Towards Scalable Ontological Reasoning using Machine Learning

Daniel Ruffinelli
2017 International Web Rule Symposium  
Ontological reasoning has become a very useful technique for several different applications. However, the use of large knowledge bases has shown that reasoning can be a very resource intensive task which does not scale well. The goal of this work is to explore the development of scalable reasoning approximation methods based on machine learning. Our most important concern is determining in which contexts would such methods be more convenient than currently available approximate reasoning
more » ... ues. For this purpose, we will study the use of currently available approximation approaches, and we will develop new machine learning based methods to compete with them. Our preliminary results already provide evidence that this is possible. However, there are several questions that need to be answered, e.g. what reasoning tasks can be efficiently approximated, or what is the appropriate feature representation for such purposes. Finally, it will be important to determine the degree of completeness and correctness of such methods based on machine learning, and compare them with approximate methods based on standard reasoning.
dblp:conf/ruleml/Ruffinelli17 fatcat:u6dyxywydrarra5qkk52dxb6wi