@article{llmoracles-kbs310,
acm = {10.1016/j.knosys.2024.112940},
apice = {LlmoraclesKbs310},
articleno = 112940,
arxiv = {2404.04108},
author = {Ciatto, Giovanni and Agiollo, Andrea and Magnini, Matteo and Omicini, Andrea},
dblp = {journals/kbs/CiattoAMO25},
doi = {10.1016/j.knosys.2024.112940},
iris = {11585/1001205},
issn = {0950-7051},
journal = {Knowledge-Based Systems},
keywords = {Ontology population; Large language models; Nutrition; Automation; Domain-specific knowledge},
lens = {002-808-574-876-090},
month = {15~} # feb,
numpages = 22,
openalex = {W4406141444},
opencitations = {0606112740},
pages = {1--22},
publisher = {Elsevier B.V.},
scholar = {12608317221311042342},
scopus = {2-s2.0-85214522484},
title = {Large language models as oracles for instantiating ontologies with domain-specific knowledge},
url = {https://www.sciencedirect.com/science/article/pii/S0950705124015740},
urlopenaccess = {https://www.sciencedirect.com/science/article/pii/S0950705124015740},
volume = 310,
wos = {WOS:001397431800001},
year = 2025
}@inproceedings{activelylearning-ecai2025,
abstract = {In active learning, a learner attempts to learn from a teacher by posing questions. The questions made by the learner are called membership queries and are answered with `yes' or `no'. This kind of query is often studied as part of a communication protocol that also includes equivalence queries. Intuitively, equivalence queries ask whether the idea of the learner about the knowledge of the teacher is correct or not. If not, then the teacher should provide a counterexample showing the difference. Here, we consider the teacher as a large language model (LLM) and study the case in which knowledge is expressed as an EL terminology. Membership queries ask whether concept inclusions are true or not. E.g., ``Can algae be considered a subcategory of plant?''. Equivalence queries are simulated by a sample with concept inclusions labelled as positive or negative. We present a non-trivial extension of the ExactLearner tool to extract EL terminologies from LLMs. Given the relevant symbols as input (e.g., algae, plant, etc.), the tool tries to find how these symbols should be logically connected by posing questions to LLMs. To evaluate the approach, we present performance results of the ExactLearner in the task of reconstructing existing EL terminologies.},
apice = {ActivelylearningEcai2025},
author = {Magnini, Matteo and Squarcialupi, Riccardo and Martin T. Sterri and Ana Ozaki},
booktitle = {28th European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy – Including 14th Conference on Prestigious Applications of Intelligent Systems (PAIS 2025)},
doi = {10.3233/FAIA251009},
editor = {Inês Lynce and Nello Murano and Mauro Vallati and Serena Villata and Chesani, Federico and Milano, Michela and Omicini, Andrea and Mehdi Dastani},
isbn = {978-1-64368-631-8},
numpages = 8,
openalex = {W4415428417},
pages = {1792--1799},
publisher = {IOS Press},
series = {Frontiers in Artificial Intelligence and Applications},
title = {Actively Learning EL Terminologies from Large Language Models},
url = {https://ebooks.iospress.nl/doi/10.3233/FAIA251009},
urlopenaccess = {https://ebooks.iospress.nl/doi/10.3233/FAIA251009},
volume = 413,
year = 2025
}@article{privacypreserving-smhealth36,
abstract = {Medical chatbots are becoming a basic component in telemedicine, propelled by advancements in Large Language Models (LLMs). However, LLMs' integration into clinical settings comes with several issues, with privacy concerns being particularly significant. The paper proposes a tailored architectural solution and an information workflow that address privacy issues, while preserving the benefits of LLMs. We examine two solutions to prevent the disclosure of sensitive information: (i) a filtering mechanism that processes sensitive data locally but leverage a robust OpenAI's online