Neuro-Symbolic Reasoning with Attention-Based Knowledge Graphs

Abstract

This paper proposes a novel approach to neuro-symbolic reasoning that addresses the challenges of seamless integration between neural networks and symbolic knowledge graphs. The core idea is to introduce an attention mechanism within the neural network architecture, allowing it to dynamically select and prioritize relevant information from the knowledge graph during the reasoning process. This dynamic retrieval and contextualization significantly enhance the robustness and flexibility of the system. We represent a knowledge graph as a set of nodes (s1, s2, ..., sN) and edges (eij, where i and j are indices representing nodes), and the attention mechanism learns weights (αij) associated with each edge, reflecting its importance to a given query (q). The output of the system is then a weighted combination of the node representations, determined by these attention weights. This approach offers a practical solution for overcoming the limitations of traditional neuro-symbolic methods and provides a foundation for more sophisticated reasoning capabilities. The system's architecture is defined by the following key components: a neural network (NN) represented as a function f(x, g), where 'x' is the input and 'g' is the learned parameters, and an attention-based knowledge graph (AKG) represented by the node set S = {s1, s2, ..., sN} and edge set E = {eij | i, j ∈ {1, 2, ..., N}} and the attention weights αij. The overall reasoning process can be summarized as: q → αij → ∑i,j αij * si * eij. The attention weights are learned during training to optimize the reasoning performance.