![]() ![]() Besides, our method achieved the first place in the first task of CCKS-2023 Knowledge Graph Construction. Experimental results validate the advantages of our proposed method. We further boost KGC performances via an elaborately designed schema-constrained decoding strategy and a LLM-guided correction module. ![]() To alleviate the bias of a single LLM, we integrate the superiority of several expert models to derive credible results from multiple perspectives. We fine-tune a LLM with tailored KGC corpus, through which the generalization ability of LLMs are transfered for KGC with evolving schema. In this paper, we propose a schema-adaptive KGC method driven by the instruction-tuning large language models (LLM). Despite the success of prior studies, it is struggling to accommodate existing KGC models with evolving entity-relation knowledge schema. Knowledge graph construction (KGC) aims to build the semantic network which expresses the relationship between named entities.
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