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Dr. Emanuele Laurenzi

Dr. Emanuele Laurenzi

Tätigkeiten an der FHNW

Dozentin und leitende Forscherin für intelligente und innovative Informationssysteme
Leiter des CAS Design Thinking for Artificial Intelligence, Leiter des ImpactLab.

Betreute Studienprojekte von Dr. Emanuele Laurenzi

Nur peer-reviewed

Keine peer-reviewed Inhalte verfügbar
Peer-reviewed

Buchmann, R., Eder, J., Fill, H.-G., Frank, U., Karagiannis, D., Laurenzi, E., Mylopoulos, J., Plexousakis, D., & Santos, M. Y. (2024). Large language models: Expectations for semantics-driven systems engineering. Data & Knowledge Engineering, 152, 102324. https://doi.org/10.1016/j.datak.2024.102324
Laurenzi, E., Meyer, D., & Moesch, P. (2024). A decision-support approach for university incubators. In K. Hinkelmann & H. Smuts (Eds.), Society 5.0. 4th International Conference, Society 5.0 2024, Moka, Mauritius, June 26–28, 2024, Revised Selected Papers (pp. 218–228). Springer. https://doi.org/10.1007/978-3-031-71412-2_16
Peer-reviewed

Laurenzi, E., Mathys, A., & Martin, A. (2024). An LLM-aided Enterprise Knowledge Graph (EKG) engineering process. In R. Petrick & C. Geib (Eds.), Proceedings of the AAAI 2024 Spring Symposium Series: Vol. 3(1) (pp. 148–156). Stanford University. https://doi.org/10.1609/aaaiss.v3i1.31194
Peer-reviewed

Laurenzi, E., Allan, J., Campos Macias-Hammel, N., & Stoller, S. (2024). An ontology-based meta-modelling approach for semantic-driven building management systems. In J. P. A. Almeida, C. Di Ciccio, & C. Kalloniatis (Eds.), Advanced information systems engineering workshops (pp. 200–211). Springer. https://doi.org/10.1007/978-3-031-61003-5_18
Peer-reviewed

Düggelin, W., & Laurenzi, E. (2024). A knowledge graph-based decision support system for resilient supply chain networks. In J. Araújo, J. L. de la Vara, M. Y. Santos, & S. Assar (Eds.), Research challenges in information science (Vol. 1, pp. 66–81). Springer. https://doi.org/10.1007/978-3-031-59465-6_5
Witschel, H. F., Pande, C., Martin, A., Laurenzi, E., & Hinkelmann, K. (2023). Visualisierung von Mustern für hybrides Lernen und Reasoning mit menschlicher Beteiligung. In R. Dornberger (ed.), Neue Trends in Wirtschaftsinformatik und eingesetzte Technologien. Digitale Innovation und digitale Transformation (pp. 205–217). Springer. https://doi.org/10.1007/978-3-031-32538-0_13
Peer-reviewed

Witschel, H. F., Laurenzi, E., Jüngling, S., Kadvany, Y., & Trojan, A. (2023). An early warning system that combines machine learning and a rule-based approach for the prediction of cancer patients’ unplanned visits. In A. Martin, H.-G. Fill, A. Gerber, K. Hinkelmann, D. Lenat, R. Stolle, & F. van Harmelen (Eds.), Proceedings of the AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023). Sun SITE Central Europe. https://irf.fhnw.ch/handle/11654/48276
Peer-reviewed

Rordorf, D. H.-P., Käser, J., Crego Corot, A. E., & Laurenzi, E. (2023). A hybrid intelligent approach combining machine learning and a knowledge graph to support academic journal publishers addressing the Reviewer Assignment Problem (RAP). In A. Martin, H.-G. Fill, A. Gerber, K. Hinkelmann, D. Lenat, R. Stolle, & F. Harmelen (Eds.), Proceedings of the AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023) (pp. 1–19). Sun SITE, Informatik V, RWTH Aachen. https://doi.org/10.26041/fhnw-11149
Peer-reviewed

