The topics associated to the KMi publications listed in this page were automatically generated using the CSO Classifier, a solution developed by the SKM3 team in KMi. This technology has also been adopted by Springer Nature and is used routinely by them to generate automatically the metadata for all Computer Science conference proceedings they publish.
Salatino, A., Osborne, F., Recupero, D.R., Angioni, S. and Motta, E. (2026). Does Diversity of Expertise Drive Citation Impact? Evidence from Computer Science. Scientometrics, 131 pp. 1119–1146. https://oro.open.ac.uk/109014/.
Bongini, P., Rossolini, M., Maurino, A. and Osborne, F. (2025). The information power of social media for investment decisions: an AI-driven analysis of Reddit posts. Journal of Financial Management, Markets and Institutions, 13(02), https://oro.open.ac.uk/107939/.
Murgia, M., Dessi, D., Osborne, F., Buscaldi, D., Motta, E. and Recupero, D.R. (2025). CiteGen: A Web Application for Citation Recommendation Powered by LLMs and Knowledge Graphs. In: The Semantic Web: ESWC 2025 Satellite Events, 01-05 Jun 2025, Portoroz, Slovenia. https://oro.open.ac.uk/107360/.
Birti, M., Maurino, A. and Osborne, F. (2025). Optimizing Large Language Models for ESG Activity Detection in Financial Texts. In: ICAIF ’25: 6th ACM International Conference on AI in Finance, 15-18 Nov 2025, Singapore, Singapore. https://oro.open.ac.uk/107288/.
Cadeddu, A., Chessa, A., De Leo, V., Fenu, G., Motta, E., Osborne, F., Reforgiato Recupero, D., Salatino, A. and Secchi, L. (2025). A Comparative Study of Task Adaptation Techniques of Large Language Models for Identifying Sustainable Development Goals. IEEE Access, 13 pp. 175271–175291. https://oro.open.ac.uk/106905/.
Aggarwal, T., Salatino, A., Osborne, F. and Motta, E. (2026). Large language models for scholarly ontology generation: An extensive analysis in the engineering field. Information Processing & Management, 63(1), https://oro.open.ac.uk/105868/.
Meloni, A., Reforgiato Recupero, D., Osborne, F., Salatino, A.A., Motta, E., Vahadati, S. and Lehmann, J. (2025). Exploring Large Language Models for Scientific Question Answering via Natural Language to SPARQL Translation. ACM Transactions on Intelligent Systems and Technology (Early access). https://oro.open.ac.uk/105679/.
Cadeddu, A., Chessa, A., De Leo, V., Fenu, G., Motta, E., Osborne, F., Recupero, D.R., Salatino, A. and Secchi, L. (2025). Benchmarking Large Language Models for Sustainable Development Goals Classification: Evaluating In-Context Learning and Fine-Tuning Strategies. In: 3rd International Workshop on Semantic Technologies and Deep Learning Models for Scientific, Technical and Legal Data (SemTech4STLD 2025), 01 Jun 2025, Portoroz, Slovenia. https://oro.open.ac.uk/105343/.
Buscaldi, D., Dessì, D., Osborne, F., Piras, D. and Recupero, D.R. (2025). Evaluating LLMs for Named Entity Recognition in Scientific Domain with Fine-Tuning and Few-Shot Learning. In: 3rd International Workshop on Semantic Technologies and Deep Learning Models for Scientific, Technical and Legal Data (SemTech4STLD 2025), 01 Jun 2025, Portoroz, Slovenia. https://oro.open.ac.uk/105348/.
Motta, E., Daga, E., Gangemi, A., Gjelsvik, M.L., Osborne, F. and Salatino, A. (2025). The Epistemology of Fine-Grained News Classification. Semantic Web, 16(3), https://oro.open.ac.uk/104622/.
Dessí, D., Osborne, F., Buscaldi, D., Reforgiato Recupero, D. and Motta, E. (2025). CS-KG 2. 0: A Large-scale Knowledge Graph of Computer Science. Scientific Data, 12(1), https://oro.open.ac.uk/104624/.
Meloni, A., Recupero, D.R., Osborne, F., Salatino, A., Motta, E., Vahadati, S. and Lehmann, J. (2025). Assessing Large Language Models for SPARQL Query Generation in Scientific Question Answering. In: ISWC 2024 Special Session on Harmonising Generative AI and Semantic Web Technologies,, 13 Nov 2024, Baltimore, Maryland, USA. https://oro.open.ac.uk/104247/.
Tsaneva, S., Dessì, D., Osborne, F. and Sabou, M. (2025). Knowledge graph validation by integrating LLMs and human-in-the-loop. Information Processing & Management, 62(5), https://oro.open.ac.uk/103792/.
Salatino, A., Aggarwal, T., Mannocci, A., Osborne, F. and Motta, E. (2025). A survey of knowledge organization systems of research fields: Resources and challenges. Quantitative Science Studies, 6 pp. 567–610. https://oro.open.ac.uk/103702/.
Borrego, A., Dessì, D., Ayala, D., Hernández, I., Osborne, F., Recupero, D.R., Buscaldi, D., Ruiz, D. and Motta, E. (2025). Research hypothesis generation over scientific knowledge graphs. Knowledge-Based Systems, 315 https://oro.open.ac.uk/103220/.
Greco, D., Osborne, F., Pusceddu, S. and Reforgiato Recupero, D. (2025). Modelling big data platforms as knowledge graphs: the data platform shaper. Journal of Big Data, 12(1), https://oro.open.ac.uk/103274/.
Aggarwal, T., Salatino, A., Osborne, F. and Motta, E. (2024). Identifying Semantic Relationships Between Research Topics Using Large Language Models in a Zero-Shot Learning Setting. In: 4th International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment, Sci-K 2024, 12 Nov 2024, Baltimore. https://oro.open.ac.uk/101266/.
Tsaneva, S., Dessì, D., Osborne, F. and Sabou, M. (2024). Enhancing Scientific Knowledge Graph Generation Pipelines with LLMs and Human-in-the-Loop. In: 4th International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment, Sci-K 2024, 12 Nov 2024, Baltimore. https://oro.open.ac.uk/101265/.
Motta, E., Osborne, F., Pulici, M.M., Salatino, A. and Naja, I. (2024). Capturing the Viewpoint Dynamics in the News Domain. In: Proceedings of the 24th International Conference on Knowledge Engineering and Knowledge Management (EKAW-24), 26-28 Nov 2024, Amsterdam, Netherlands. https://oro.open.ac.uk/100046/.
Pisu, A., Pompianu, L., Salatino, A., Osborne, F., Riboni, D., Motta, E. and Reforgiato Recupero, D. (2024). Classifying Scientific Topic Relationships with SciBERT. In: Joint Proc. of Posters, Demos, Workshops, and Tutorials of the 20th Int.l Conf. on Semantic Systems (SEMANTiCS 2024), 17-19 Sep 2024, Amsterdam. https://oro.open.ac.uk/100269/.
