A hybrid AI approach for recommending collaborators in research projects
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Author (Corporation)
Publication date
2026
Type of student thesis
Course of study
Collections
Type
04B - Conference paper
Editor (Corporation)
Supervisor
Parent work
Society 5.0. 5th International Conference Society 5.0 2025, San Benedetto Del Tronto, Italy, June 25–27, 2025, Revised Selected Papers
Special issue
DOI of the original publication
Link
Related research data
Series
Communications in Computer and Information Science (CCIS)
Series number
2787
Volume
Issue / Number
Pages / Duration
252-264
Patent number
Publisher / Publishing institution
Springer
Place of publication / Event location
San Benedetto Del Tronto
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
The success of research project proposals heavily depends on the consortium, which should be experienced and knowledgeable in the topics outlined in the corresponding calls, e.g., those in the EU’s research and innovation programme Horizon Europe. Yet, one of the most challenging activities in such a context is the formation of the consortium, which requires the identification of adequate research collaborators. Traditional methods take this challenge by relying solely on social networks and, or the number of author citations, which proved to be limited in efficacy. This paper proposes an Agentic Graph Retrieval-Augmented Generation (RAG) method, that provides contextual and explainable recommendations, which are tailored to researchers’ areas of expertise and project relevance, thus more effective than existing approaches. The proposed method combines Knowledge Graphs (KGs) and Large Language Models (LLMs) capabilities and has been developed following the Design Science research methodology. The new method has been evaluated by considering two of the highest-performant LLMs currently in the market: Claude Sonnet 3.5 and GPT-4o.
Keywords
Event
5th International Conference Society 5.0 2025
Exhibition start date
Exhibition end date
Conference start date
25.06.2025
Conference end date
27.06.2025
Date of the last check
ISBN
978-3-032-15462-0
978-3-032-15463-7
978-3-032-15463-7
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
peer-reviewed
Open access category
Closed
Citation
Rosati, P., Laurenzi, E., & Quadrini, M. (2026). A hybrid AI approach for recommending collaborators in research projects. In F. Corradini, K. Hinkelmann, H. Smuts, & B. Re (Eds.), Society 5.0. 5th International Conference Society 5.0 2025, San Benedetto Del Tronto, Italy, June 25–27, 2025, Revised Selected Papers (pp. 252–264). Springer. https://doi.org/10.1007/978-3-032-15463-7_21