Semantics-Aware Caching for Concept Learning
Concept learning is a form of supervised machine learning that operates on knowledge bases in description logics. State-of-the-art concept learners often rely on an iterative search through a countably infinite concept space. In each iteration, they retrieve instances of candidate solutions to select the best concept for the next iteration. While simple learning problems might require a few dozen instance retrieval calls to find a fitting solution, complex learning problems might necessitate thousands of calls. We alleviate the resulting runtime challenge by presenting a semantics-aware caching approach. Our cache is essentially a subsumption-aware map that links concepts to a set of inst...
| Authors | Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo |
|---|---|
| Primary category | stat.ML |
| Published | 6 March 2026 |
| Last updated at source | 12 March 2026 |
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