1. Massri, M.Besher, Spahiu, Blerina, Grobelnik, Marko, Alexiev, Vladimir, Palmonari, Matteo, & Roman, Dumitru. (2023). Towards InnoGraph: A Knowledge Graph for AI Innovation. 3rd International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment (Sci-K 2023), Austin, Texas, USA. https://doi.org/10.1145/3543873.3587614
2. Buza, Krisztian. (2023). Data Augmentation Does Not Necessarily Beat a Smart Algorithm. 12th Japanese-Hungarian Symposium on Discrete Mathematics and Its Applications. https://doi.org/10.5281/zenodo.8010581
3. Buza, Krisztian Antal. (2023). Time Series Forecasting with Distortion-Aware Convolutional Neural Networks. 9th SIGKDD International Workshop on Mining and Learning from Time Series. https://doi.org/10.5281/zenodo.8186597
4. Buza, Krisztian Antal. (2023). Classification of Sparse and Irregularly Sampled Time Series with Convolutional Neural Networks. 9th SIGKDD International Workshop on Mining and Learning from Time Series. https://doi.org/10.5281/zenodo.8186573
5. R. Selvan, Julian Schon, Erik Dam Operating Critical Machine Learning Models in Resource Constrained Regimes Resource Efficient Medical Image Analysis workshop at MICCAI-2023. https://doi.org/10.1007/978-3-031-47425-5_29
6. A. Q. Khan et al., Towards Graph-based Cloud Cost Modelling and Optimisation, 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), Torino, Italy, 2023, pp. 1337-1342, https://10.1109/COMPSAC57700.2023.00203.
7. A. Klironomos, B. Zhou, Z, Tan, Z. Zheng, G. Mohamed, H. Paulheim and E. Kharlamov (2023). ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics. Extended Semantic Web Conference. https://doi.org/10.48550/arXiv.2305.02966.
8. Nicklas Boserup, Raghavendra Selvan Efficient Self-Supervision using Patch-based Contrastive Learning 10.7557/18.6798
9. Zhou, B., Nikolov, N., Zheng, Z., Luo, X., Savkovic, O., Roman, D., Soylu, A., and Kharlamov, E. (2023). Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case. The 22nd International Semantic Web Conference. Athens, Greece. ArXiv, abs/2308.01094.
10. Z. Tan, B. Zhou, Z. Zheng, O. Savkovic, Z. Huang, I. Gonzalez, A. Soylu, and Evgeny Kharlamov (2023). Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case. The 22nd International Semantic Web Conference. Athens, Greece. https://doi.org/10.48550/arXiv.2308.01105.
11. Rincon-Yanez, D. et al. (2023). Addressing the Scalability Bottleneck of Semantic Technologies at Bosch. In: Pesquita, C., et al. The Semantic Web: ESWC 2023 Satellite Events. ESWC 2023. Lecture Notes in Computer Science, vol 13998. Springer, Cham. https://doi.org/10.1007/978-3-031-43458-7_33
12. C.X. Chu, M.H. Gad-Elrab, T.K. Tran, M. Schiller, E. Kharlamov, D. Stepanova (2023). Supplier Optimization at Bosch with Knowledge Graphs and Answer Set Programming. n: Pesquita, C., et al. The Semantic Web: ESWC 2023 Satellite Events. ESWC 2023. Lecture Notes in Computer Science, vol 13998. Springer, Cham. https://doi.org/10.1007/978-3-031-43458-7_38
13. R. Selvan, Julian Schon, Erik Dam. Operating Critical Machine Learning Models in Resource-Constrained Environments, 10.1007/978-3-031-47425-5_29
14. R. Avogadro, M. Ciavotta, F. De Paoli, M. Palmonari, D. Roman (2023). Estimating Link Confidence for Human-in-the-loop Table Annotation. In proceeding of the 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, Venice, Italy, 26-29 October 2023.
15. Nikolay Nikolov, Arnor Solberg, Radu Prodan, Ahmet Soylu, Mihhail Matskin, Dumitru Roman (2023). Container-based Data Pipelines on the Computing Continuum for Remote Patient Monitoring. IEEE Computer Magazine. Special issue on Computing in Telemedicine.
16. Nikolov, N.; Matskin, M.; Prodan, R.; Roman, D.; Sahin, B.; Bussler, C.; Soylu, A. Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines. Sensors 2023, 23, 564. https://doi.org/10.3390/s23020564.
