ParaKG: Scalable Corpus-Level Knowledge Graph Construction from Scientific Documents with Parallel LLM Agents

Knowledge graphs (KGs) are essential for organizing large collections of unstructured scientific literature. While existing LLM-based methods can effectively construct local KGs from individual documents, scaling to corpus-level graphs poses both severe computational bottlenecks and cross-document integration challenges. To address these challenges, we propose **ParaKG**, a parallel multi-agent framework for large-scale scientific KG construction. ParaKG distributes the document extraction workload across dynamically scheduled agents and merges the resulting local graphs via a hierarchical binary-tree reduction. To scale entity resolution without sacrificing precision, we introduce a two-tier mechanism combining fast lexical filtering with context-grounded LLM verification and persistent caching. We evaluate ParaKG on real-world materials science corpora, demonstrating linear speedups during document-level extraction. Our analysis also characterizes sub-linear scaling bottlenecks during the global merge phase, investigates non-deterministic concurrency effects on graph topology, and evaluates stage-specific performance trade-offs across diverse large language model architectures.