6 research outputs found

    Probabilistic Reasoning with Abstract Argumentation Frameworks

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    Abstract argumentation offers an appealing way of representing and evaluating arguments and counterarguments. This approach can be enhanced by considering probability assignments on arguments, allowing for a quantitative treatment of formal argumentation. In this paper, we regard the assignment as denoting the degree of belief that an agent has in an argument being acceptable. While there are various interpretations of this, an example is how it could be applied to a deductive argument. Here, the degree of belief that an agent has in an argument being acceptable is a combination of the degree to which it believes the premises, the claim, and the derivation of the claim from the premises. We consider constraints on these probability assignments, inspired by crisp notions from classical abstract argumentation frameworks and discuss the issue of probabilistic reasoning with abstract argumentation frameworks. Moreover, we consider the scenario when assessments on the probabilities of a subset of the arguments are given and the probabilities of the remaining arguments have to be derived, taking both the topology of the argumentation framework and principles of probabilistic reasoning into account. We generalise this scenario by also considering inconsistent assessments, i.e., assessments that contradict the topology of the argumentation framework. Building on approaches to inconsistency measurement, we present a general framework to measure the amount of conflict of these assessments and provide a method for inconsistency-tolerant reasoning

    Exploiting general-purpose background knowledge for automated schema matching

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    The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

    Logic and Games of Norms: a Computational Perspective

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    Covid-19: reinforcing the impact of Islamic banking through value-based intermediation

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    The novel Covid-19 pandemic has caused an unprecedented human crisis around the globe. The necessary actions implemented to contain the virus have sparked both economic and social downturn. It shows the fragility and unpreparedness of the economy to face such a pandemic. Significant weakening of economic conditions has escalated the pressure on households, businesses and financial markets. However, before the Covid-19 outbreak, Bank Negara Malaysia has taken a new initiative by introducing Value-Based Intermediation (VBI). VBI’s strategy opens up a new holistic layer for Islamic banks in providing the public at large with impactful and profitable services. This paper discusses VBI’s strategy and its potential application from the viewpoint of Sharīʽah. This paper also discusses Islamic banks' activities in implementing VBI as well as their response to the Covid-19 pandemic, based on qualitative inquiry. The paper concludes that VBI is a long journey that requires significant transformation of mindset among key stakeholders. As Covid-19 has adversely impacted communities in several ways, Islamic banks could empower communities through provision of financial solutions that create positive impact
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