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Current generative knowledge graph construction approaches usually fail tocapture structural knowledge by simply flattening natural language intoserialized texts or a specification language. However, large generativelanguage model trained on structured data such as code has demonstratedimpressive capability in understanding natural language for structuralprediction and reasoning tasks. Intuitively, we address the task of generativeknowledge graph construction with code language model: given a code-formatnatural language input, the target is to generate triples which can berepresented as code completion tasks. Specifically, we develop schema-awareprompts that effectively utilize the semantic structure within the knowledgegraph. As code inherently possesses structure, such as class and functiondefinitions, it serves as a useful model for prior semantic structuralknowledge. Furthermore, we employ a rationale-enhanced generation method toboost the performance. Rationales provide intermediate steps, thereby improvingknowledge extraction abilities. Experimental results indicate that the proposedapproach can obtain better performance on benchmark datasets compared withbaselines. Code and datasets are available inhttps://github.com/zjunlp/DeepKE/tree/main/example/llm.

CodeKGC: Code Language Model for Generative Knowledge Graph Construction