Enhancing Research Information Systems with Identification of Domain Experts
Gautam Kishore Shahi, Oliver Hummel
TL;DR
The paper tackles the problem of locating domain experts within growing research ecosystems, where traditional catalogues and static profiles fall short. It proposes a knowledge-based search engine that ingests official researcher lists, crawls publications, extracts text from PDFs, and combines metadata with LLM-derived classifications to map researchers to research areas, stored in Elasticsearch with a Flask-based UI. A prototype implemented at Hochschule Mannheim demonstrates end-to-end extraction and search capabilities, including a word-cloud overview of topics and detailed expert profiles. The work aims to enable collaborations, technology transfer, and advanced training by surface up-to-date expertise across institutions, while outlining data-source, multilingual, and profiling limitations and a clear path for future enhancements.
Abstract
Research organisations and their research outputs have been growing considerably in the past decades. This large body of knowledge attracts various stakeholders, e.g., for knowledge sharing, technology transfer, or potential collaborations. However, due to the large amount of complex knowledge created, traditional methods of manually curating catalogues are often out of time, imprecise, and cumbersome. Finding domain experts and knowledge within any larger organisation, scientific and also industrial, has thus become a serious challenge. Hence, exploring an institutions domain knowledge and finding its experts can only be solved by an automated solution. This work presents the scheme of an automated approach for identifying scholarly experts based on their publications and, prospectively, their teaching materials. Based on a search engine, this approach is currently being implemented for two universities, for which some examples are presented. The proposed system will be helpful for finding peer researchers as well as starting points for knowledge exploitation and technology transfer. As the system is designed in a scalable manner, it can easily include additional institutions and hence provide a broader coverage of research facilities in the future.