Witschel, Hans Friedrich

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Hans Friedrich
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Witschel, Hans Friedrich

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  • Publikation
    Natural language-based user guidance for knowledge graph exploration: a user study
    (SciTePress, 2021) Witschel, Hans Friedrich; Riesen, Kaspar; Grether, Loris; Cucchiara, Rita; Fred, Ana; Filipe, Joaquim [in: Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management]
    Large knowledge graphs hold the promise of helping knowledge workers in their tasks by answering simple and complex questions in specialised domains. However, searching and exploring knowledge graphs in current practice still requires knowledge of certain query languages such as SPARQL or Cypher, which many untrained end users do not possess. Approaches for more user-friendly exploration have been proposed and range from natural language querying over visual cues up to query-by-example mechanisms, often enhanced with recommendation mechanisms offering guidance. We observe, however, a lack of user studies indicating which of these approaches lead to a better user experience and optimal exploration outcomes. In this work, we make a step towards closing this gap by conducting a qualitative user study with a system that relies on formulating queries in natural language and providing answers in the form of subgraph visualisations. Our system is able to offer guidance via query recommendations based on a current context. The user study evaluates the impact of this guidance in terms of both efficiency and effectiveness (recall) of user sessions. We find that both aspects are improved, especially since query recommendations provide inspiration, leading to a larger number of insights discovered in roughly the same time.
    04B - Beitrag Konferenzschrift
  • Publikation
    Hybrid conversational AI for intelligent tutoring systems
    (Sun SITE, Informatik V, RWTH Aachen, 2021) Pande, Charuta; Witschel, Hans Friedrich; Martin, Andreas; Montecchiari, Devid; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Dough; Stolle, Reinhard; Harmelen, Frank van [in: Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021)]
    We present an approach to improve individual and self-regulated learning in group assignments. We focus on supporting individual reflection by providing feedback through a conversational system. Our approach leverages machine learning techniques to recognize concepts in student utterances and combines them with knowledge representation to infer the student’s understanding of an assignment’s cognitive requirements. The conversational agent conducts end-to-end conversations with the students and prompts them to reflect and improve their understanding of an assignment. The conversational agent not only triggers reflection but also encourages explanations for partial solutions.
    04B - Beitrag Konferenzschrift
  • Publikation
    KvGR: A graph-based interface for explorative sequential question answering on heterogeneous information sources
    (Springer, 2020) Witschel, Hans Friedrich; Riesen, Kaspar; Grether, Loris; Jose, Joemon M.; Yilmaz, Emine; Magalhães, João; Castells, Pablo; Ferro, Nicola; Silva, Mário J.; Martins, Flávio [in: Advances in Information Retrieval. 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14-17, 2020. Proceedings]
    Exploring a knowledge base is often an iterative process: initially vague information needs are refined by interaction. We propose a novel approach for such interaction that supports sequential question answering (SQA) on knowledge graphs. As opposed to previous work, we focus on exploratory settings, which we support with a visual representation of graph structures, helping users to better understand relationships. In addition, our approach keeps track of context – an important challenge in SQA – by allowing users to make their focus explicit via subgraph selection. Our results show that the interaction principle is either understood immediately or picked up very quickly – and that the possibility of exploring the information space iteratively is appreciated.
    04B - Beitrag Konferenzschrift
  • Publikation
    Case model for the RoboInnoCase recommender system for cases of digital business transformation: structuring information for a case of digital change
    (SciTePress, 2019) Witschel, Hans Friedrich; Peter, Marco; Seiler, Laura; Parlar, Soyhan; Gatziu Grivas, Stella; Bernardino, Jorge; Salgado, Ana; Filipe, Joaquim [in: ICEIS 2019. 21st International Conference on Enterprise Information Systems. Proceedings]
    In this work, we develop a case model to structure cases of past digital transformations which act as input data for a recommender system. The purpose of that recommender is to act as an inspiration and support for new cases of digital transformation. To define the case model, case analyses, where 40 cases of past digital transformations are analysed and coded to determine relevant attributes and values, literature research and the particularities of the case for digital change, are used as a basis. The case model is evaluated by means of an experiment where two different scenarios are fed into a prototypical case-based recommender system and then matched, based on an entropically derived weighting system, with the case base that contains cases structured according to the case model. The results not only suggest that the case model’s functionality can be guaranteed, but that a good quality of the given recommendations is achieved by applying a case-based recommender system using the proposed case model. The results not only suggest that the case model’s functionality can be guaranteed, but that a good quality of the given recommendations is achieved by applying a case-based recommender system using the proposed case model.
