用小模型高效生成知识图谱，降低硬件门槛。
LightKGG: Simple and Efficient Knowledge Graph Generation from Textual Data
- 将上下文信息融入节点与边的统一图结构中
- 利用图拓扑结构推断关系，减少对语言理解依赖
- 适合资源受限场景下的知识图谱构建
高质量知识图谱（KG）的匮乏仍是下游AI应用的关键瓶颈，现有提取方法依赖易出错的模式匹配或资源密集型大语言模型（LLMs）。尽管近期工具借助LLMs生成KG，但其计算开销限制了低资源环境的应用。本文提出LightKGG，一种基于小规模语言模型（SLMs）的高效知识图谱生成框架，包含两项核心技术：(1) 上下文融合图抽取将上下文信息整合到节点与边中，形成统一图结构，降低对复杂语义处理的依赖，同时保留更多关键信息；(2) 拓扑增强的关系推理利用已提取图的固有拓扑结构，高效推断关系，无需依赖LLM的复杂语言理解能力。该方法在极低硬件要求下实现准确的KG构建，弥合了自动化知识抽取与实际部署之间的差距，并为优化SLMs在结构化NLP任务中的效率提供了科学方法。
原文摘要 · Abstract (English)
The scarcity of high-quality knowledge graphs (KGs) remains a critical bottleneck for downstream AI applications, as existing extraction methods rely heavily on error-prone pattern-matching techniques or resource-intensive large language models (LLMs). While recent tools leverage LLMs to generate KGs, their computational demands limit accessibility for low-resource environments. Our paper introduces LightKGG, a novel framework that enables efficient KG extraction from textual data using small-scale language models (SLMs) through two key technical innovations: (1) Context-integrated Graph extraction integrates contextual information with nodes and edges into a unified graph structure, reducing the reliance on complex semantic processing while maintaining more key information; (2) Topology-enhanced relationship inference leverages the inherent topology of the extracted graph to efficiently infer relationships, enabling relationship discovery without relying on complex language understanding capabilities of LLMs. By enabling accurate KG construction with minimal hardware requirements, this work bridges the gap between automated knowledge extraction and practical deployment scenarios while introducing scientifically rigorous methods for optimizing SLM efficiency in structured NLP tasks.
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