Combining Sub-Symbolic and Symbolic Methods for Explainability

TL;DR

This paper proposes a hybrid approach combining sub-symbolic and symbolic methods to improve the explainability of Graph Neural Networks, incorporating domain knowledge and causality for more understandable human-centric explanations.

Contribution

It introduces a novel conceptual framework that integrates symbolic reasoning with sub-symbolic GNNs and proposes a fidelity metric for explanation quality evaluation.

Findings

The method enhances explanation interpretability for non-expert users.

Evaluation on a chemical dataset demonstrates the approach's explanatory value.

The approach shows reliable alignment with GNN decision processes.

Abstract

Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making. A number of sub-symbolic approaches have been developed to provide insights into the GNN decision making process. These are first important steps on the way to explainability, but the generated explanations are often hard to understand for users that are not AI experts. To overcome this problem, we introduce a conceptual approach combining sub-symbolic and symbolic methods for human-centric explanations, that incorporate domain knowledge and causality. We furthermore introduce the notion of fidelity as a metric for evaluating how close the explanation is to the GNN's internal decision making process. The evaluation with a chemical dataset and ontology shows the explanatory value and reliability of our method.