🤖 AI Summary
This work proposes a novel knowledge graph representation paradigm that addresses the limitations of traditional approaches, which rely on predefined symbolic relations and consequently fail to capture the context-dependency, fine-grained semantics, and inherent uncertainty of real-world relationships—often leading to critical information loss. By reformulating relations as natural language descriptions rather than discrete symbols, the proposed framework leverages the generative and reasoning capabilities of large language models. It integrates prompt engineering with a minimal structural backbone, thereby harmonizing structured scaffolding with unstructured semantic expression. This hybrid design substantially enhances the semantic richness and context-awareness of relation modeling, offering a more adaptive and expressive pathway for knowledge graph construction in the era of large language models.
📝 Abstract
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.