Ontology-based semantic link prediction for enhancing academic collaboration through knowledge management

Abstract

This paper introduces a novel ontology-based semantic link prediction framework that unifies structural, temporal, and semantic signals from heterogeneous scholarly sources to enhance academic collaboration forecasting. By integrating AMiner, DBLP, and Mendeley datasets into a unified SKOS- and Dublin Core-aligned ontology, the framework enables semantic enrichment, cross-source reasoning, and contextualized link prediction. Unlike previous studies that focus solely on structural features or basic content similarity, our approach leverages ontology-based semantic feature engineering and graph-based learning for robust and interpretable predictions. Experimental results show that random forest and graph neural networks significantly outperform traditional models, achieving high accuracy and ranking precision. This work contributes to knowledge management by enabling expert recommendation, trend identification, and semantic integration for strategic academic planning.