Smarter Extraction of Scholarly Metadata using Knowledge Graphs, Language Models, and Agents

Abstract

Scholarly metadata is an essential element in the scholarly ecosystem, empowering discovery, interoperability, and provenance and its efficacy relies on metadata quality and coverage. However, with the substantial increase in the number and diversity of research artifacts over time, it has become a cumbersome task to harvest metadata features for scholarly publications, datasets, source code, and machine learning models. These artifacts generate heterogeneous metadata features, which are distributed across various repositories. In addition, large language models (LLMs) and agents have emerged as powerful tools and are now being extensively adopted to facilitate a wide range of tasks across domains, ranging from information extraction to knowledge graphs. Nevertheless, numerous open questions persist regarding their adoption in diversified research disciplines, especially concerning their accuracy, robustness, biases, and societal impact when applied in the context of research metadata, scholarly information extraction, and open science. This workshop aims to cover these exciting aspects centered around metadata features from research artifacts, with a focus on large language models and knowledge graphs.