Practical Author Name Disambiguation under Metadata Constraints: A Contrastive Learning Approach for Astronomy Literature

Abstract The ability to distinctly and properly collate an individual researcher’s publications is crucial for ensuring appropriate recognition, guiding the allocation of research funding, and informing hiring decisions. However, accurately grouping and linking a researcher’s entire body of work with their individual identity is challenging because of widespread name ambiguity across the growing literature. Algorithmic author name disambiguation (AND) provides a scalable approach to disambiguating author identities, yet existing methods have limitations. Many modern AND methods rely on comprehensive metadata features such as email, venue, or affiliation. Despite advancements in digitally indexing publications, metadata is often unavailable or inconsistent in large digital libraries (e.g., SAO/NASA Astrophysics Data System). We introduce the Neural Author Name Disambiguator (NAND), a method that effectively disambiguates author identities in large digital libraries despite limited metadata availability. We formulate the AND task as a similarity learning problem by employing a Siamese neural network to disambiguate author names across publications relying solely on widely available publication metadata—author names, titles, and abstracts. We construct the Large-Scale Physics Open Researcher and Contributor ID (ORCID)-Linked dataset to evaluate the NAND method by cross-matching NASA/ADS publications ORCiD. By leveraging foundation models to embed metadata into features, the NAND achieves up to 94% accuracy in pairwise disambiguation and over 95% F1 in clustering publications into their correct researcher identities. We release the testing dataset as a benchmark for physics and astronomy, providing realistic evaluation conditions for future disambiguation methods. The NAND algorithm demonstrates effective disambiguation with minimal metadata, offering a scalable solution for name ambiguity in large-scale digital libraries.