Schmid, C., Laurenzi, E., Michelucci, U., & Venturini, F. (2023). Explainable AI for the olive oil industry. In K. Hinkelmann, F. J. López-Pellicer, & A. Polini (Eds.), Proceedings of the 22nd International Conference on Business Informatics Research, BIR 2023 (pp. 158–171). Springer. https://doi.org/10.1007/978-3-031-43126-5_12
Peer-reviewed

Mancuso, M. C., & Laurenzi, E. (2023). An approach for knowledge graphs-based user stories in agile methodologies. In K. Hinkelmann, F. J. López-Pellicer, & A. Polini (Eds.), Perspectives in Business Informatics Research. BIR 2023 (pp. 133–141). Springer. https://doi.org/10.1007/978-3-031-43126-5_10
Peer-reviewed

Fedeli, A., Beutling, N., Laurenzi, E., & Polini, A. (2023). Comparison of general-purpose and domain-specific modeling languages in the IoT domain: A case study from the OMiLAB community. In A. Morichetta, R. A. Buchmann, K. Sandkuhl, U. Seigerroth, M. Kirikova, C. Møller, P. Forbrig, A. Gutschmidt, A.-M. Ghiran, A. Marcelletti, F. Härer, B. Re, & B. Johansson (Eds.), Joint Proceedings of the BIR 2023 Workshops and Doctoral Consortium co-located with 22nd International Conference on Perspectives in Business Informatics Research (BIR 2023) (pp. 145–157). Sun SITE, Informatik V, RWTH Aachen. https://doi.org/10.26041/fhnw-11146
Peer-reviewed

Laurenzi, E. (2023). A methodological approach for ontology-based meta-modelling. In A. Morichetta, R. A. Buchmann, K. Sandkuhl, U. Seigerroth, M. Kirikova, C. Møller, P. Forbrig, A. Gutschmidt, A.-M. Ghiran, A. Marcelletti, F. Härer, B. Re, & B. Johansson (Eds.), Joint Proceedings of the BIR 2023 Workshops and Doctoral Consortium co-located with 22nd International Conference on Perspectives in Business Informatics Research (BIR 2023) (pp. 55–64). Sun SITE, Informatik V, RWTH Aachen. https://doi.org/10.26041/fhnw-11145
Peer-reviewed

Sandhu, G., Kilburg, A., Martin, A., Pande, C., Witschel, H. F., Laurenzi, E., & Billing, E. (2022). Practice track: a learning tracker using digital biomarkers for autistic preschoolers. In K. Hinkelmann & A. Gerber (Eds.), Proceedings of the Society 5.0 Conference 2022 - Integrating digital world and real world to resolve challenges in business and society (pp. 219–230). https://doi.org/10.29007/m2jx
Peer-reviewed

Prater, R., & Laurenzi, E. (2022). A hybrid intelligent approach for the support of higher education students in literature discovery. In A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, & F. van Harmelen (Eds.), Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022). https://doi.org/10.26041/fhnw-7307
Peer-reviewed

Egger, N. C., & Laurenzi, E. (2022). Value creation patterns for industry-relevant model-based cyber-physical systems. In L. Ferreira Pires, S. Hammoudi, & E. Seidewitz (Eds.), MODELSWARD 2022. 10th International Conference on Model-Driven Engineering and Software Development. Proceedings (Vol. 1, pp. 364–370). SciTePress. https://doi.org/10.5220/0010984400003119
Witschel, H. F., Pande, C., Martin, A., Laurenzi, E., & Hinkelmann, K. (2020). Visualization of patterns for hybrid learning and reasoning with human involvement. In R. Dornberger (Ed.), New trends in business information systems and technology (pp. 193–204). Springer. https://doi.org/10.1007/978-3-030-48332-6_13
Laurenzi, E., Hinkelmann, K., Montecchiari, D., & Goel, M. (2020). Agile visualization in design thinking. In R. Dornberger (Ed.), New trends in business information systems and technology (pp. 31–47). Springer. https://doi.org/10.1007/978-3-030-48332-6_3
Peer-reviewed

