Semantic-Condition Tuning: Fusing Graph Context with Large Language Models for Knowledge Graph Completion

• URL: http://arxiv.org/abs/2510.08966v1
• Date: Fri, 10 Oct 2025 03:22:27 GMT
• Title: Semantic-Condition Tuning: Fusing Graph Context with Large Language Models for Knowledge Graph Completion
• Authors: Ruitong Liu, Yan Wen, Te Sun, Yunjia Wu, Pingyang Huang, Zihang Yu, Siyuan Li,
• Abstract summary: Fusing Knowledge Graphs with Large Language Models is crucial for knowledge-intensive tasks like knowledge graph completion.<n>We propose Semantic-Adaptive Graph Tuning (SCT), a new knowledge injection paradigm comprising two key modules.<n>SCT provides a more direct and potent signal, enabling more accurate and robust knowledge reasoning.
• Score: 6.895510903358326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fusing Knowledge Graphs with Large Language Models is crucial for knowledge-intensive tasks like knowledge graph completion. The prevailing paradigm, prefix-tuning, simply concatenates knowledge embeddings with text inputs. However, this shallow fusion overlooks the rich relational semantics within KGs and imposes a significant implicit reasoning burden on the LLM to correlate the prefix with the text. To address these, we propose Semantic-condition Tuning (SCT), a new knowledge injection paradigm comprising two key modules. First, a Semantic Graph Module employs a Graph Neural Network to extract a context-aware semantic condition from the local graph neighborhood, guided by knowledge-enhanced relations. Subsequently, this condition is passed to a Condition-Adaptive Fusion Module, which, in turn, adaptively modulates the textual embedding via two parameterized projectors, enabling a deep, feature-wise, and knowledge-aware interaction. The resulting pre-fused embedding is then fed into the LLM for fine-tuning. Extensive experiments on knowledge graph benchmarks demonstrate that SCT significantly outperforms prefix-tuning and other strong baselines. Our analysis confirms that by modulating the input representation with semantic graph context before LLM inference, SCT provides a more direct and potent signal, enabling more accurate and robust knowledge reasoning.

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