LLM for engaging with the user effectively, and (ii) a fully local deployment of open-source LLMs. The effectiveness of these solutions is assessed in the context of hypertension management across various tasks, ranging from intent recognition to reliable and emphatic conversation. Interestingly, while the first solution proved to be more robust in intent recognition, an evaluation by domain experts of the models' responses, based on reliability and empathetic principles, revealed that two out of six open LLMs received the highest scores. The study underscores the viability of incorporating LLMs into medical chatbots. In particular, our findings suggest that open LLMs can offer a privacy-preserving, yet promising, alternative to external LLM services, ensuring safer and more reliable telemedicine practices. Future efforts will focus on fine-tuning local models to enhance their performance across all tasks.},
apice = {PrivacypreservingSmhealth36},
articleno = 100552,
author = {Montagna, Sara and Stefano Ferretti and Lorenz Cuno Klopfenstein and Michelangelo Ungolo and Martino Francesco Pengo and Aguzzi, Gianluca and Magnini, Matteo},
doi = {10.1016/j.smhl.2025.100552},
eissn = {2352-6491},
issn = {2352-6483},
journal = {Smart Health},
keywords = {Large Language Model, Medical chatbot, Patient self-management, Patient empowerment},
month = mar,
numpages = 13,
openalex = {W4408237661},
publisher = {Elsevier Inc.},
title = {Privacy-preserving LLM-based chatbots for hypertensive patient self-management},
url = {https://www.sciencedirect.com/science/article/pii/S2352648325000133},
urlopenaccess = {https://www.sciencedirect.com/science/article/pii/S2352648325000133},
volume = 36,
year = 2025
}@inproceedings{dslnesy-ansya2025,
apice = {DslNesyAnsya2025},
author = {Matteini, Mattia and Ciatto, Giovanni and Magnini, Matteo and Kuru, Emre and Aydo{\u g}an, Reyhan and Omicini, Andrea},
booktitle = {ANSyA 2025: Advanced Neuro-Symbolic Applications},
dblp = {conf/ansya/MatteiniCMKAO25},
editor = {Agiollo, Andrea and Bardhi, Enkeleda and Ciatto, Giovanni and Dumancic, Giovanni and Marra, Giuseppe},
iris = {11585/1048553},
keywords = {symbolic knowledge injection, SKI-lang, NeSy, language, Python},
month = oct,
note = {Proceedings of the 1st International Workshop on Advanced Neuro-Symbolic Applications co-located with the 28th European Conference on Artificial Intelligence (ECAI 2025)},
numpages = 9,
pages = {84--92},
publisher = {CEUR-WS},
scholar = {5084539886289942855},
scopus = {2-s2.0-105038978909},
series = {CEUR Workshop Proceedings},
title = {A Domain-Specific Language for {NeSy} Focussing on Symbolic Knowledge Injection},
url = {https://ceur-ws.org/Vol-4125/paper_21.pdf},
urlopenaccess = {https://ceur-ws.org/Vol-4125/paper_21.pdf},
urlpdf = {https://ceur-ws.org/Vol-4125/paper_21.pdf},
volume = 4125,
wos = {WOS:001664416600013},
year = 2025
}@inproceedings{nesyaichronicdiseasetelmed2025,
apice = {NeSyAIChronicDiseaseTelmed2025},
author = {Magnini, Matteo and Ciatto, Giovanni and Ahmet Emre Kuru and Christel Sirocchi and Montagna, Sara},
booktitle = {2025 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)},
doi = {10.1109/PerComWorkshops65533.2025.00106},
editor = {Piero Castoldi and Anna Lina Ruscelli and Lorenzo Mucchi and Matti Hämäläinen},
isbn = {979-8-3315-3553-7},
keywords = {Symbolic Knowledge Injection, Neurosymbolic AI, Clinical protocols and data},
month = {17-21 March},
note = {4th International Workshop on Telemedicine and e-Health evolution in the new era of social distancing (TELMED 2025)},
pages = {446--451},
publisher = {IEEE},
title = {Neuro-Symbolic AI for Supporting Chronic Disease Diagnosis and Monitoring},
url = {https://ieeexplore.ieee.org/document/11038535},
urlpdf = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11038535},
year = 2025
}
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