Hinkelmann, Knut

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Hinkelmann
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Knut
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Hinkelmann, Knut

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2013 - 20197
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Autor:in: Gerber, Aurona × Enddatum: 2019 × Anfangsdatum: 2013 × Dateien dazu vorhanden?: Ja ×

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Gerade angezeigt 1 - 7 von 7
  • Vorschaubild
    Publikation
    Virtual bartender: a dialog system combining data-driven and knowledge-based recommendation
    (2019) Hinkelmann, Knut [in: Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)]
    This research is about combination of data-driven and knowledge-based recommendations The research is made in an application scenario for whisky recommendation, where a guest chats with a recommender system. Preferences about taste are difficult to express and the knowledge about taste is tacit and thus can hardly be represented and used adequately. People or not aware of how to describe flavors in a standardized way and how to do a justified choice. This is because knowledge about taste is mainly tacit knowledge. To deal with this knowledge, data-driven recommendation is adequate. On the other hand, in particular experienced customers use knowledge about distilleries, locations and the distillery process to express their preferences and want to have arguments for the recommended products. This shows that a combination of data-driven and knowledge-based recommendations is appropriate in areas where tacit knowledge and explicit knowledge are available.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Learning and engineering similarity functions for business recommenders
    (2019) Witschel, Hans Friedrich; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; Harmelen, Frank van; Clark, Peter [in: Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)]
    We study the optimisation of similarity measures in tasks where the computation of similarities is not directly visible to end users, namely clustering and case-based recommenders. In both, similarity plays a crucial role, but there are also other algorithmic components that contribute to the end result. Our suggested approach introduces a new form of interaction into these scenarios that make the use of similarities transparent to end users and thus allows to gather direct feedback about similarity from them. This happens without distracting them from their goal – rather allowing them to obtain better and more trustworthy results by excluding dissimilar items. We then propose to use the feedback in a way that incorporates machine learning for updating weights and decisions of knowledge engineers about possible additional features, based on insights derived from a summary of user feedback. The reviewed literature and our own previous empirical investigations suggest that this is the most feasible way – involving both machine and human, each in a task that they are particularly good at.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Towards an assistive and pattern learning-driven process modeling approach
    (2019) Hinkelmann, Knut [in: Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)]
    The practice of business process modeling not only requires modeling expertise but also significant domain expertise. Bringing the latter into an early stage of modeling contributes to design models that appropriately capture an underlying reality. For this, modeling experts and domain experts need to intensively cooperate, especially when the former are not experienced within the domain they are modeling. This results in a time-consuming and demanding engineering effort. To address this challenge, we propose a process modeling approach that assists domain experts in the creation and adaptation of process models. To get an appropriate assistance, the approach is driven by semantic patterns and learning. Semantic patterns are domain-specific and consist of process model fragments (or end-to-end process models), which are continuously learned from feedback from domain as well as process modeling experts. This enables to incorporate good practices of process modeling into the semantic patterns. To this end, both machine-learning and knowledge engineering techniques are employed, which allow the semantic patterns to adapt over time and thus to keep up with the evolution of process modeling in the different business domains.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Leverage white-collar workers with AI
    (2019) Jüngling, Stephan; Hofer, Angelin; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; Clark, Peter [in: Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)]
    Based on the example of automated meeting minutes taking, the paper highlights the potential of optimizing the allocation of tasks between humans and machines to take the particular strengths and weaknesses of both into account. In order to combine the functionality of supervised and unsupervised machine learning with rule-based AI or traditionally programmed software components, the capabilities of AI-based system actors need to be incorporated into the system design process as early as possible.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Combining machine learning with knowledge engineering to detect fake news in social networks - A survey
    (2019) Hinkelmann, Knut [in: Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)]
    Due to extensive spread of fake news on social and news media it became an emerging research topic now a days that gained attention. In the news media and social media the information is spread highspeed but without accuracy and hence detection mechanism should be able to predict news fast enough to tackle the dissemination of fake news. It has the potential for negative impacts on individuals and society. Therefore, detecting fake news on social media is important and also a technically challenging problem these days. We knew that Machine learning is helpful for building Artificial intelligence systems based on tacit knowledge because it can help us to solve complex problems due to real word data. On the other side we knew that Knowledge engineering is helpful for representing experts knowledge which people aware of that knowledge. Due to this we proposed that integration of Machine learning and knowledge engineering can be helpful in detection of fake news. In this paper we present what is fake news, importance of fake news, overall impact of fake news on different areas, different ways to detect fake news on social media, existing detections algorithms that can help us to overcome the issue, similar application areas and at the end we proposed combination of data driven and engineered knowledge to combat fake news. We studied and compared three different modules text classifiers, stance detection applications and fact checking existing techniques that can help to detect fake news. Furthermore, we investigated the impact of fake news on society. Experimental evaluation of publically available datasets and our proposed fake news detection combination can serve better in detection of fake news.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    A new paradigm for the continuous alignment of business and IT: combining enterprise architecture modelling and enterprise ontology
    (Elsevier, 2016) Hinkelmann, Knut [in: Computers in Industry]
    The paper deals with Next Generation Enterprise Information Systems in the context of Enterprise Engineering. The continuous alignment of business and IT in a rapidly changing environment is a grand challenge for today’s enterprises. The ability to react timeously to continuous and unexpected change is called agility and is an essential quality of the modern enterprise. Being agile has consequences for the engineering of enterprises and enterprise information systems. In this paper a new paradigm for next generation enterprise information systems is proposed, which shifts the development approach of model-driven engineering to continuous alignment of business and IT for the agile enterprise. It is based on a metamodelling approach, which supports both human-interpretable graphical enterprise architecture and machine-interpretable enterprise ontologies. Furthermore, next generation enterprise information systems are described, which embed modelling tools and algorithms for model analysis.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Connecting Enterprise Architecture and Information Objects Using an Enterprise Ontology
    (IEEE, 11/2013) Hinkelmann, Knut [in: International Conference on Enterprise Systems ES2013]
    In this paper we show how semantic metadata – derived from the enterprise architecture description - can improve both the exploitation of information in the operative business and the continuous alignment of information systems with the business. ArchiMEO, a formal representation of the ArchiMate framework, was applied to model selected parts of a large Swiss company, including metadata of concrete information objects stored in various applications. The evaluation of our approach clearly gave evidence that linking an enterprise ontology with operational databases is beneficiary: it provides an integrated view and management of enterprise entities spread over various data stores, represented in different ways and levels of granularity. Furthermore, because of its inference capabilities, when used as metadata encoding scheme an enterprise ontology can provide context-sensitive access to relevant information.
    04 - Beitrag Sammelband oder Konferenzschrift