Research on the construction and application of problem-method-oriented academic graph empowered by LLM - Qigang Liu; Yinfan Wang; Lifeng Mu; Jun Li (Discover Computing, 2025)
Abstract Nowadays, the volume of literature in each field is huge and is growing rapidly, which posts challenge to researchers’ literature review. In this circumstance, developing useful tool for achieving efficient literature management is of high value. Traditional literature management tools, such as tools for key word searching, paper recommendation, relation visualization, and keyword cloud drawing, are not suitable for conducting content-level literature review. To address the issues of traditional literature management tools, a novel problem and method-oriented fine-grained academic graph is proposed to facilitate the exploration of research questions, methodologies, study perspectives, and their connections hidden in massive literature. For building such graph, a new ontology dedicated for describing the features of research paper is developed, an innovative multi-relation join extraction model is proposed, and a creative approach for leveraging the Large Language Models (LLM) to augment the triplet extraction results generated by supervised-learning model is developed. Experiments on widely used benchmark datasets show that the proposed multi-relation extraction model is able to achieve at least 8.01% and 8.65% improvement on entity identification and relation classification respectively, compared with state-of-the-art models. The visualized demonstration of the proposed graph shows that our graph is capable of accurately capturing the problem network, method network and hot topics hidden in massive literature. The Q&A system supported by the proposed graph demonstrates that our graph is really helpful for conducting literature review.