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Prof. Dr. Hans Friedrich Witschel

Prof. Dr. Hans Friedrich Witschel

Activities at FHNW

Deputy Programme Head, Master of Science in Business Information Systems
Deputy Programme Head, Master of Science in Medical Informatics
Researcher in the Intelligent Information Systems Research Group

Student Projects supervised by Prof. Dr. Hans Friedrich Witschel

PhD Projects supervised by Prof. Dr. Hans Friedrich Witschel

Only peer reviewed

No peer reviewed content available
Peer reviewed

Pande, C., Witschel, H. F., & Martin, A. (2022). New hybrid techniques for business recommender systems. Applied Sciences, 12(10). https://doi.org/10.3390/app12104804
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

Witschel, H. F., Riesen, K., & Grether, L. (2021). Natural language-based user guidance for knowledge graph exploration: a user study. In R. Cucchiara, A. Fred, & J. Filipe (Eds.), Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (Vol. 1, pp. 95–102). SciTePress. https://doi.org/10.5220/0010640500003064
Peer reviewed

Pande, C., Witschel, H. F., Martin, A., & Montecchiari, D. (2021). Hybrid conversational AI for intelligent tutoring systems. In A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, & F. v. Harmelen (Eds.), Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021). Sun SITE, Informatik V, RWTH Aachen. https://doi.org/10.26041/fhnw-6988
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
Witschel, H. F., Diwanji, P., & Hinkelmann, K. (2020). A dialog-based tutoring system for project-based learning in information systems education. In R. Dornberger (Ed.), New trends in business information systems and technology. Digital innovation and digital business transformation (pp. 81–95). Springer. https://doi.org/10.1007/978-3-030-48332-6_6
Peer reviewed

Witschel, H. F., Riesen, K., & Grether, L. (2020). KvGR: A graph-based interface for explorative sequential question answering on heterogeneous information sources. In J. M. Jose, E. Yilmaz, J. Magalhães, P. Castells, N. Ferro, M. J. Silva, & F. Martins (Eds.), Advances in Information Retrieval. 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14-17, 2020. Proceedings (Vol. 1, pp. 760–773). Springer. https://doi.org/10.1007/978-3-030-45439-5_50
Peer reviewed

Witschel, H. F., Peter, M., Seiler, L., Parlar, S., & Gatziu Grivas, S. (2019). Case model for the RoboInnoCase recommender system for cases of digital business transformation: structuring information for a case of digital change. In J. Bernardino, A. Salgado, & J. Filipe (Eds.), ICEIS 2019. 21st International Conference on Enterprise Information Systems. Proceedings (Vol. 3, pp. 62–73). SciTePress. https://doi.org/10.5220/0008064900620073
Peer reviewed

Inglese, T., von Kutzschenbach, M., & Witschel, H. F. (2019). Enhancing reflective practices within business management education: what kinds of e-learning scenarios can be designed? IC4E ’19. 10th International Conference on E-Education, E-Business, E-Management and E-Learning, 108–113. https://doi.org/10.1145/3306500.3306580
Peer reviewed

Witschel, H. F., & Martin, A. (2019). Learning and engineering similarity functions for business recommenders. In A. Martin, K. Hinkelmann, A. Gerber, D. Lenat, F. v. 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-6789
Peer reviewed

Diwanji, P., Hinkelmann, K., & Witschel, H. F. (2018). Enhance classroom preparation for flipped classroom using AI and analytics. In S. Hammoudi, M. Smialek, O. Camp, & J. Filipe (Eds.), ICEIS 2018. 20th International Conference on Enterprise Information Systems. Proceedings (Vol. 1, pp. 477–483). SciTePress. https://doi.org/10.5220/0006807604770483
Peer reviewed

Rudolf von Rohr, C., Witschel, H. F., & Martin, A. (2018). Training and re-using human experience: a recommender for more accurate cost estimates in project planning. IC3K 2018 - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 52–62. https://doi.org/10.5220/0006893200520062
Peer reviewed

