Semantics-Aware Caching for Concept Learning

TL;DR

This paper introduces a semantics-aware caching method for concept learning in description logics, significantly reducing runtime by efficiently storing and retrieving instances during iterative searches.

Contribution

It presents a subsumption-aware cache that improves the efficiency of concept learning algorithms, applicable to both symbolic and neuro-symbolic reasoners.

Findings

Reduces concept retrieval runtime by up to tenfold.

Effective across multiple datasets and reasoning systems.

Enhances scalability of concept learning processes.

Abstract

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 instances via crisp set operations. Our experiments on 5 datasets with 4 symbolic reasoners, a neuro-symbolic reasoner, and 5 popular pagination policies demonstrate that our cache can reduce the runtime of concept retrieval and concept learning by an order of magnitude while being effective for both symbolic and neuro-symbolic reasoners.