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.
Aggarwal, T., Salatino, A., Osborne, F. and Motta, E. (2026). Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field. In: Workshop on LLM-driven Knowledge Graph and Ontology Engineering (llms4kgoe) 2026 co-located with ESWC 2026, 11 May 2026, Dubrovnik, Croatia. https://oro.open.ac.uk/112032/.
Ramzan, F., Salatino, A., Osborne, F., Reforgiato Recupero, D. and Motta, E. (2026). CSO Classifier 4. 0: Software for efficient ontology-based topic annotation in computer science. SoftwareX, 35 https://oro.open.ac.uk/111829/.
Fadda, M., Motta, E., Osborne, F., Reforgiato Recupero, D. and Salatino, A. (2026). Generating knowledge graphs from news articles via multi-agent claim extraction and attribution. Knowledge-Based Systems, 352 https://oro.open.ac.uk/111827/.
Fadda, M., Motta, E., Osborne, F., Recupero, D.R. and Salatino, A. (2026). From Text to Triples: A Neuro-Symbolic Pipeline for Structured Claim Extraction. In: ESWC’26: 5th Workshop on LLM-Integrated Knowledge Graph Generation from Text (TEXT2KG),, 10-14 May 2026, Dubrovnik, Croatia. https://oro.open.ac.uk/111732/.
Fadda, M., Motta, E., Osborne, F., Reforgiato Recupero, D. and Salatino, A. (2026). Integrating Large Language Models and knowledge graphs to capture political viewpoints in news media. EPJ Data Science, 15(1), https://oro.open.ac.uk/111485/.
Bolanos Burgos, F., Salatino, A.A., Osborne, F. and Motta, E. (2026). Modelling and classifying the components of literature reviews: a novel annotation schema and evaluation of transformer models. PeerJ Computer Science, 12 https://oro.open.ac.uk/110897/.
Aggarwal, T., Salatino, A., Osborne, F. and Motta, E. (2026). Leveraging Large Language Models for Generating Research Topic Ontologies: A Multi-Disciplinary Study. ACM Transactions on Intelligent Systems and Technology, 17(5), https://oro.open.ac.uk/110795/.
Dessì, D., Dessì, R., Joy, J., Aras, H. and Osborne, F. (2026). SemTech 2026: The 4th International Workshop on AI and Semantic Technologies for the Scientific, Technical, and Legal Web. In: WWW Companion '26: Companion Proceedings of the ACM Web Conference 2026, 29 Jun - 03 Jul 2026, Dubai, United Arab Emirates. https://oro.open.ac.uk/110451/.
D’Amico, S., Maurino, A., Osborne, F. and Sperlì, G. (2026). Evaluating the effectiveness of fine-tuning in financial NLP: The case of Social Trading Action Detection. Information Processing and Management, 63(8), https://oro.open.ac.uk/110360/.
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, 17(6), 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/.
