Hinkelmann, Knut

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Hinkelmann
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Knut
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Hinkelmann, Knut

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2011 - 20199
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Enddatum: 2019 × Anfangsdatum: 2010 × Dateien dazu vorhanden?: Ja × Typ: 04B - Beitrag Konferenzschrift × Thema: 330 - Wirtschaft ×

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Gerade angezeigt 1 - 9 von 9
  • 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
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    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
    Explicitly Modelling Relationships of Risks on Business Architecture
    (IIMC International Information Management Corporation, 2014) Hinkelmann, Knut [in: eChallenges e-2014 Conference Proceedings]
    Todays increased interest in enterprise risk management is motivated by decision making in reaction to change and complex compliance requirements as well as the need to minimize business losses and improve business outcomes. It is therefore important to help business stakeholders become fully aware of applicable risks and their possible impact on other business constituents. This paper represents an extension of the OMG Business Motivation Model that addresses this topic by improving risk visibility through modelling explicit dependencies of risks on business motivation, business decisions, business processes and compliance requirements. This extension in the form of a meta-model as well as its potential to increase overall risk awareness in enterprises were evaluated.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Combining Process Modelling and Case Modeling
    (2014) Hinkelmann, Knut [in: 8th International Conference on Methodologies, Technologies and Tools enabling e-Government MeTTeG14]
    Adaptive Case Management deals with processes that are not predefined or repeatable, but depend on evolving circumstances and decisions regarding a particular situation. While case management is often considered as different from conventional business process management, in reality they cannot be strictly separated. A structured business process can contain parts which deal with non-routine cases. The Object Management Group (OMG) published the Business Process Model & Notation (BPMN) as well as the Case Management Model & Notation (CMMN). There is an ongoing debate whether these two languages should be combined are kept independent. After a short introduction into CMMN and BPMN we analyse an application process as it is typical for public administration in order to identify strengths and weaknesses of both BPMN and CMMN. We show that typical processes contain both structured and non-structured parts and neither BPMN nor CMMN alone is adequate to model the process. Finally, we propose recommendations for a metamodel, which combines elements of BPMN and CMMN.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Extending Business Motivation Modelling to Foster Regional Flexibility in IT Architecture Management
    (IIMC International Information Management Corporation, 2013) Hinkelmann, Knut [in: eChallenges e-2013 Conference Proceedings]
    Among the main challenges for standardisation of an IT architecture in a multinational company are the different environments in the countries, in particular regarding regulations and market specifics. Therefore, some subsidiaries have to implement local IT solutions that contradict standardisation efforts and lead to a heterogeneous IT architecture. Dynamic business environments require the on-going adaption and re-alignment of business and IT. This is particularly challenging in global multinational organisations that have to balance between standardisation and local solutions. This paper presents an extension of the OMG Business Motivation Model that supports strategic Business-IT alignment by making explicit the dependencies between a company’s strategy and the different IT architectural realisations. The approach has been developed as a case study with a multinational company and evaluated with a transformation project in a regional organisation.
    04B - Beitrag Konferenzschrift
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    Publikation
    Added Value of Sociofact Analysis for Business Agility
    (The AAAI Press, 2011) Hinkelmann, Knut [in: AI for Business Agility. AAAI 2011 Spring Symposium]
    The increasing agility of business requires an accelerated adaptation of organizations to continuously changing conditions. Individual and organizational learning are prominent means to achieve this. Hereby learning is always accompanied by the development of knowledge artifacts. For the entire of learning and artifact development the term knowledge maturing has been introduced recently, which focuses on these three manifestations of knowledge: cognifacts, sociofacts, and artifacts. In this paper we will focus on sociofacts as the subject-bound knowledge manifestation of social actions. Sociofacts are rooted in respective cognifacts play an independent role due to their binding to collective actions and subjects. These are particularly difficult to grasp but play a decisive role for the performance of organizations and the collaboration in there. The presented paper approaches the notion of sociofacts, discusses them on a theoretical level and establishes a first formal notation for sociofacts. We use the case of a merger between two companies to describe the advantages of sociofact analysis for such process. Some sociofact related problems during a merger are described and possible solutions are presented. We identify technical approaches for seizing sociofacts from tool-mediated social interaction and discuss open question for future research.
    04B - Beitrag Konferenzschrift