Frontiers and Exact Learning of ELI Queries under DL-Lite Ontologies
This work addresses query learning in description logics, providing foundational algorithmic results for knowledge representation systems, though it is incremental as it builds on existing frameworks.
The paper tackles the problem of learning ELI queries under DL-Lite ontologies, showing that for DL-LiteH, frontiers are polynomial-sized and computable in polynomial time, while for DL-LiteF, they may be infinite unless restricted, enabling polynomial-time exact learnability with membership queries.
We study ELI queries (ELIQs) in the presence of ontologies formulated in the description logic DL-Lite. For the dialect DL-LiteH, we show that ELIQs have a frontier (set of least general generalizations) that is of polynomial size and can be computed in polynomial time. In the dialect DL-LiteF, in contrast, frontiers may be infinite. We identify a natural syntactic restriction that enables the same positive results as for DL-LiteH. We use out results on frontiers to show that ELIQs are learnable in polynomial time in the presence of a DL-LiteH / restricted DL-LiteF ontology in Angluin's framework of exact learning with only membership queries.