Expert Recommendation System for Project Reviews Based on Knowledge Graphs

In scientific research projects or technical evaluations, finding suitable reviewers is crucial. However, traditional methods often rely on simple keyword matching or manual selection, failing to comprehensively consider experts' fields of expertise, research directions, qualifications, and practical experience. Currently, most project reviewer systems use computer assisted human methods, heavily relying on operators' subjective judgments of experts, which can lead to lax review and unfair evaluation. This also results in a high degree of coupling between operators and recommendation systems. Knowledge graphs can integrate and analyze vast amounts of expert data, including their academic backgrounds, professional experiences, research projects, and scholarly impact. By leveraging relationships and semantic information within knowledge graphs, project requirements can be intelligently matched with experts' backgrounds to provide optimal recommendations for reviewer selection. This article primarily focuses on two aspects: constructing an expert knowledge graph and building a reviewer recommendation system, pro-posing a design solution for project reviewer recommendation systems.