Hinkelmann, Knut

Profilbild
E-Mail-Adresse
Geburtsdatum
Projekt
Organisationseinheiten
Berufsbeschreibung
Nachname
Hinkelmann
Vorname
Knut
Name
Hinkelmann, Knut

Filter

2011 - 201925
Filter zurücksetzen

Einstellungen

Typ: 04B - Beitrag Konferenzschrift × Anfangsdatum: 2010 × Enddatum: 2019 ×

Suchergebnisse

Gerade angezeigt 1 - 10 von 25
  • 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 nicht verfügbar
    Publikation
    Enhance classroom preparation for flipped classroom using AI and analytics
    (SciTePress, 2018) Hinkelmann, Knut [in: ICEIS 2018. 20th International Conference on Enterprise Information Systems. Proceedings]
    In a flipped classroom setting, it is important for students to come prepared for the classroom. Being prepared in advance helps students to grasp the concepts taught during classroom sessions. A recent student survey at Fachhochschule Nordwestschweiz (FHNW), Business School, Switzerland, revealed that only 27.7% students often prepared before a class and only 7% always prepared before a class. The main reason for not preparing for classes was lack of time and workload. A literature review study revealed that there is a growth of the use of Artificial Intelligence (AI), for example, chatbots and teaching assistants, which support both teachers and students for classroom preparation. There is also a rise in the use of data analytics to support tutor decision making in real time. However, many of these tools are based on external motivation factors like grading and assessment. Intrinsic motivation among students is more rewarding in the long term. This paper proposes an application based on AI and data analysis that focuses on intrinsically motivating and preparing students in a flipped classroom approach.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Enterprise architecture driven and user-friendly SaaS service selection
    (IEEE, 2018) Hinkelmann, Knut [in: 2018 Sixth International Conference on Enterprise Systems. ES 2018. 1-2 October 2018, Limassol, Cyprus. Proceedings]
    In the last decade the number of cloud services has grown significantly, and its nature challenges the traditional process of requirements elicitation for software solutions. Most of the conducted cloud research address nonfunctional requirements, such as payment strategies, performance or security. Methods are required which provide an appropriate level of abstraction for describing the functional aspects of cloud services. We suggest an approach which exploits the potential of enterprise architecture modeling for cloud service selection. This study contributes to cloud service selection in two ways. First, we suggest a concept for the description of functional requirements for vertical SaaS services. Second, we provide a method that enables various kinds of stakeholders to describe their requirements considering their individual knowledge level.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Towards an agile and ontology-aided modeling environment for DSML adaptation
    (2018) Hinkelmann, Knut [in: Advanced Information Systems Engineering Workshops (CAiSE 2018)]
    The advent of digitalization exposes enterprises to an ongoing transformation with the challenge to quickly capture relevant aspects of changes. This brings the demand to create or adapt domain-specific modeling languages (DSMLs) efficiently and in a timely manner, which, on the contrary, is a complex and time-consuming engineering task. This is not just due to the required high expertise in both knowledge engineering and targeted domain. It is also due to the sequential approach that still characterizes the accommodation of new requirements in modeling language engineering. In this paper we present a DSML adaptation approach where agility is fostered by merging engineering phases in a single modeling environment. This is supported by ontology concepts, which are tightly coupled with DSML constructs. Hence, a modeling environment is being developed that enables a modeling language to be adapted on-the-fly. An initial set of operators is presented for the rapid and efficient adaptation of both syntax and semantics of modeling languages. The approach allows modeling languages to be quickly released for usage.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Towards business-to-IT alignment in the cloud
    (2018) Hinkelmann, Knut [in: Advances in service-oriented and cloud computing. Workshops of ESOCC 2017, Oslo, Norway, September 27-29, 2017, revised selected papers]
    Cloud computing offers a great opportunity for business process (BP) flexibility, adaptability and reduced costs. This leads to realising the notion of business process as a service (BPaaS), i.e., BPs offered on-demand in the cloud. This paper introduces a novel architecture focusing on BPaaS design that includes the integration of existing state-of-the-art components as well as new ones which take the form of a business and a syntactic matchmaker. The end result is an environment enabling to transform domain-specific BPs into executable workflows which can then be made deployable in the cloud so as to become real BPaaSes.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Stakeholder-oriented and enterprise architecture driven cloud service selection
    (2018) Hinkelmann, Knut [in: Business Information Systems Workshops. BIS 2018 International Workshops, Berlin, Germany, July 18-20, 2018, revised papers]
    04B - Beitrag Konferenzschrift