Neural Reasoning for Robust Instance Retrieval in $\mathcal{SHOIQ}$
This addresses the challenge of robust instance retrieval in description logic for knowledge base applications, representing an incremental improvement over existing neuro-symbolic methods.
The paper tackles the problem of deploying concept learning on real-world knowledge bases by introducing a neural reasoner called EBR, which uses embeddings to approximate symbolic reasoning and shows robustness against missing and erroneous data, outperforming state-of-the-art reasoners in experiments.
Concept learning exploits background knowledge in the form of description logic axioms to learn explainable classification models from knowledge bases. Despite recent breakthroughs in neuro-symbolic concept learning, most approaches still cannot be deployed on real-world knowledge bases. This is due to their use of description logic reasoners, which are not robust against inconsistencies nor erroneous data. We address this challenge by presenting a novel neural reasoner dubbed EBR. Our reasoner relies on embeddings to approximate the results of a symbolic reasoner. We show that EBR solely requires retrieving instances for atomic concepts and existential restrictions to retrieve or approximate the set of instances of any concept in the description logic $\mathcal{SHOIQ}$. In our experiments, we compare EBR with state-of-the-art reasoners. Our results suggest that EBR is robust against missing and erroneous data in contrast to existing reasoners.