Semantics-Aware Caching for Concept Learning

2026-03-06

AI Summary

Concept learning in complex problems often suffers from inefficiency due to frequent instance retrieval. This work proposes a semantic-aware caching mechanism that introduces, for the first time, a subsumption-aware mapping structure to efficiently associate concepts with their corresponding instance sets, thereby significantly accelerating concept retrieval and learning. The approach provides unified support for both symbolic and neuro-symbolic reasoners, enabling cross-paradigm cache reuse. By integrating description logic knowledge bases, subsumption-based inference, set operations, and a paging-based caching strategy, the method achieves an order-of-magnitude reduction in runtime across five benchmark datasets and multiple reasoning systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

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.

Problem

• concept learning
• instance retrieval
• runtime efficiency
• description logics
• semantic caching

Innovation

• semantics-aware caching
• concept learning
• subsumption-aware
• instance retrieval
• neuro-symbolic reasoning