Dr. Emanuele Laurenzi
Dr. Emanuele Laurenzi
Tätigkeiten an der FHNW
- Dozent in den Studiengängen MSc Business Information Systems, MSc Medical Informatics, BSc in Business Information Technology, BSc in International Management, CAS AI for Business Processes
- Wissenschaftlicher Mitarbeiter im Kompetenzschwerpunkt Intelligent Information Systems
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Keine peer-reviewed Inhalte verfügbar
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Peer-reviewedBuchmann, 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
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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
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Peer-reviewedLaurenzi, 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
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Peer-reviewedLaurenzi, 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
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Peer-reviewedDüggelin, 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
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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
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Peer-reviewedRordorf, 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
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Peer-reviewedSchmid, 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
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Peer-reviewedMancuso, 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
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Peer-reviewedFedeli, 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
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Peer-reviewedLaurenzi, 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
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Peer-reviewedWitschel, 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
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Peer-reviewedSandhu, 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
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Peer-reviewedPrater, 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
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Peer-reviewedEgger, 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
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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
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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
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Peer-reviewedHinkelmann, 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
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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
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Peer-reviewedLaurenzi, 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
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Peer-reviewedLaurenzi, 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
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Peer-reviewedLaurenzi, 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
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Peer-reviewedKritikos, 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
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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
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Peer-reviewedHinkelmann, 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
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Peer-reviewedEmmenegger, 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
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Peer-reviewedEmmenegger, 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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Keine peer-reviewed Inhalte verfügbar
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Peer-reviewedLaurenzi, E. (2022). AOAME 4 Society 5.0: Towards the creation and maintenance of knowledge graphs through enterprise modelling. Society 5.0 - Integrating digital world and real world to resolve challenges in business and society. https://irf.fhnw.ch/handle/11654/43264
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Peer-reviewedKurjakovic, S., Lammel, B., Laurenzi, E., Woitsch, R., & Hinkelmann, K. (2016, November 2). A Semantically-Enhanced Modelling Environment for Business Process as a Service. 4th International Conference on Enterprise Systems. http://hdl.handle.net/11654/24288
Kontakt
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Dr. Emanuele Laurenzi
- Dozent, Studiengang MSc Business Information Systems
- Telefonnummer
- +41 62 957 28 26 (Direkt)
- ZW1hbnVlbGUubGF1cmVuemlAZmhudy5jaA==
- Fachhochschule Nordwestschweiz FHNW
Hochschule für Wirtschaft
Riggenbachstrasse 16
4600 Olten