17. Gorton, I., Khomh, F., Lenarduzzi, V., Menghi, C., Roman, D. (2023). Software Architectures for AI Systems: State of Practice and Challenges. In: Pelliccione, P., Kazman, R., Weber, I., Liu, A. (eds) Software Architecture. Springer, Cham. https://doi.org/10.1007/978-3-031-36847-9_2.
18. Omer Bobrowski, Primoz Skraba. Cluster Persistence for Weighted Graphs, Entropy 2023
19. V. Alexiev, B. Bechev and A. Ositsyn. The InnoGraph Artificial Intelligence Taxonomy Full-Length White Paper. White paper, 2023
20. Nicholas E. Souter, Loïc Lannelongue, Gabriella Samuel. Ten recommendations for reducing the carbon footprint of AI research 10.1162/imag_a_00043
21. Khan, M. Matskin, R. Prodan, C. Bussler, D. Roman, A. Soylu (2024) Could storage cost: A taxonomy and survey. Springer https://link.springer.com/article/10.1007/s11280-024-01273-4
22. De Paoli, M. Ciavotta, R. Avogadro, E. Hristov, M. Borukova, D. Petrova-Antonova, I. Krasteva (2024) An Interactive Approach to Semantic Enrichment with Geospatial Data. Data & Knowledge Engineering
23. C. Liang, H. Jiang, S. Yang, P. Tian, X. Ma, Z. Tang, H. Wang, W. Wang Characterizing Street Trees In Three Metropolises Of Central China By Using Street View Data: From Individual Trees To Landscape Mapping Ecological Informatics. 10.1016/j.ecoinf.2024.102480
24. B. Spahiu, M. Palmonari, R. A. Alva Principe, A. Rula. Understanding the structure of knowledge graphs with ABSTAT profiles, https://boa.unimib.it/retrieve/776bee8d-1a66-444b-93bb-3e183a682cf2/Spahiu-2023-Seman%20Web-AAM.pdf
25. T. Liang, F. Wang, S. Wang, K. Li, X. Ma, X. Mo Towards Unified Aleatory And Epistemic Uncertainty Quantification For Machinery Health Prognostic Through Sequential Heteroscedastic Gaussian Process Regression. Advanced Engineering Informatics 10.1016/j.aei.2024.102719.
26. D. Lu, F. Wang, S. Wang, K. Bu, K. Li, X. Ma Multi-mode Froth Flotation Process Operating Performance Assessment Based on Deep Embedded Graph Clustering Network IEEE Transactions on Industrial Informatics 10.1109/TII.2024.3384616
27. M. Waszak, T. Moen, A.H. Hansen, G. Bouquet, A. Pultier, X. Ma, D. Roman Vibration Sensors for Detecting Critical Events: A Case Study in Ferrosilicon Production. IEEE Access 10.1109/ACCESS.2024.3356067
28. Gultekin, A. Globo, A. Zugarini, M. Ernandes, Leonardo Rigutini (2024) An energy-based comparative analysis of common approaches to text classification in the Legal domain. The 4th International Conference on NLP & Text Mining (NLTM 2024)
29. E. Hristov, D. Petrova-Antonova, F. De Paoli, I. Krasteva, M. Ciavotta, R. Avogadro (2024) Geospatial Data Enrichment through Address Geocoding: Challenges and Solutions. ISPRS Technical Commission IV Symposium 2024
30. Eliassen, R. Selvan (2024) Activation Compression of Graph Neural Networks Using Block-Wise Quantization with Improved Variance Minimization. International Conference on Acoustics, Speech and Signal Processing (ICASSP-2024).https://doi.org/10.1109/ICASSP48485.2024.10446393
31. Bakhtiarifard, C. Igel, R. Selvan (2024) EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Search. International Conference on Acoustics, Speech and Signal Processing (ICASSP-2024) https://doi.org/10.1109/ICASSP48485.2024.10448303
32. H.T. Mai, C.X. Chu, H. Paulheim Do LLMs Really Adapt to Domains? An Ontology Learning Perspective ISWC 2024 https://doi.org/10.48550/arXiv.2407.19998
33. F. Moiraghi, M. Palmonari, D. Allavena, F. Morando Zero-Shot Hierarchical Classification on the Common Procurement Vocabulary Taxonomy COMPSAC n/a https://arxiv.org/pdf/2405.09983
