasoner for a specific ontology. On four different datasets, we show that such learned models constantly achieve an accuracy above 95 % at less than 2 % of the runtime of a reasoner, using a decision tree with no more than 20 inner nodes. For example, this allows for validating 293M Microdata documents against the schema.org ontology in less than 90 min, compared to 18 days required by a state of the art ontology reasoner.

Keywords

Approximate ontology reasoning Machine learning 
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Notes

Acknowledgements

The authors would like to thank Aldo Gangemi for providing the DOLCE mappings for DBpedia and YAGO, and Robert Meusel for his assistance in providing suitable samples from the WebDataCommons corpora. This work has been supported by RapidMiner in the course of the RapidMiner Academia program.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Data and Web Science GroupUniversity of MannheimMannheimGermany

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