Martin, Andreas

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Martin
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Andreas
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Martin, Andreas

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Autor:in: Lenat, Doug × Autor:in: Martin, Andreas × Autor:in: van Harmelen, Frank × Enddatum: 2019 × Anfangsdatum: 2019 ×

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  • Vorschaubild
    Publikation
    Virtual bartender: a dialog system combining data-driven and knowledge-based recommendation
    (2019) Hinkelmann, Knut; Blaser, Monika; Faust, Oliver; Horst, Alexander; Mehli, Carlo; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; van Harmelen, Frank; Clark, Peter [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
    Towards an assistive and pattern learning-driven process modeling approach
    (2019) Laurenzi, Emanuele; Hinkelmann, Knut; Jüngling, Stephan; Montecchiari, Devid; Pande, Charuta; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; van Harmelen, Frank; Clark, Peter [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
    Combining machine learning with knowledge engineering to detect fake news in social networks - A survey
    (2019) Ahmed, Sajjad; Hinkelmann, Knut; Corradini, Flavio; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; van Harmelen, Frank [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