Hinkelmann, Knut
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Hinkelmann, Knut
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- PublikationDevelopment of fake news model using machine learning through natural language processing(World Academy of Science, Engineering and Technology, 2020) Hinkelmann, Knut [in: International Journal of Computer and Information Engineering]Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.01B - Beitrag in Magazin oder Zeitung
- PublikationAgile visualization in design thinking(Springer, 2020) Hinkelmann, Knut [in: New trends in business information systems and technology]This chapter presents an agile visualization approach that supports one of the most widespread innovation processes: Design Thinking. The approach integrates the pre-defined graphical elements of SAP Scenes to sketch digital scenes for storyboards. Unforeseen scenarios can be created by accommodating new graphical elements and related domain-specific aspects on-the-fly. This fosters problem understanding and ideation, which otherwise would be hindered by the lack of elements. The symbolic artificial intelligence (AI)-based approach ensures the machineinterpretability of the sketched scenes. In turn, the plausibility check of the scenes is automated to help designers creating meaningful storyboards. The plausibility check includes the use of a domain ontology, which is supplied with semantic constraints. The approach is implemented in the prototype AOAME4Scenes, which is used for evaluation.04A - Beitrag Sammelband
- PublikationVisualization of patterns for hybrid learning and reasoning with human involvement(Springer, 2020) Hinkelmann, Knut [in: New trends in business information systems and technology]04A - Beitrag Sammelband
- PublikationA dialog-based tutoring system for project-based learning in information systems education(Springer, 2020) Hinkelmann, Knut [in: New trends in business information systems and technology. Digital innovation and digital business transformation]04A - Beitrag Sammelband
- PublikationOntology-based visualization for business model design(Springer, 2020) Hinkelmann, Knut [in: The Practice of Enterprise Modeling. 13th IFIP Working Conference, PoEM 2020, Riga, Latvia, November 25-27, 2020. Proceedings]The goal of this paper is to demonstrate the feasibility of combining visualization and reasoning for business model design by combining the machine-interpretability of ontologies with a further development of the widely accepted business modeling tool, the Business Model Canvas (BMC). Since ontologies are a machine-interpretable representation of enterprise knowledge and thus, not very adequate for human interpretation, we present a tool that combines the graphical and human interpretable representation of BMC with a business model ontology. The tool connects a business model with reusable data and interoperability to other intelligent business information systems so that additional functionalities are made possible, such as a comparison between business models. This research follows the design science strategy with a qualitative approach by applying literature research, expert interviews, and desk research. The developed AOAME4BMC tool consists of the frontend, a graphical web-based representation of an enhanced BMC, a web service for the data exchange with the backend, and a speci c ontology for the machine-interpretable representation of a business model. The results suggest that the developed tool AOAME4BMC supports the suitability of an ontology-based representation for business model design.04B - Beitrag Konferenzschrift
- PublikationTowards AI-based solutions in the system development lifecycle(2020) Jüngling, Stephan; Peraic, Martin; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard; van Harmelen, Frank [in: Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020)]Many teams across different industries and organizations explicitly apply agile methodologies such as Scrum in their system development lifecycle (SDLC). The choice of the technology stack, the programming language, or the decision whether AI solutions could be incorporated into the system design either is given by corporate guidelines or is chosen by the project team based on their individual skill set. The paper describes the business case of implementing an AI-based automatic passenger counting system for public transportation, shows preliminary results of the prototype using anonymous passenger recognition on the edge with the help of Google Coral devices.It shows how different solutions could be integrated with the help of rule base systems and how AI-based solutions could be established in the SDLC as valid and cost-saving alternatives to traditionally programmed software components.04B - Beitrag Konferenzschrift
- PublikationCombining symbolic and sub-symbolic AI in the context of education and learning(2020) Telesko, Rainer; Jüngling, Stephan; Gachnang, Phillip; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard; van Harmelen, Frank [in: Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020)]Abstraction abilities are key to successfully mastering the Business Information Technology Programme (BIT) at the FHNW (Fachhochschule Nordwestschweiz). Object-Orientation (OO) is one example - which extensively requires analytical capabilities. For testing the OO-related capabilities a questionnaire (OO SET) for prospective and 1st year students was developed based on the Blackjack scenario. Our main target of the OO SET is to identify clusters of students which are likely to fail in the OO-related modules without a substantial amount of training. For the interpretation of the data the Kohonen Feature Map (KFM) is used which is nowadays very popular for data mining and exploratory data analysis. However, like all sub-symbolic approaches the KFM lacks to interpret and explain its results. Therefore, we plan to add - based on existing algorithms - a “postprocessing” component which generates propositional rules for the clusters and helps to improve quality management in the admission and teaching process. With such an approach we synergistically integrate symbolic and sub-symbolic artificial intelligence by building a bridge between machine learning and knowledge engineering.04B - Beitrag Konferenzschrift
- PublikationArchiMEO: A standardized enterprise ontology based on the ArchiMate conceptual model(2020) Hinkelmann, Knut [in: Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development]Many enterprises face the increasing challenge of sharing and exchanging data from multiple heterogeneous sources. Enterprise Ontologies can be used to effectively address such challenge. In this paper, we present an Enterprise Ontology called ArchiMEO, which is based on an ontological representation of the ArchiMate standard for modeling Enterprise Architectures. ArchiMEO has been extended to cover various application domains such as supply risk management, experience management, workplace learning and business process as a service. Such extensions have successfully proven that our Enterprise Ontology is beneficial for enterprise applications integration purposes.04B - Beitrag Konferenzschrift
- PublikationReports of the AAAI 2019 Spring Symposium Series(American Association for Artificial Intelligence, 2019) Hinkelmann, Knut [in: AI Magazine]The AAAI 2019 Spring Series was held Monday through Wednesday, March 25–27, 2019 on the campus of Stanford University, adjacent to Palo Alto, California. The titles of the nine symposia were Artificial Intelligence, Autonomous Machines, and Human Awareness: User Interventions, Intuition and Mutually Constructed Context; Beyond Curve Fitting — Causation, Counterfactuals and Imagination-Based AI; Combining Machine Learning with Knowledge Engineering; Interpretable AI for Well-Being: Understanding Cognitive Bias and Social Embeddedness; Privacy- Enhancing Artificial Intelligence and Language Technologies; Story-Enabled Intelligence; Towards Artificial Intelligence for Collaborative Open Science; Towards Conscious AI Systems; and Verification of Neural Networks.01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationDigital Maturity Model - How companies can achieve sustainable competitiveness in the future and the digital age(edition gesowip, 2019) Fluri, Jasmin; Schwaferts, Dino; Verkuil, Arie Hans; Hinkelmann, Knut; Aeschbacher, Marc [in: Digitalisierung und andere Innovationsformen im Management]For companies to be sustainably successful in the digital age, there are certain preconditions they must fulfill to be able to react to customer expectations and offer appropriate products or services competitively. This ability to proactively provide proper products or services within an adequate period is called Digital Maturity. To reach Dig-ital Maturity, companies must establish four preconditions: Customer Knowledge, Strategic Management, Technical Capability, and Cultural Agility. Those preconditions are called pillars of Digital Maturity. This chapter explains, why Digital Maturity concerns the whole company and requires different prerequisites on the leadership- as well as on the technical-, organizational, and cultural side of a company. The Digital Maturity assessment of the University of Applied Sciences and Arts Northwestern Switzerland is introduced and explains how to set up and incrementally perform a Digital Maturity assessment inside a company to continuously improve the companies’ Digital Maturity.04A - Beitrag Sammelband