Hinkelmann, K., Laurenzi, E., Martin, A., Montecchiari, D., Spahic, M., & Thönssen, B. (2020). ArchiMEO: A standardized enterprise ontology based on the ArchiMate conceptual model. In S. Hammoudi, L. Ferreira Pires, & B. Selić (Eds.), Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development (Vol. 1, pp. 417–424). https://doi.org/10.5220/0009000204170424
Laurenzi, E. (2020). An agile and ontology-aided approach for domain-specific adaptations of modelling languages [University of Pretoria]. https://irf.fhnw.ch/handle/11654/42970
Peer-reviewed

Laurenzi, E., Hinkelmann, K., Jüngling, S., Montecchiari, D., Pande, C., & Martin, A. (2019). Towards an assistive and pattern learning-driven process modeling approach. In A. Martin, K. Hinkelmann, A. Gerber, D. Lenat, F. van Harmelen, & P. Clark (Eds.), Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019). https://doi.org/10.26041/fhnw-6431
Peer-reviewed

Laurenzi, E., Hinkelmann, K., Izzo, S., Reimer, U., & van der Merwe, A. (2018). Towards an agile and ontology-aided modeling environment for DSML adaptation. Advanced Information Systems Engineering Workshops (CAiSE 2018), 222–234. https://doi.org/10.1007/978-3-319-92898-2_19
Peer-reviewed

Laurenzi, E., Hinkelmann, K., & van der Merwe, A. (2018). An agile and ontology-aided modeling environment. In R. A. Buchmann, D. Karagiannis, & M. Kirikova (Eds.), The Practice of Enterprise Modeling. 11th IFIP WG 8.1 Working Conference, PoEM 2018, Vienna, Austria, October 31-November 2, 2018, proceedings (pp. 221–237). Springer. https://doi.org/10.1007/978-3-030-02302-7_14
Peer-reviewed

Kritikos, K., Laurenzi, E., & Hinkelmann, K. (2018). Towards business-to-IT alignment in the cloud. In Z. Á. Mann & V. Stolz (Eds.), Advances in service-oriented and cloud computing. Workshops of ESOCC 2017, Oslo, Norway, September 27-29, 2017, revised selected papers (pp. 35–52). https://doi.org/10.1007/978-3-319-79090-9_3
Hinkelmann, K., Laurenzi, E., Martin, A., & Thönssen, B. (2018). Ontology-based metamodeling. In R. Dornberger (Ed.), Business information systems and technology 4.0. New trends in the age of digital change (pp. 177–194). Springer. https://doi.org/10.1007/978-3-319-74322-6_12
Peer-reviewed

Hinkelmann, K., Kurjakovic, S., Lammel, B., Laurenzi, E., & Woitsch, R. (2017). A semantically-enhanced modelling environment for business process as a service. In G. Li & Y. Yu (Eds.), 4th International Conference on Enterprise Systems. Advances in Enterprise Systems: ES 2016 (pp. 143–152). IEEE. https://doi.org/10.1109/ES.2016.25
Peer-reviewed

Emmenegger, S., Thönssen, B., Laurenzi, E., Martin, A., Zhang Sprenger, C., Hinkelmann, K., & Witschel, H. F. (2016). An Ontology-based and Case-based Reasoning supported Workplace Learning Approach. Communications in Computer and Information Science. http://hdl.handle.net/11654/24551
Peer-reviewed

Emmenegger, S., Laurenzi, E., Thönssen, B., Zhang Sprenger, C., Hinkelmann, K., & Witschel, H. F. (2016). Workplace Learning - Providing Recommendations of Experts and Learning Resources in a Context-sensitive and Personalized Manner. Proceedings of Special Session on Learning Modeling in Complex Organizations (LCMO) at MODELSWARD′16. https://doi.org/10.26041/fhnw-1012

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