ISKO UK Meetup - A Survey on Knowledge Organization Systems of Research Fields: Resources and Challenges

Knowledge Organization Systems (KOSs), such as term lists, thesauri, taxonomies, and ontologies, play a fundamental role in categorising, managing, and retrieving information. In the academic domain, KOSs are often adopted for representing research areas and their relationships, primarily aiming to classify research articles, academic courses, patents, books, scientific venues, domain experts, grants, software, experiment materials, and several other relevant products and agents. These structured representations of research areas, widely embraced by many academic fields, have proven effective in empowering AI-based systems to i) enhance the retrievability of relevant documents, ii) enable advanced analytic solutions to quantify the impact of academic research, and iii) analyse and forecast research dynamics. This paper aims to present a comprehensive survey of the current KOS for academic disciplines. We analysed and compared 45 KOSs according to five main dimensions: scope, structure, curation, usage, and links to other KOSs. Our results reveal a very heterogeneous scenario in terms of scope, scale, quality, and usage, highlighting the need for more integrated solutions for representing research knowledge across academic fields. We conclude by discussing the main challenges and the most promising future directions.

About Dr Angelo Salatino:

Dr Angelo Salatino is a Research Fellow at the Knowledge Media Institute of The Open University who develops AI solutions to analyse scholarly data. His main research interests include detecting emerging research trends and creating semantic technologies to organise scholarly knowledge. He has created several systems used by publishers and research organisations, including the Computer Science Ontology (CSO), currently the largest taxonomy of research topics in Computer Science; the CSO Classifier, which annotates research documents; and Augur, a tool for detecting emerging research topics. Recently, his research has focused on investigating how Large Language Models can support the analysis of scientific knowledge. He is particularly interested in exploring how these models can be enhanced with domain knowledge to understand scientific content better and support researchers and practitioners in their daily activities.