    04B - Beitrag Konferenzschrift
  • Publikation
    Enhancing reflective practices within business management education: what kinds of e-learning scenarios can be designed?
    (ACM, 2019) Inglese, Terry; von Kutzschenbach, Michael; Witschel, Hans Friedrich [in: IC4E '19. 10th International Conference on E-Education, E-Business, E-Management and E-Learning]
    Contrary to the dominant appearance of the topic ‘digitalization,’ a majority of managers do not know what it means and how they can leverage the development of new technologies and disruptive innovations for their business. Furthermore, doing business is getting increasingly complex due to globalization and specialization. Thus, it looks like everybody is hyperactively looking for an external solution to their managerial challenges while, at the same time, managers seem to have lost their intuition for future direction and are unable to step back and think about intended and unintended consequences of the digital revolution. We, who provide business management education for future leaders, are concerned about this development and teach our students to appreciate the discomfort with the hard work of thinking and reflecting to learn from the insights about innovation, strategy and personal development to achieve improved leadership competence. In this paper, we will present our lessons learnt from asking students of a leadership class at an applied university to write a reflective journal for deep learning purpose.
    04B - Beitrag Konferenzschrift
  • 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
  • Publikation
    Enhance classroom preparation for flipped classroom using AI and analytics
    (SciTePress, 2018) Diwanji, Prajakta; Hinkelmann, Knut; Witschel, Hans Friedrich; Hammoudi, Slimane; Smialek, Michal; Camp, Olivier; Filipe, Joaquim [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
  • Publikation
    Training and re-using human experience: a recommender for more accurate cost estimates in project planning
    (SciTePress, 2018) Rudolf von Rohr, Christian; Witschel, Hans Friedrich; Martin, Andreas [in: IC3K 2018 - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management]
    In many industries, companies deliver customised solutions to their (business) customers within projects. Estimating the human effort involved in such projects is a difficult task and underestimating efforts can lead to non-billable hours, i.e. financial loss on the side of the solution provider. Previous work in this area has focused on automatic estimation of the cost of software projects and has largely ignored the interaction between automated estimation support and human project leads. Our main hypothesis is that an adequate design of such interaction will increase the acceptance of automatically derived estimates and that it will allow for a fruitful combination of data-driven insights and human experience. We therefore build a recommender that is applicable beyond software projects and that suggests job positions to be added to projects and estimated effort of such positions. The recommender is based on the analysis of similar cases (case-based reasoning), "explains" derived similarities and allows human intervention to manually adjust the outcomes. Our experiments show that recommendations were considered helpful and that the ability of the system to explain and adjust these recommendations was heavily used and increased the trust in the system. We conjecture that the interaction of project leads with the system will help to further improve the accuracy of recommendations and the support of human learning in the future.
    04B - Beitrag Konferenzschrift
  • Publikation
    Random walks on human knowledge: incorporating human knowledge into data-driven recommenders
    (2018) Witschel, Hans Friedrich; Martin, Andreas; Bernardino, Jorge; Salgado, Ana; Filipe, Joaquim [in: IC3K 2018. 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. Proceedings]
    We explore the use of recommender systems in business scenarios such as consultancy. In these situations, apart from personal preferences of users, knowledge about objective business-driven criteria plays a role. We investigate strategies for representing and incorporating such knowledge into data-driven recommenders. As a baseline, we choose a robust and flexible paradigm that is based on a simple graph-based representation of past customer cases and choices, in combination with biased random walks. On a real data set from a business intelligence consultancy firm, we study how the incorporation of two important types of explicit human knowledge – namely taxonomic and associative knowledge – impacts the effectiveness of a data-driven recommender. Our results show no consistent improvement for taxonomic knowledge, but quite substantial and significant gains when using associative knowledge.
    04B - Beitrag Konferenzschrift
  • Publikation
    Energy saving in smart homes based on consumer behavior: A case study
    (IEEE, 2015) Zehnder, Michael; Wache, Holger; Witschel, Hans Friedrich; Zanatta, Danilo; Rodriguez, Miguel [in: First IEEE International Smart Cities Conference (ISC2-2015)]
    This paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that mines consumer behavior data only and applies machine learning to suggest actions for inhabitants to reduce the energy consumption of their homes. The system looks for frequent and periodic patterns in the event data provided by the digitalSTROM home automation system. These patterns are converted into association rules, prioritized and compared with the current behavior of the inhabitants. If the system detects opportunities to save energy without decreasing the comfort level, it sends a recommendation to the inhabitants.
    04B - Beitrag Konferenzschrift