GLAMOR: Graph-based LAnguage MOdel embedding for citation Recommendation

Digital publishing’s exponential growth has created vast scholarly collections. Guiding researchers to relevant resources is crucial, and knowledge graphs (KGs) are key tools for unlocking hidden knowledge. However, current methods focus on external links between concepts, ignoring the rich information within individual papers. Challenges like insufficient multi-relational data, name ambiguity, and cold-start issues further limit existing KG-based methods, failing to capture the intricate attributes of diverse entities. To solve these issues, we propose GLAMOR, a robust KG framework encompassing entities e.g., authors, papers, fields of study, and concepts, along with their semantic interconnections. GLAMOR uses a novel random walk-based KG text generation method and then fine-tunes the language model using the generated text. Subsequently, the acquired context-preserving embeddings facilitate superior top@k predictions. Evaluation results on two public benchmark datasets demonstrate our GLAMOR’s superiority against state-of-the-art methods especially in solving the cold-start problem.