2024/03/18 by William Watson, Watson, William, Lawrence Yong +1
Computer Science · #Advanced Graph Neural Networks #Advanced Text Analysis Techniques #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Semantic Web and Ontologies #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2403.18855
openalex publication_date 2024/03/18 · openalex created_date 2024/03/30 · openalex updated_date 2026/07/28
We explore link prediction as a proxy for automatically surfacing documents from existing literature that might be topically or contextually relevant to a new document. Our model uses transformer-based graph embeddings to encode the meaning of each document, presented as a node within a citation network. We show that the semantic representations that our model generates can outperform other content-based methods in recommendation and ranking tasks. This provides a holistic approach to exploring citation graphs in domains where it is critical that these documents properly cite each other, so as to minimize the possibility of any inconsistencies