Efficient Concept Induction for Description Logics
This work addresses performance bottlenecks in ontology engineering for AI and knowledge representation, offering a more efficient alternative to existing systems in scenarios where speed is critical.
The paper tackled the problem of concept induction in Description Logics, where existing algorithms suffer from performance issues due to frequent reasoner invocations, and presented a new algorithm that reduces these invocations, achieving execution time improvements of up to several orders of magnitude while maintaining reasonably high correctness in coverage.
Concept Induction refers to the problem of creating complex Description Logic class descriptions (i.e., TBox axioms) from instance examples (i.e., ABox data). In this paper we look particularly at the case where both a set of positive and a set of negative instances are given, and complex class expressions are sought under which the positive but not the negative examples fall. Concept induction has found applications in ontology engineering, but existing algorithms have fundamental performance issues in some scenarios, mainly because a high number of invokations of an external Description Logic reasoner is usually required. In this paper we present a new algorithm for this problem which drastically reduces the number of reasoner invokations needed. While this comes at the expense of a more limited traversal of the search space, we show that our approach improves execution times by up to several orders of magnitude, while output correctness, measured in the amount of correct coverage of the input instances, remains reasonably high in many cases. Our approach thus should provide a strong alternative to existing systems, in particular in settings where other systems are prohibitively slow.