Witschel, H. F., & Martin, A. (2018). Random walks on human knowledge: incorporating human knowledge into data-driven recommenders. In J. Bernardino, A. Salgado, & J. Filipe (Eds.), IC3K 2018. 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. Proceedings (Vol. 3, pp. 61–70). https://doi.org/10.5220/0006893900630072
Thönssen, B., Witschel, H. F., & Rusinov, O. (2018). Determining information relevance based on personalization techniques to meet specific user needs. In R. Dornberger (Ed.), Business information systems and technology 4.0 - New trends in the age of digital change (pp. 31–45). Springer. https://doi.org/10.1007/978-3-319-74322-6_3
Peer reviewed

Ferrari, A., Witschel, H. F., Spagnolo, G. O., & Gnesi, S. (2018). Improving the quality of business process descriptions of public administrations: resources and research challenges. Business Process Management Journal, 24(1), 49–66. https://doi.org/10.26041/fhnw-1307
Gatziu Grivas, S., & Witschel, H. F. (2017). Business Analytics aus der Cloud: Möglichkeiten und Herausforderungen. In M. K. Peter (ed.), KMU-Transformation - Als KMU die Digitale Transformation erfolgreich umsetzen (1 ed., pp. 182–187). https://doi.org/10.26041/fhnw-1309
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
Peer reviewed

Emmenegger, S., Thönssen, B., Hinkelmann, K., & Witschel, H. F. (2016). KPIs 4 Workplace Learning. In F. Ana & D. Aveiro (Eds.), Proceedings of the 8th International Conference on Knowledge Management and Information Sharing (KMIS) (p. 3). Springer. https://doi.org/10.26041/fhnw-1011
Peer reviewed

Sanne, U., Ferrari, A., Gnesi, S., & Witschel, H. F. (2016). Ensuring Action: Identifying Unclear Actor Specifications in Textual Business Process Descriptions. In F. Ana & D. Aveiro (Eds.), Proceedings of the 8th International Conference on Knowledge Management and Information Sharing (KMIS), 2016. Springer. https://doi.org/10.26041/fhnw-1003
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

Riesen, K., Witschel, H. F., & Galliè, E. (2015). A Graph-Based Recommender for Enhancing the Assortment of Web Shops. Proceedings of Workshop on Data Mining in Marketing DMM′2015. https://doi.org/10.26041/fhnw-52
Peer reviewed

von Bergen, P., Hinkelmann, K., & Witschel, H. F. (2015). Adapting an Enterprise Architecture for Business Intelligence. 8th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modelling. https://doi.org/10.26041/fhnw-2820
Peer reviewed

Schweizer, D., Zehnder, M., Wache, H., Zanatta, D., Rodriguez, M., & Witschel, H. F. (2015). Using consumer behavior data to reduce energy consumption in smart homes. Proceedings of the IEEE 2015 International Conference on Machine Learning and Applications. https://doi.org/10.26041/fhnw-1004
Peer reviewed

Witschel, H. F., Riesen, K., & Loo, S. (2015). How to Support Customer Segmentation with Useful Cluster Descriptions. Proc. Of Industrial Conference on Data Mining ICDM′15, 2015. https://doi.org/10.26041/fhnw-58
Peer reviewed

Emmenegger, S., Lutz, J., Witschel, H. F., & Martin, A. (2015). A new Retrieval Function for Ontology-Based Complex Case Descriptions. Proceedings of CBR-MD′15, 2015. https://doi.org/10.26041/fhnw-57
Peer reviewed

Zehnder, M., Wache, H., Witschel, H. F., Zanatta, D., & Rodriguez, M. (2015). Energy saving in smart homes based on consumer behavior: A case study. First IEEE International Smart Cities Conference (ISC2-2015), 1–6. https://doi.org/10.26041/fhnw-2786
Witschel, H. F. (2014, October 1). Führung. Verwenden Sie die richtigen Kennzahlen? KMU Business World. http://hdl.handle.net/11654/9257
Witschel, H. F., Emmenegger, S., Czekala, T., & Rossi, A. (2014, June 26). Index für den IT-Markt. Unternehmer Zeitung, 30–31. https://doi.org/10.26041/fhnw-2917
Peer reviewed

