Neural Symbolic Reasoning Engine for Dynamic Knowledge Graph Construction

Abstract

This paper proposes a novel approach to knowledge graph construction by integrating neural symbolic reasoning engines. The core idea is to leverage the strengths of both neural networks (specifically Transformer-based models for reasoning) and traditional knowledge graph reasoning techniques. We introduce a dynamic knowledge graph construction system that can automatically learn and update the knowledge graph based on new information and evolving relationships. The system utilizes a neural inference model to perform reasoning over the knowledge graph, and then employs the inference results to dynamically modify the graph's structure and node connections. This approach addresses the limitations of traditional knowledge graph construction, which heavily relies on manual annotation and lacks the ability to adapt to changing information. The resulting system offers a more robust and intelligent method for knowledge graph development, particularly in domains where data is constantly evolving. The primary contribution lies in the synergistic combination of neural inference and symbolic reasoning, enabling a truly dynamic knowledge graph. ---