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LLM-Based Knowledge Graph Completion Combining Discrete Structural Coding with Similar Entity Information

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Knowledge graph completion requires models to use both textual descriptions and relational structure. Existing LLM-based methods either encode KG structure as discrete tokens or refine a restricted set of candidate entities, and these two directions have largely been studied separately. We propose CoSC for LLM-based KGC, which combines discrete structural coding with similar entity information. Specifically, an LLM generates an initial candidate entity ranking from discrete structural codes, after which information from entities with structures similar to that of the query entity refines the r

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