Hinkelmann, Knut

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Hinkelmann
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Knut
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Hinkelmann, Knut

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2000 - 2009312010 - 2019322020 - 202211
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Gerade angezeigt 1 - 10 von 76
  • Vorschaubild nicht verfügbar
    Publikation
    Fact checking: an automatic end to end fact checking system
    (Springer, 2022) Ahmed, Sajjad; Hinkelmann, Knut; Corradini, Flavio; Lahby, Mohamed; Pathan, Al-Sakib Khan; Maleh, Yassine; Yafooz, Wael Mohamed Shaher [in: Combating fake news with computational intelligence techniques]
    04A - Beitrag Sammelband
  • Vorschaubild nicht verfügbar
    Publikation
    Towards ontology-based validation of EA principles
    (2022) Montecchiari, Devid; Hinkelmann, Knut; Barn, Balbir S.; Sandkuhl, Kurt [in: The Practice of Enterprise Modeling. 15th IFIP WG 8.1 Working Conference, PoEM 2022, London, UK, November 23-25, 2022. Proceedings]
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Proceedings of the Society 5.0 Conference 2022 - Integrating digital world and real world to resolve challenges in business and society
    (EasyChair, 2022) Hinkelmann, Knut; Gerber, Aurona [in: Hinkelmann, K. and Gerber, A. (2022): Proceedings of the Society 5.0 Conference 2022 - Integrating Digital World and Real World to Resolve Challenges in Business and Society. EPiC Series in Computing, Volume 84.]
    03 - Sammelband
  • Vorschaubild nicht verfügbar
    Publikation
    Enterprise maps: zooming in and out of enterprise models
    (SciTePress, 2022) Spahic, Maja; Hinkelmann, Knut; Filipe, Joaquim; Smialek, Michal; Brodsky, Alexander; Hammoudi, Slimane [in: Proceedings of the 24th International Conference on Enterprise Information Systems]
    A company’s architecture can be represented by domain-specific models, which are defined by domain-specific modeling language. Since not all stakeholders are interested in the same models, dedicated views can be created to support navigation through the enterprise models. These views offer a snippet of the entire company and cover stakeholder-specific concerns. The relationships between the different views and models remain hidden and can be unveiled with much effort. The developed concept of the zoomability principle offers the ability to change the degree of detail using zoom in and out of the enterprise model. The different models and modeling languages used to express an enterprise are considered, and a form of navigation is established similar to an online map. The concept is based on two pillars,”Zoom Within” and”Zoom into Complements”. For this purpose, a metamodel was developed, which formalizes the elements used in the concept and their relationships. Developing the artifact, rules were defined that contribute to a generic approach allowing an application to another case. Furthermore, a prototype was developed, representing the zoomability principle and offering the possibility to perform zooming behavior. The artifact was evaluated through a demonstration. An additional prototype was created to demonstrate that the developed concept can be applied to a predefined set of situations.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Fact checking: detection of check worthy statements through support vector machine and feed forward neural network
    (Springer, 2021) Ahmed, Sajjad; Balla, Klestia; Hinkelmann, Knut; Corradini, Flavio; Arai, Kohei [in: Advances in information and communication. Proceedings of the 2021 Future of Information and Communication Conference (FICC)]
    Detection of check-worthy statements is a subtask in the fact-checking process, automation of which would decrease the time and burden required to fact-check a statement. This paper proposes an approach focused on the classification of statements into check-worthy and not check-worthy. For the current paper, a dataset is constructed by consulting different fact-checking organizations. It contains debates and speeches in the domain of politics. Thus, even the ability of check worthy approach is evaluated on this domain. It starts with extracting sentence-level and context features from the sentences, and classifying them based on these features. The feature set and context were chosen after several experiments, based on how well they differentiate check-worthy statements. The findings indicated that the context in the approach gives considerable contribution in the classification, while also using more general features to capture information from the sentences. The results were analyzed by examining all features used, assessing their contribution in classification, and how well the approach performs in speeches and debates separately to detect the check worthy statements to reduce the time and burden of fact checking process.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Agile visualization in design thinking
    (Springer, 2020) Laurenzi, Emanuele; Hinkelmann, Knut; Montecchiari, Devid; Goel, Mini; Dornberger, Rolf [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
  • Vorschaubild nicht verfügbar
    Publikation
    Ontology-based visualization for business model design
    (Springer, 2020) Peter, Marco; Montecchiari, Devid; Hinkelmann, Knut; Gatziu Grivas, Stella; Grabis, Jānis; Bork, Dominik [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
  • Vorschaubild nicht verfügbar
    Publikation
    ArchiMEO: A standardized enterprise ontology based on the ArchiMate conceptual model
    (2020) Hinkelmann, Knut; Laurenzi, Emanuele; Martin, Andreas; Montecchiari, Devid; Spahic, Maja; Thönssen, Barbara; Hammoudi, Slimane; Ferreira Pires, Luis; Selić, Bran [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
  • Vorschaubild nicht verfügbar
    Publikation
    Visualization of patterns for hybrid learning and reasoning with human involvement
    (Springer, 2020) Witschel, Hans Friedrich; Pande, Charuta; Martin, Andreas; Laurenzi, Emanuele; Hinkelmann, Knut; Dornberger, Rolf [in: New trends in business information systems and technology]
    04A - Beitrag Sammelband
  • Vorschaubild nicht verfügbar
    Publikation
    Development of fake news model using machine learning through natural language processing
    (World Academy of Science, Engineering and Technology, 2020) Ahmed, Sajjad; Hinkelmann, Knut; Corradini, Flavio [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