34. I. Jayawardene, D. Roman, Y. Zhao, A.G. Ulyashin, A. Soylu, X. Ma Towards an Open Energy Management System for Integrated Energy Storage and Electric Vehicle Fast Charging Station. Companion Proceedings of the 8th International Joint Conference on Rules and Reasoning (RuleML+RR-Companion 2024)
35. Z. Liu, Q. Wang, J. Dang, X. Wang, K. Li, R. Avogadro, D. Roman, X. Ma Developing Deep Learning based Intelligent Perception Algorithm for Thickener Equipment Monitoring. Companion Proceedings of the 8th International Joint Conference on Rules and Reasoning (RuleML+RR-Companion 2024)
36. R. Avogadro, I. Jayawardene, X. Ma, A. Soylu, D. Roman Koala-UI: An Interactive User Interface for Tabular Data Linking. Companion Proceedings of the 8th International Joint Conference on Rules and Reasoning (RuleML+RR-Companion 2024)
37. Y. Zhu, N. Potyka, M. Nayyeri, B. Xiong, Y. He, E. Kharlamov, S. Staab. Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction EMNLP 2024 https://doi.org/10.48550/arXiv.2408.08226
38. D. Wright, C. Igel, R. Selvan. BMRS: Bayesian Model Reduction for Structured Pruning. 38th Annual Conference on Neural Information Processing Systems (NeurIPS) https://doi.org/10.48550/arXiv.2406.01345
39. F. De Paoli, R. Avogadro, M. Ripamonti, M. Palmonari. Interactive Enrichment of Tabular Data with SemTUI. ISWC 2024
40. R. Avogadro, F. De Paoli, M. Palmonari, D. Roman. Semantic Data Enrichment: from Interactive Exploration to Scalable Deployment Extended Semantic Web Conference
41. E. Motamedi, I. Novalija, L. Rei Classification of Patents Into Knowledge Fields: Using a Proposed Knowledge Mapping Taxonomy (KnowMap) SiKDD2024
42. Makovec, Rei, Novalija Preparing AI for Compliance: Initial Steps of a Framework for Teaching LLMs to Reason About Compliance. Declarative AI 2024
43. S. Gultekin, A. Globo, A. Zugarini, M. Ernandes An energy-based comparative analysis of common NLP models 10.5121/csit.2024.140203
44. Andrew Zamai, Leonardo Rigutini, Marco Maggini, Andrea Zugarini. SLIMER-IT: Zero-Shot NER on Italian Language, https://doi.org/10.48550/arXiv.2409.15933
45. Northern Lights Deep Learning Conference https://doi.org/10.48550/arXiv.2403.12562
46. R. Pozzi, V. Barbera, R. A. Principe, D. Giardini, R. Rubini, M. Palmonari (2024) Combining Knowledge Graphs and NLP to Analyze Instant Messaging Data in Criminal Investigations Web Information Systems Engineering https://doi.org/10.1007/978-981-96-0567-5_30
47. Nico Potyka, Yuqicheng Zhu, Yunjie He, Evgeny Kharlamov, Steffen Staab (2024) Robust Knowledge Extraction from Large Language Models using Social Choice Theory, https://doi.org/10.48550/arXiv.2312.14877
48. R. Selvan, B. Pepin, C. Igel, G. Samuel, E. (2025) Dam PePR: Performance Per Resource Unit as a Metric to Promote Small-scale Deep Learning in Medical Image Analysis
49. T. Chen, R. Selvan (2025) Is Adversarial Training with Compressed Datasets Effective? Scandinavian Conference on Image Analysis, Doi: 10.48550/arXiv.2402.05675
50. N. Inie, J. Olsen, R. Selvan (2025) How Co2stly Is CHI? The Carbon Footprint of Generative AI in HCI Research and What We Should Do About It Conference on Human Factors in Computing Systems (CHI ’25), DOI:10.1145/3706598.3714227
