LightKGG: Simple and Efficient Knowledge Graph Generation from Textual Data
This work addresses the bottleneck of high-quality knowledge graph scarcity for downstream AI applications, particularly in low-resource environments, though it is incremental in optimizing existing approaches.
The paper tackles the problem of inefficient knowledge graph generation from text by introducing LightKGG, a framework that uses small-scale language models to achieve efficient extraction with minimal hardware requirements, reducing computational demands compared to existing methods.
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.