Thönssen, B., Witschel, H. F., & Lutz, J. (2014). Where Did I(t) put it? A holistic solution to the automatic construction of topic trees for navigation. Proceedings of KMIS′14, 2014. https://doi.org/10.26041/fhnw-53
Witschel, H. F. (2013, April 3). Data-Mining im KMU - Das Beste aus Daten. Unternehmer Zeitung, 36–37. https://doi.org/10.26041/fhnw-3155
Peer reviewed

Lutz, J., Thönssen, B., & Witschel, H. F. (2013). An approach for coping with risks in contract management. Knowledge Engineering and Knowledge Management, IC3K 2012. http://hdl.handle.net/11654/10838
Peer reviewed

Lutz, J., Thönssen, B., & Witschel, H. F. (2013). Breaking free from your information prison - A recommender based on semantically enriched context descriptions. Proceedings of the First International Conference on Enterprise Systems, 2013. https://doi.org/10.26041/fhnw-2803
Peer reviewed

Mühleisen, B., Schneider, C., Witschel, H. F., & Gutknecht, C. (2013). Success Factors of Enterprise 2.0. Proceedings of I-KNOW 2013. https://doi.org/10.26041/fhnw-2801
Peer reviewed

Rivera-Pelayo, V., Braun, S., Riss, U. V., Witschel, H. F., & Hu, B. (2013). Building Expert Recommenders from Email-Based Personal Social Networks (Vol. 6). Springer. https://doi.org/10.26041/fhnw-2800
Hinkelmann, K., & Witschel, H. F. (2012). Auswahl der richtigen Wissensmanagement-Methoden. Blickpunkt KMU. https://doi.org/10.26041/fhnw-2999
Peer reviewed

Hinkelmann, K., Witschel, H. F., & Nguyen, T. Q. (2012). Learning Business Rules for Adaptive Process Models. BUSTECH 2012 - the Second International Conference on Business Intelligence and Technology. http://hdl.handle.net/11654/10774
Peer reviewed

Brander, S., Hinkelmann, K., Hu, B., Martin, A., Riss, U., Thönssen, B., & Witschel, H. F. (2011). Refining process models through the analysis of informal work practice. 9th International Conference on Business Process Management (BPM 2011). http://hdl.handle.net/11654/9162
Peer reviewed

Witschel, H. F., Hu, B., Riss, U. V., & Thönssen, B. (2010). A Collaborative Approach To Maturing Process-Related Knowledge. In R. Hull, J. Mendling, & S. Tai (Eds.), Business Process Management - Lecture Notes in Computer Science (Vol. 6336). https://doi.org/10.26041/fhnw-2805
Peer reviewed

Witschel, H. F., Leidig, T., Kaufmann, V., Ostertag, M., Brecht, U., & Grebner, O. (2010). Optimisation of Knowledge Work in the Public Sector by Means of Digital Metaphors. Proceedings of I-KNOW 2010. https://doi.org/10.26041/fhnw-2802
Peer reviewed

Ramezani, M., & Witschel, H. F. (2010). An Intelligent System for Semi-Automatic Evolution of Ontologies. IEEE Conference on Intelligent Systems, 2010. http://hdl.handle.net/11654/6341
Peer reviewed

Ramezani, M., Witschel, H. F., Braun, S., & Zacharias, V. (2010). Using Machine Learning to Support Continuous Ontology Development. Proceedings of 17th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2010. https://doi.org/10.26041/fhnw-2804
Riss, U., Witschel, H. F., Brun, R., & Thönssen, B. (2009). What is Organizational Knowledge Maturing and how can it be assessed? Proceedings of I-KNOW ’09 and I-SEMANTICS ’09, 28–38. https://doi.org/10.26041/fhnw-3202

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