Although neural models have achieved remarkable performance, they still
encounter doubts due to the intransparency. To this end, model prediction
explanation is attracting more and more attentions. However, current methods
rarely incorporate external knowledge and still suffer from three limitations:
(1) Neglecting concept completeness. Merely selecting concepts may not
sufficient for prediction. (2) Lacking concept fusion. Failure to merge
semantically-equivalent concepts. (3) Difficult in manipulating model behavior.
Lack of verification for explanation on original model. To address these
issues, we propose a novel knowledge-aware neuron interpretation framework to
explain model predictions for image scene classification. Specifically, for
concept completeness, we present core concepts of a scene based on knowledge
graph, ConceptNet, to gauge the completeness of concepts. Our method,
incorporating complete concepts, effectively provides better prediction
explanations compared to baselines. Furthermore, for concept fusion, we
introduce a knowledge graph-based method known as Concept Filtering, which
produces over 23% point gain on neuron behaviors for neuron interpretation. At
last, we propose Model Manipulation, which aims to study whether the core
concepts based on ConceptNet could be employed to manipulate model behavior.
The results show that core concepts can effectively improve the performance of
original model by over 26%.

通过引入外部知识和核心概念，我们提出了一种新颖的知识感知神经元解释框架，用于解释图像场景分类模型的预测结果。我们的方法相比基线模型在解释性能上提供了更好的预测解释，并且核心概念能够有效地改善原始模型的性能。