Research
LLM-Based Knowledge Graph Completion Combining Discrete Structural Coding with Similar Entity Information
arXiv:2608.30235v1 Announce Type: new Abstract: Knowledge graph completion requires models to use both textual descriptions and relational structure. Existing LLM-based methods either encode KG struct
arXiv:2608.30235v1 Announce Type: new Abstract: 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 ranking. Experiments on FB15k-237 show that CoSC outperforms existing baselines on MRR and Hits@10 while remaining competitive on Hits@1.
Related
- GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion
- Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction
- ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion
- SABET-QA: Temporal Knowledge Graph Question Answering
Source: arXiv cs.AI | 2026-09-01