Annotating Research Infrastructure in Scientifc Papers: An NLP-driven Approach

In this work, we present a natural language processing (NLP) pipeline for the identifcation, extraction and linking of Research Infrastructure (RI) used in scientifc publications. Links between scientifc equipment and publications where the equipment was used can support multiple use cases, such as evaluating the impact of RI investment, and supporting Open Science and research reproducibility. These links can also be used to establish a profle of the RI portfolio of each institution and associate each equipment with scientifc output. The system we are describing here is already in production, and has been used to address real business use cases, some of which we discuss in this paper. The computational pipeline at the heart of the system comprises both supervised and unsuper-vised modules to detect the usage of research equipment by processing the full text of the articles. Additionally, we have created a knowledge graph of RI, which is utilized to annotate the articles with metadata. Finally, examples of the business value of the insights made possible by this NLP pipeline are illustrated.