Fast Approximate A-box Consistency Checking using Machine Learning

author: Heiko Paulheim, Institut für Informatik, University of Mannheim
published: July 28, 2016,   recorded: May 2016,   views: 1614


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


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 ontology in less than 90 minutes, compared to 18 days required by a state of the art ontology reasoner.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: