Hinkelmann, Knut

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Hinkelmann
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Knut
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Hinkelmann, Knut

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2011 - 201922
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Gerade angezeigt 1 - 10 von 22
  • 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
    Der Geschäftsprozess in der Cloud
    (Swiss Professional Media, 2018) Hinkelmann, Knut [in: Unternehmer Zeitung]
    CLOUD-COMPUTING: Zunehmend durchdringt die IT auch das Geschäftsmodell. Für die Strategie in der Cloud wurde im Rahmen eines EU-Projekts ein neues Konzept erarbeitet.
    01B - Beitrag in Magazin oder Zeitung
  • Vorschaubild
    Publikation
    Run-Time Planning of Case-based Business Processes
    (IEEE, 2016) Hinkelmann, Knut [in: IEEE Tenth International Conference on Research Challenges in Information Science]
    Organizations act in highly competitive markets, which forces them to be flexible. Constantly changing business requirements require flexible business processes. Case Management Model and Notation (CMMN) supports modeling run-time flexibility of partially structured business process models, but does not fully specify the control flow. Objective: The goal is to develop a planning algorithm that supports the case worker in planning case-based business processes at run-time. Method: We identify the requirements of run-time planning of partly structured processes by analyzing the admission process for the master degree at FHNW. To plan the process instance, we develop a planning algorithm. Our planning algorithm is evaluated using concrete cases provided by FHNW in order to demonstrate real application. Results: The planning algorithm reflects the requirements for serializing tasks at run-time. Conclusion: Our planning algorithm allows to automatically deriving context-specific execution plans for CMMN models at run-time.
    04 - Beitrag Sammelband oder Konferenzschrift
  • Vorschaubild
    Publikation
    Business Process as a Service (BPaaS): The Smart BPaaS Design Environment
    (2016) Hinkelmann, Knut [in: CAiSE 2016 Industry Track]
    This paper introduces the project idea of Business Processes as a Services (BPaaS) that is worked out in the H2020 project CloudSocket. Concept models and semantics are used to align domain specific business processes with executable workflows that are deployed and in production in a multi-cloud environment. The Business Process Management System Paradigm (BPMS) is requesting the functional capabilities of the so-called BPaaS Environments (i) design, (ii) allocation, (iii) execution and (iv) evaluation, which technically compose the CloudSocket Broker platform. This paper introduces first findings of aligning customers’ business needs with BPaaS cloud offerings using a model-based approach.
    04 - Beitrag Sammelband oder Konferenzschrift
  • Vorschaubild
    Publikation
    Workplace Learning - Providing Recommendations of Experts and Learning Resources in a Context-sensitive and Personalized Manner
    (2016) Hinkelmann, Knut [in: Proceedings of Special Session on Learning Modeling in Complex Organizations (LCMO) at MODELSWARD'16]
    Support of workplace learning is increasingly important as change in every form determines today's working world in industry and public administrations alike. Adapt quickly to a new job, a new task or a new team is a major challenge that must be dealt with ever faster. Workplace learning differs significantly from school learning as it should be strictly aligned to business goals. In our approach we support workplace learning by providing recommendations of experts and learning resources in a context-sensitive and personalized manner. We utilize user s' workplace environment, we consider their learning preferences and zone of proximal development, and compare required and acquired competencies in order to issue the best suited recommendations. Our approach is part of the European funded project Learn PAd. Applied research method is Design Science Research. Evaluation is done in an iterative process. The recommender system introduced here is evaluated theoretically based on user requirements and practically in an early evaluation process conducted by the Learn PAd application partner.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    KPIs 4 Workplace Learning
    (Springer, 2016) Hinkelmann, Knut [in: Proceedings of the 8th International Conference on Knowledge Management and Information Sharing (KMIS)]
    Enterprises and Public Administrations alike need to ensure that newly hired employees are able to learn the ropes fast. Employers also need to support continuous workplace learning. Work-place learning should be strongly related to business goals and thus, learning goals should direct-ly add to business goals. To measure achievement of both learning and business goals we pro-pose augmented Key Performance Indicators (KPI). In our research we applied model driven engineering. Hence we developed a model for a Learning Scorecard comprising of business and learning goals and their KPIs represented in an ontology. KPI performance values and scores are calculated with formal rules based on the SPARQL Inferencing Notation. Results are presented in a dashboard on an individual level as well as on a team/group level. Requirements, goals and KPIs as well as performance measurement were defined in close co-operation with Marche Region, business partner in Learn PAd.
    04B - Beitrag Konferenzschrift