Expert Recommendation for Interdisciplinary Project Evaluation Based on Multi-Agent Collaboration and Dynamic Knowledge Graphs

Interdisciplinary scientific project evaluation requires reviewer selection that is both accurate and explainable; however, rapidly changing topics and heterogeneous terminology make it difficult to match experts. This study develops and evaluates an interpretable expert recommendation framework for interdisciplinary project evaluation. The proposed Knowledge Graph-driven Multi-Agent Recommendation System (KG-MARS) combines terminology alignment for cross-domain semantic normalization, graph retrieval over a dynamically weighted knowledge graph, and expert ranking with explicit justifications. Here, the term “agents” refers to specialized coordinated software modules with fixed roles, not to autonomous large language model or reinforcement learning agents. We evaluate the proposed framework on a de-identified institutional dataset containing 2846 experts, 462 research projects, and 31,306 weighted relations across 12 interdisciplinary domains. Rather than claiming a uniformly large accuracy gain—the overall margin over the strongest baseline is small and obtained at comparable computational cost—we show that the benefit of KG-MARS is concentrated where interdisciplinary matching is hardest, while remaining robust to label leakage and corpus drift. Under a common offline protocol with five randomized splits, KG-MARS achieved a precision of 0.8027±0.011, a recall of 0.7384±0.013, and an F1-score of 0.7852±0.010, outperforming ten baselines; the recall gain over the strongest baseline remained statistically significant after Holm–Bonferroni correction for multiple comparisons (paired t-test, p<0.05), with a large effect size in the paired five-split test (Cohen’s d>0.8); the F1-score gain was significant at the split level but only borderline at the more conservative project level, whereas the precision difference over that baseline was not statistically significant at either level. KG-MARS also achieved the best normalized discounted cumulative gain (NDCG@10), mean average precision (MAP), and mean reciprocal rank (MRR). A temporal hold-out experiment separating feature and label time windows confirmed that the advantage is not an artifact of label leakage, and a drift experiment showed that periodic dynamic updates preserve accuracy as the corpus evolves. Removing individual modules lowered the F1-score by 14.0–19.2% and cross-domain analyses showed significantly larger gains on domain pairs with greater semantic distance, reaching up to 10.3 F1 points on the most distant pair. These findings indicate that dynamic knowledge graphs and coordinated agents can support more accurate and auditable expert selection in institutional project evaluation, particularly for interdisciplinary proposals.