51. Lam, A. N., Avogadro, R, Martin-Recuerda, F., Brian Elvesæter, B., Ma, X., Nystad, E.J., Roman D., and A. J. Berre 𝐓𝐨𝐰𝐚𝐫𝐝𝐬 𝐚 𝐓𝐨𝐨𝐥𝐤𝐢𝐭 𝐟𝐨𝐫 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐈𝐧𝐭𝐞𝐫𝐨𝐩𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐒𝐩𝐚𝐜𝐞𝐬 IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2025) in Emden, Germany. https://www.sintef.no/en/publications/publication/2379502/
52. Wilma Johanna Schmidt, Irlan Grangel-González, Tobias Huschle, Lena Wagner, Evgeny Kharlamov and Adrian Paschke MYAM: LLM-Supported Mapping Generation for Semantic Manufacturing Retrieval ESWC 2025
53. Wilma Johanna Schmidt, Irlan Grangel-González, Tobias Huschle, Lena Wagner, Evgeny Kharlamov and Adrian Paschke LLM-Supported Mapping Generation for Semantic Manufacturing Treasure Hunting ESWC 2025
54. Y. Zhu Thesis Proposal: Uncertainty in Knowledge Graph Embeddings Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop) https://aclanthology.org/2025.naacl-srw.4/
55. Yuqicheng Zhu, Nico Potyka, Jiarong Pan, Bo Xiong, Yunjie He, Evgeny Kharlamov, Steffen Staab Conformalized Answer Set Prediction for Knowledge Graph Embedding NAACL 2025 https://doi.org/10.48550/arXiv.2408.08248
56. Yunjie He, Bo Xiong, Daniel Hernández, Yuqicheng Zhu, Evgeny Kharlamov, Steffen Staab DAGE: DAG Query Answering via Relational Combinator with Logical Constraints WWW 2025 https://doi.org/10.1145/3696410.3714677
57. Zhu, Y., Potyka, N., Pan, J., Xiong, B., He, Y., Kharlamov, E., and S. Staab. Conformalized Answer Set Prediction for Knowledge Graph Embedding, https://doi.org/10.48550/arXiv.2408.08248
58. A. Alidu, M. Ciavotta, F. De Paoli (2025) SemT: A Framework for Enhancing Tabular Data Through Enrichment-as-a-Service ESOCC 2025 10.1007/978-3-031-84617-5_3
59. R. Pozzi, M. Palmonari, A. Coletta, L. Bellomarini, J. Lehmann, S. Vahdati (2025) Scalable Reasoning with Real Facts via Constrained Generation, https://arxiv.org/pdf/2508.16983
60. D. Ghilardi, F. Belotti, M. Molinari, T. Ma, M. Palmonari (2025) Efficient Training of Sparse Autoencoders for Large Language Models via Layer Groups
61. R. Agazzi, R. Alva Principe, R. Pozzi, M. Ripamonti, M. Palmonari (2025) DAVE: A Framework for Assisted Analysis of Document Collections in Knowledge-Intensive Domains
62. M. Cremaschi, F. Belotti, J. D’Souza, M. Palmonari (2025) MammoTab 25: A Large-Scale Dataset for Semantic Table Interpretation – Training, Testing, and Detecting Weaknesses
63. C. Bono, F. Belotti, M. Palmonari (2025) Efficient Uncertainty Estimation for LLM-based Entity Linking in Tabular Data Ontology Matching Workshop, colocated with ISWC 2025
64. A. Alidu, M. Ciavotta, F. De Paoli (2025) Prompt2DAG: A Modular Prompting Approach for Democratizing Data Pipeline Generation IEEE SSE 2025, DOI: 10.1109/SSE67621.2025.00010
65. Andrew Zamai, Andrea Zugarini, Leonardo Rigutini, Marco Ernandes, Marco Maggini (2025) Show Less, Instruct More: Enriching Prompts with Definitions and Guidelines for Zero-Shot NER IJCNN2025, https://arxiv.org/abs/2407.01272
66. D. Wright, G. Samuel, C. Igel, R. Selvan Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI Communications of ACM https://doi.org/10.48550/arXiv.2309.02065
67. N. Novalija, D. Roman, F. Belotti, V. Alexiev, L. Rei, R. Avogadro, B. Khalilvandian, B. Bechev, C. Chinie, I. Ciurea, J. Brank, C. Udroiu, A. Soylu, M. Palmonari (2025) From Code to Concept: A Semantic Approach to AI Innovation Discovery in Open Source Software Repositories
68. Dunja Mladenić, D., Grobelnik, M. Classification of patents into knowledge fields: using a proposed knowledge mapping taxonomy (KnowMap) https://is.ijs.si/wp-content/uploads/2024/11/IS2024_Volume-C-1.pdf