Search

This dissertation presents models and algorithms for accurately and efficiently extracting data from revisioned content in Collaborative Writing Systems about (i) the provenance and history of specific sequences of text, as well as (ii) interactions between editors via the content changes they perform, especially disagreement. Visualization tools are presented to gain further insights into the extracted data. Collaboration mechanisms to be researched with these new data and tools are discussed.

In this thesis we join methods for evaluating queries over interlinked resources via link traversal with approaches for the integration of data over interlinked schemata via reasoning. Our approach allows for the on-the-fly alignment and processing of dynamically retrieved data in a streaming fashion including incremental query answering. We go beyond the simple consumption of exposed information by enabling manipulations of remote resources in a parallel execution system.

Die Herausforderung in Ambient Assisted Living (AAL) ist es, die vielen heterogenen Komponenten in der AAL-Umgebung geeignet zu arrangieren, um auf die individuellen, schnell wechselnden Bedürfnisse der Nutzer einzugehen. Die Lösung ist ein semantisches Konfigurationsframework. Dieses Framework beinhaltet geeignete Prozesse, Werkzeuge sowie einen ontologiebasierten Austausch, Wiederverwendung und Automatisierung der Informationen zwischen den einzelnen Integrationsschritten.

We extend the description logic SROIQ (OWL 2 DL) with the epistemic operator K and argue that unintended effects occur when imposing the semantics traditionally employed. Consequently, we identify the most expressive DL for which the traditional approach can still be adapted. For the epistemic extension of SROIQ and alike expressive DLs, we suggest a revised semantics that behaves more intuitively in these cases and coincides with the traditional semantics on less expressive DLs.

Information seeking in the Web can be facilitated by recommender systems that guide the users in a personalized manner to relevant resources in the large space of the possible options in the Web. This work investigates how to model people's Web behavior at multiple sites and learn to predict future preferences, in order to generate relevant cross-domain recommendations. This thesis contributes with novel techniques for building cross-domain recommender systems in an open Web setting.

One main problem towards the effective usage of structured data is instance matching, where the goal is to find instance representations referring to the same real-world thing. In this book we investigate how to effectively match Heterogeneous structured data. We evaluate our approaches against the latest baselines. The results show advances beyond the state-of-the-art.

In this thesis, we explore the challenge of search relevance in the context of semantic search. Specifically, the notion of semantic relevance can be distinguished from the other types of relevance in Information Retrieval (IR) in terms of employing an underlying semantic model. We propose the emerging Semantic Web data on the Web which is represented in RDF graph structures as an important candidate to become such a semantic model in a search process.

Die vorliegende Arbeit beschäftigt sich mit dieser Thematik, insbesondere mit der Problematik der Ambiguität, die bei der Zusammenführung natürlich-sprachlicher Informationen mit dem durch Ontologien repräsentierten Wissen auftritt.

In this thesis we consider how user-generated content that is assembled by different popular Web portals can be exploited for Multilingual Information Retrieval. We define the knowledge that can be derived from such portals as Social Semantics. We present to approaches, Cross-lingual Explicit Semantic Analysis and Discriminative Retrieval Models, that are able to support multilingual retrieval models by integrating Social Semantics derived from Wikipedia and Yahoo! Answers.

This thesis describes iMapping and QuiKey, two novel user interface concepts for dealing with structured information. iMapping is a visual knowledge mapping technique based on zooming, which combines the advantages of several existing approaches and scales up to very large maps. QuiKey is a text-based tool to interact with graph-structured knowledge bases with very high interaction efficiency. Both tools have been implemented and positively evaluated in user studies.

This thesis utilizes recommender systems to aid the user in contributing to the Social Semantic Web. In this work, we propose a framework that maps domain properties to recommendation technologies. Next, we develop novel recommendation algorithms for improving personalized tag recommendation and for recommendation of semantic relations. Finally, we introduce a framework to analyze different types of potential attacks against social tagging systems and evaluate their impact on those systems.

This thesis contributes a business process modeling framework based on semantic technologies. The framework consists of modeling languages, methods and tools, which aim at facilitating the design and improving the quality of conceptual business process models.

Extracting information from text is the task of obtaining structured, machine-processable facts from information that is mentioned in an unstructured manner. It thus allows systems to automatically aggregate information for further analysis, efficient retrieval, automatic validation, or appropriate visualization. This thesis explores the potential of using textual patterns for Information Extraction from the World Wide Web.

Conceptual Data Structures (CDS) is a unified meta-model for representing knowledge cues in varying degrees of granularity, structuredness, and formality. CDS consists of: (1) A simple, expressive data-model; (2) A relation ontology which unifies the relations found in cognitive models of personal knowledge management tools, e. g., documents, mind-maps, hypertext, or semantic wikis. (3) An interchange format for structured text. Implemented prototypes have been evaluated.

Ontology evaluation is the task of measuring the quality of an ontology. It enables us to answer the following main question: How to assess the quality of an ontology for the Web? In this thesis a theoretical framework and several methods breathing life into the framework are presented. The application to the above scenarios is explored, and the theoretical foundations are thoroughly grounded in the practical usage of the emerging Semantic Web.

In this thesis we present the client-side approach of Adaptive and Reactive Rich Internet Applications as the main result of our research into how to bring in time adaptivity to Rich Internet Applications. Our approach leverages previous work on adaptive hypermedia, event processing and other research disciplines. We present a holistic framework covering the design-time as well as the runtime aspects of Adaptive and Reactive Rich Internet Applications focusing especially on the run-time aspects.

Knowledge workers (KWers) deal with personal information and use tools like, e.g., desktop workspaces to support their work. But KWer support is hindered by personal information fragmentation, i.e., applications keep a set of personal information while not interconnecting it. This thesis addresses this in the domains personal task management and meeting management by using a common unified personal information model as offered by the semantic desktop personal information management (PIM) system.

Im Rahmen dieser Arbeit wurden zwei Softwarewerkzeuge für die technische Unterstützung von Wissensmanagement (WM) konzipiert und technisch umgesetzt, die zum einen den Reifegrad von Unternehmen unter Verwendung von beliebigen Reifegradmodellen für WM erfassen und darauf basierend Handlungsempfehlungen zur Verfügung stellen, zum anderen auf das Anforderungsprofil eines Unternehmens zugeschnittene Best Practices für WM identifizieren und auf die neue Unternehmenssituation übertragen.

The thesis tackles the problem of high effort for achieving business process compliance to regulations in the area of Enterprise Risk Management. Common to these regulations are requirements on the presence of effective internal controls in companies. The level of automation with regard to translating compliance requirements into a set of internal controls and assuring the effectiveness of these controls during execution of business processes is raised thorugh a novel model-driven approach.