Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies
This addresses the challenge of efficiently learning logical structures in knowledge representation for AI systems, though it is incremental as it builds on existing active learning frameworks with specific restrictions.
The paper tackles the problem of learning concepts and conjunctive queries under ELr-ontologies using active learning, showing that EL-concepts, symmetry-free ELI-concepts, and chordal, symmetry-free CQs of bounded arity can be learned in polynomial time, while EL-concepts are not polynomial query learnable with ELI-ontologies.
We consider the problem to learn a concept or a query in the presence of an ontology formulated in the description logic ELr, in Angluin's framework of active learning that allows the learning algorithm to interactively query an oracle (such as a domain expert). We show that the following can be learned in polynomial time: (1) EL-concepts, (2) symmetry-free ELI-concepts, and (3) conjunctive queries (CQs) that are chordal, symmetry-free, and of bounded arity. In all cases, the learner can pose to the oracle membership queries based on ABoxes and equivalence queries that ask whether a given concept/query from the considered class is equivalent to the target. The restriction to bounded arity in (3) can be removed when we admit unrestricted CQs in equivalence queries. We also show that EL-concepts are not polynomial query learnable in the presence of ELI-ontologies.