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- 5 creator heiko-paulheim.
- 5 creator heiner-stuckenschmidt.
- 5 type InProceedings.
- 5 label "Efficient Approximation of Ontology Reasoning by Machine Learning".
- 5 sameAs 5.
- 5 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 the results delivered by a reasoner by observing an actual reasoner and training a machine learning model which approximates the behavior of that reasoner. 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 checking the consistency of all relational assertions in DBpedia in less than 90 minutes, compared to 15 days required by a state of the art ontology reasoner. ".
- 5 hasAuthorList authorList.
- 5 isPartOf proceedings.
- 5 title "Efficient Approximation of Ontology Reasoning by Machine Learning".