From Symbolic to Natural-Language Relations: Rethinking Knowledge Graph Construction in the Era of Large Language Models

TL;DR

This paper advocates for transitioning from symbolic relation schemas to natural-language relation descriptions in knowledge graphs, leveraging LLMs for more nuanced, context-aware, and flexible relational representations.

Contribution

It introduces a hybrid design approach that maintains a minimal structure while enabling richer, context-sensitive relation representations using natural language.

Findings

Highlights limitations of symbolic relation schemas in KGs.

Proposes a shift to natural-language relation descriptions.

Suggests hybrid design principles for flexible KG construction.

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.