  The number of publications related to the Sustainable Development Goals
(SDGs) continues to grow. These publications cover a diverse spectrum of
research, from humanities and social sciences to engineering and health. Given
the imperative of funding bodies to monitor outcomes and impacts, linking
publications to relevant SDGs is critical but remains time-consuming and
difficult given the breadth and complexity of the SDGs. A publication may
relate to several goals (interconnection feature of goals), and therefore
require multidisciplinary knowledge to tag accurately. Machine learning
approaches are promising and have proven particularly valuable for tasks such
as manual data labeling and text classification. In this study, we employed
over 82,000 publications from an Australian university as a case study. We
utilized a similarity measure to map these publications onto Sustainable
Development Goals (SDGs). Additionally, we leveraged the OpenAI GPT model to
conduct the same task, facilitating a comparative analysis between the two
approaches. Experimental results show that about 82.89% of the results obtained
by the similarity measure overlap (at least one tag) with the outputs of the
GPT model. The adopted model (similarity measure) can complement GPT model for
SDG classification. Furthermore, deep learning methods, which include the
similarity measure used here, are more accessible and trusted for dealing with
sensitive data without the use of commercial AI services or the deployment of
expensive computing resources to operate large language models. Our study
demonstrates how a crafted combination of the two methods can achieve reliable
results for mapping research to the SDGs.
