Semantics-Aware Caching for Concept Learning

TL;DR

This cache is essentially a subsumption-aware map that links concepts to a set of instances via crisp set operations that 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.

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.

Figures (4)

• Figure 1: Run time performance vs cache size for five reasoners on large datasets. The dotted horizontal lines represent the baseline performance of each reasoner without caching.
• Figure 2: Run time performance vs. cache size for each reasoner on the Family dataset. The gray dotted line indicates the runtime of the reasoner without our cache.
• Figure 3: Hit score performance vs cache size for each reasoner on the Family dataset.
• Figure 4: Average runtime of concept learners using no cache, the semantic cache, and the non-semantic cache baseline across different datasets.