2021, Witschel, Hans Friedrich, Riesen, Kaspar, Grether, Loris, Cucchiara, Rita, Fred, Ana, Filipe, Joaquim

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.

2020, Witschel, Hans Friedrich, Riesen, Kaspar, Grether, Loris, Jose, Joemon M., Yilmaz, Emine, Magalhães, João, Castells, Pablo, Ferro, Nicola, Silva, Mário J., Martins, Flávio

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.

2019, Witschel, Hans Friedrich, Peter, Marco, Seiler, Laura, Parlar, Soyhan, Gatziu Grivas, Stella, Bernardino, Jorge, Salgado, Ana, Filipe, Joaquim

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.

2018, Diwanji, Prajakta, Hinkelmann, Knut, Witschel, Hans Friedrich, Hammoudi, Slimane, Smialek, Michal, Camp, Olivier, Filipe, Joaquim

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.

2021, Pande, Charuta, Witschel, Hans Friedrich, Martin, Andreas

Practitioners in business information systems are frequently faced with tasks that involve interpretation and representation of organizational information as models, e.g. business processes, the involved participants, and data. Usually, this information comes from varied sources like stakeholders and documents, often resulting in subjective, biased, or incomplete information. Simulating a realistic organizational environment is important for the education of future business process experts, but challenging to achieve in the educational setting. In this work, we propose a chatbot-based educational adventure game that can introduce complex and often contradictory sources of information in a fun learning approach and help the students in abstracting and interpreting this information, constructing models as an outcome. We elaborate on the first design ideas and requirements.

2020, Witschel, Hans Friedrich, Pande, Charuta, Martin, Andreas, Laurenzi, Emanuele, Hinkelmann, Knut, Dornberger, Rolf

2018, Rudolf von Rohr, Christian, Witschel, Hans Friedrich, Martin, Andreas

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.

2020, Witschel, Hans Friedrich, Diwanji, Prajakta, Hinkelmann, Knut, Dornberger, Rolf

2019, Inglese, Terry, von Kutzschenbach, Michael, Witschel, Hans Friedrich

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.

2018, Witschel, Hans Friedrich, Martin, Andreas, Bernardino, Jorge, Salgado, Ana, Filipe, Joaquim

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.