从符号关系到自然语言关系：在大语言模型时代重新思考知识图谱构建
From Symbolic to Natural-Language Relations: Rethinking Knowledge Graph Construction in the Era of Large Language Models
AI总结：
本文探讨在大语言模型时代，知识图谱构建应从符号关系转向自然语言关系描述，提出混合设计原则以实现更灵活的关系表示。
摘要：
知识图谱（KGs）通常使用预定义的符号关系模式进行构建，通常以分类关系标签实现。这种设计有显著缺点：现实中的关系往往是情境性的、细致的，有时甚至是不确定的，而将其压缩成离散的关系标签会丢失关键的语义细节。然而，符号关系知识图谱仍然被广泛使用，因为它们在操作上有效，并且与预大语言模型（LLM）下游模型和算法广泛兼容，在其中KG知识可以被检索或编码成量化特征和嵌入。大语言模型的出现改变了知识的创造和消费方式。LLMs支持在简洁的自然语言中进行可扩展的领域事实合成，并且基于提示的推理倾向于选择上下文丰富的自由文本而不是量化表示。本文主张这些变化要求重新思考关系本身的表示，而不是仅仅使用LLMs更高效地填充传统模式。因此，我们倡导从符号关系转向自然语言关系描述，并提出混合设计原则，以保留最小的结构骨架，同时使关系表示更加灵活和情境敏感。
英文摘要：
Knowledge graphs (KGs) have commonly been constructed using predefined symbolic relation schemas, typically implemented as categorical relation labels. This design has notable shortcomings: real-world relations are often contextual, nuanced, and sometimes uncertain, and compressing it into discrete relation labels abstracts away critical semantic detail. Nevertheless, symbolic-relation KGs remain widely used because they have been operationally effective and broadly compatible with pre-LLM downstream models and algorithms, in which KG knowledge could be retrieved or encoded into quantified features and embeddings at scale. The emergence of LLMs has reshaped how knowledge is created and consumed. LLMs support scalable synthesis of domain facts directly in concise natural language, and prompting-based inference favors context-rich free-form text over quantified representations. This position paper argues that these changes call for rethinking the representation of relations themselves rather than merely using LLMs to populate conventional schemas more efficiently. We therefore advocate moving from symbolic to natural-language relation descriptions, and we propose hybrid design principles that preserve a minimal structural backbone while enabling more flexible and context-sensitive relational representations.
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