Das Stellen von Diagnosen ist ein in verschiedenen Gebieten in ähnlicher Form wiederkehrendes Problem. Ziel ist es, unter Nutzung von Beobachtungen des fehlerhaften Systems die Ursachen für das aktuelle Fehlverhalten zu ermitteln. In den bisher vorliegenden Theorien der modellbasierten Diagnose wird die Therapie als beeinflussender Faktor vernachlässigt und als externes (nachfolgendes) Problem betrachtet. Da diagnostische Expertensysteme in der Medizin aber den jeweiligen Untersuchungs- und Behandlungsmöglichkeiten des Arztes einer bestimmten Spezialisierungstiefe entsprechen sollten, scheint es angebracht, in eine Theorie der Diagnose auch das Konzept der Therapie einzubringen.

Probabilistic data and knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic database systems, which are widely and successfully employed. Beyond all the success stories, however, such systems still lack the fundamental machinery to convey some of the valuable knowledge hidden in them to the end user, which limits their potential applications in practice. In particular, in their classical form, such systems are typically based on strong, unrealistic limitations, such as the closed-world assumption, the closed-domain assumption, the tuple-independence assumption, and the lack of commonsense knowledge. These limitations do not only lead to unwanted consequences, but also put such systems on weak footing in important tasks, querying answering being a very central one. In this thesis, we enhance probabilistic data and knowledge bases with more realistic data models, thereby allowing for better means for querying them. Building on the long endeavor of unifying logic and probability, we develop different rigorous semantics for probabilistic data and knowledge bases, analyze their computational properties and identify sources of (in)tractability and design practical scalable query answering algorithms whenever possible. To achieve this, the current work brings together some recent paradigms from logics, probabilistic inference, and database theory.

Probabilistic data and knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic database systems, which are widely and successfully employed. Beyond all the success stories, however, such systems still lack the fundamental machinery to convey some of the valuable knowledge hidden in them to the end user, which limits their potential applications in practice. In particular, in their classical form, such systems are typically based on strong, unrealistic limitations, such as the closed-world assumption, the closed-domain assumption, the tuple-independence assumption, and the lack of commonsense knowledge. These limitations do not only lead to unwanted consequences, but also put such systems on weak footing in important tasks, querying answering being a very central one. In this thesis, we enhance probabilistic data and knowledge bases with more realistic data models, thereby allowing for better means for querying them. Building on the long endeavor of unifying logic and probability, we develop different rigorous semantics for probabilistic data and knowledge bases, analyze their computational properties and identify sources of (in)tractability and design practical scalable query answering algorithms whenever possible. To achieve this, the current work brings together some recent paradigms from logics, probabilistic inference, and database theory.

Ziel dieses Beitrags aus der Lehrpraxis ist es, die erforderlichen Herangehensweisen für die Erstellung von fachübergreifenden Wissensbasen und deren Nutzung im Rahmen studentischer Semesterprojekte exemplarisch am Lehrbeispiel einer anwendenden Ingenieurdisziplin darzustellen.