LightKGG: Simple and Efficient Knowledge Graph Generation from Textual Data

TL;DR

LightKGG presents a lightweight, efficient framework for knowledge graph extraction from text using small-scale language models, reducing computational costs while maintaining high-quality graph construction.

Contribution

The paper introduces two innovative techniques—context-integrated graph extraction and topology-enhanced relationship inference—that enable effective KG generation with minimal hardware.

Findings

Achieves high-quality knowledge graphs with small models

Reduces computational requirements compared to LLM-based methods

Maintains accuracy in relationship inference

Abstract

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.