Hybrid knowledge reasoning over knowledge hypergraph: Inductive, deductive, and abductive

Traditional knowledge reasoning methods, which are predominantly reliant on static rules and structured data, often struggle to adapt to the ambiguity and dynamic evolution of real-world scenarios. To overcome these limitations, this study proposes a novel reasoning framework based on a three-layered knowledge hypergraph. Core innovation lies in the synergy of inductive, deductive, and abductive reasoning mechanisms to enhance both reliability and interpretability. Specifically, hypergraph-based inductive reasoning extracts robust evolutionary patterns by mining the historical subgraph structures. Deductive reasoning ensures transparency by constructing tree-shaped inference paths, whereas abductive reasoning establishes causal traceability by forming evidence chains from historical contexts. Experimental evaluations on the integrated crisis early warning system (ICEWS) dataset demonstrate that the proposed approach significantly outperforms existing methods in terms of accuracy and interpretability, thereby offering a scalable solution for complex event analysis.