Pseudo-contractions as Gentle Repairs

Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas

Business Model Epistemology : Support for a Semi-Structured and Inclusive Approach to Business Modeling in Established Firms

The world is facing a climate crisis and established firms are set to play a critical role in the societal changes that are needed to defeat it. At the same time, digital technologies are advancing to enable many of the technological solutions that will be required. Together, these two trends, toward greater environmental sustainability and digitalized business, superficially suggest opportunities for business development in established firms. Nevertheless, more substantial change in these directions has appeared difficult to accomplish, and many established firms remain in their current tracks of ‘business as usual’. In search of instruments for business development initiatives such as those related to sustainability and digitalization, much attention has been focused on business models. The business model term came into popular usage around the turn of the millennium and signifies possibilities to do business in fundamentally different ways, as demonstrated by the many internet-based firms that emerged during this era. Since then many different formalized frameworks for working with business models have been proposed. With the aim of guiding practitioners in this work, a large part of these has sought to establish exactly what kinds of conceptual components need to be considered to construct a working business model. A prominent and widely used example of this ontological approach is the Business Model Canvas; a tool that is based on the idea that business models can be defined in terms of a finite set of components, and instantiated as a standardized framework for universal reference in modeling activities. However, although there are several benefits to this ontological approach, it does not directly address some of the critical challenges that business modeling in established firms often faces. In the established context, business model innovation is not simply a search for a new business model, but often also a transitioning from an established and often historically successful business model. Moreover, when initiatives such as those for environmental sustainability and digitalization are assumed, more substantial and path-breaking changes are often required. This effectively means that a transitioning is often also required on the level of the innovation process itself; from a mode of continuous innovation into a mode of discontinuous innovation. The research presented in this thesis directly addresses these challenges by interpreting the overall process of business model innovation in established firms as an epistemological process of situated conceptual change. This interpretation, which takes inspiration from both previous management and cognitive science theory, contributes to a more interpretive and natural view on business models as instruments for learning, and, as mediators of cognitive change at both the individual and organizational level. As discussed in this thesis, from both an empirical and a theoretical basis, this view takes on particular significance when business model formulation is conducted as a delegated practice, separate from decision makers with authority on their eventual implementation. Overall, from a practical perspective, the proposed epistemological view is found to critically change the conditions for the design of business model tools—suggesting a more semi-structured and inclusive approach to their design

Scalable integration of uncertainty reasoning and semantic web technologies

In recent years formal logical standards for knowledge representation to model real world knowledge and domains and make them accessible for computers gained a lot of trac- tion. They provide an expressive logical framework for modeling, consistency checking, reasoning, and query answering, and have proven to be versatile methods to capture knowledge of various fields. Those formalisms and methods focus on specifying knowl- edge as precisely as possible. At the same time, many applications in particular on the Semantic Web have to deal with uncertainty in their data; and handling uncertain knowledge is crucial in many real- world domains. However, regular logic is unable to capture the real-world properly due to its inherent complexity and uncertainty, all the while handling uncertain or incomplete information is getting more and more important in applications like expert system, data integration or information extraction. The overall objective of this dissertation is to identify scenarios and datasets where methods that incorporate their inherent uncertainty improve results, and investigate approaches and tools that are suitable for the respective task. In summary, this work is set out to tackle the following objectives: 1. debugging uncertain knowledge bases in order to generate consistent knowledge graphs to make them accessible for logical reasoning, 2. combining probabilistic query answering and logical reasoning which in turn uses these consistent knowledge graphs to answer user queries, and 3. employing the aforementioned techniques to the problem of risk management in IT infrastructures, as a concrete real-world application. We show that in all those scenarios, users can benefit from incorporating uncertainty in the knowledge base. Furthermore, we conduct experiments that demonstrate the real- world scalability of the demonstrated approaches. Overall, we argue that integrating uncertainty and logical reasoning, despite being theoretically intractable, is feasible in real-world application and warrants further research

Extraction of ontology schema components from financial news

In this thesis we describe an incremental multi-layer rule-based methodology for the extraction of ontology schema components from German financial newspaper text. By Extraction of Ontology Schema Components we mean the detection of new concepts and relations between these concepts for ontology building. The process of detecting concepts and relations between these concepts corresponds to the intensional part of an ontology and is often referred to as ontology learning. We present the process of rule generation for the extraction of ontology schema components as well as the application of the generated rules.In dieser Arbeit beschreiben wir eine inkrementelle mehrschichtige regelbasierte Methode für die Extraktion von Ontologiekomponenten aus einer deutschen Wirtschaftszeitung. Die Arbeit beschreibt sowohl den Generierungsprozess der Regeln für die Extraktion von ontologischem Wissen als auch die Anwendung dieser Regeln. Unter Extraktion von Ontologiekomponenten verstehen wir die Erkennung von neuen Konzepten und Beziehungen zwischen diesen Konzepten für die Erstellung von Ontologien. Der Prozess der Extraktion von Konzepten und Beziehungen zwischen diesen Konzepten entspricht dem intensionalen Teil einer Ontologie und wird im Englischen Ontology Learning genannt. Im Deutschen enspricht dies dem Lernen von Ontologien