基于神经符号融合的知识图谱推理

Abstract

This paper introduces a novel approach to knowledge graph reasoning by fusing neural networks with symbolic reasoning techniques. Traditional knowledge graph reasoning methods often rely heavily on probabilistic models, leading to limited interpretability and potentially sacrificing accuracy. Our proposed framework addresses these limitations by integrating the strengths of both approaches. Specifically, we represent relationships and rules within a knowledge graph in a symbolic format, allowing for deductive reasoning. Simultaneously, neural networks are employed for relation prediction and learning complex patterns that may not be easily captured by traditional symbolic rules. The outputs of the neural network are then integrated back into the knowledge graph, enhancing the reasoning process. We demonstrate the effectiveness of this neuro-symbolic fusion in improving both the accuracy and interpretability of knowledge graph inferences. The core claim of this work is the construction of a system capable of merging neural networks and symbolic reasoning for knowledge graph reasoning, ultimately boosting accuracy and interpretability. The central mechanism involves translating knowledge graph relationships and rules into symbolic representations, leveraging neural networks for relation prediction and inference, and then consolidating results back into the knowledge graph. This approach differentiates itself from existing methods that predominantly utilize probabilistic models, offering a more transparent and potentially more accurate solution.