Leveraging knowledge graphs and LLMs for content-based reviewer assignment

Abstract The growing volume of academic submissions in recent years highlighted the need for scalable and accurate reviewer assignment systems, able to go beyond techniques based on manual processes and basic keyword matching. We propose a novel pipeline that integrates Knowledge Graphs (KGs) and Large Language Models (LLMs) to automate and enhance the reviewer assignment process. Our method extracts meaningful representations of papers and reviewer expertise using Open Information Extraction, the Computer Science Ontology classifier, and GLiNER to build KGs from research content. LLMs are employed to generate targeted keywords through prompt-based synthesis, refining both paper and reviewer profiles. The assignment relies on a hybrid similarity metric combining Cosine and Jaccard similarities to capture both lexical and semantic alignment. We evaluate the pipeline using standard metrics such as Mean Reciprocal Rank, Mean Average Precision, and Precision at K, on a dataset in the Computer Science domain, demonstrating its effectiveness in aligning submissions with appropriate reviewers. This approach offers a scalable and adaptive solution to the complexities of modern peer review.