PeopleMap: Visualization Tool for Mapping Out Researchers using Natural Language Processing
This tool addresses the difficulty for institutions and external entities in exploring research diversity and fostering collaboration due to outdated directories.
The paper tackles the problem of discovering research expertise at institutions by developing PeopleMap, an interactive web-based tool that visually maps researchers using NLP embeddings from Google Scholar profiles, providing a new way to summarize research talents and discover connections.
Discovering research expertise at institutions can be a difficult task. Manually curated university directories easily become out of date and they often lack the information necessary for understanding a researcher's interests and past work, making it harder to explore the diversity of research at an institution and identify research talents. This results in lost opportunities for both internal and external entities to discover new connections and nurture research collaboration. To solve this problem, we have developed PeopleMap, the first interactive, open-source, web-based tool that visually "maps out" researchers based on their research interests and publications by leveraging embeddings generated by natural language processing (NLP) techniques. PeopleMap provides a new engaging way for institutions to summarize their research talents and for people to discover new connections. The platform is developed with ease-of-use and sustainability in mind. Using only researchers' Google Scholar profiles as input, PeopleMap can be readily adopted by any institution using its publicly-accessible repository and detailed documentation.