Univ., Diplomarbeit, 2003--Frankfurt (Main) / Zsfassung in dt. und engl. Sprache

Univ., Diss.--Karlsruhe, 2005. / Literaturverz. S. [255] - 266.

Stuttgart, Univ., Diplomarbeit, 2006.

Thesis (Ph.D.)--Aberdeen University, 2008. / Title from web page (viewed on July 23, 2009). Includes bibliographical references.

Thesis (M.F.A.)--Kent State University, 2009. / Title from PDF t.p. (viewed April 22, 2010). Advisor: Sanda Katila. Keywords: microformats; semantic web; labels. Includes bibliographical references (p. 52-53).

Habil.-Schr., Univ. St. Gallen, 2007.

This thesis provides a detailed description of the research undertaken into the creation of a framework that uses Semantic Web languages to implement a recently developed commonsense reasoning formalism called Discrete Event Calculus (DEC). It aims to show to what extent DEC reasoning can be applied to Semantic Web data, using the Semantic Web standards and supporting development environments available for the purpose in 2008, when the research programme commenced. The research aims to provide an accurate and reusable DEC ontology using the languages defined in Semantic Web Standards. To this end, an ontology describing the DEC entities and axioms is defined in OWL and SWRL; this represents the core elements of the DEC formalism, namely its set of logical types and predicates and the relations between them. The ontology is used together with a proof-of-concept DEC resolver software that applies the ontology to an existing rules engine, so that new inferences can be created from a DEC domain. The design and implementation of the combined ontology and software framework are described in detail. The methodological issues involved in reconciling a software model with an ontology model are also discussed and the capabilities of the framework are validated by a series of tests modelled on established AI benchmark scenarios that can be resolved correctly using DEC. The results confirm that the framework will create the appropriate inferences with reference to the benchmark problems, though they also highlight some of current limitations in the framework, notably to do with how it represents changing fluent values. A detailed sample domain ontology is provided, which is based on the domain of turn-based multiplayer online games; this illustrates how the DEC ontology defined in this research could be extended for use with other domains. A further extension of the DEC ontology is proposed, which enables the resolver to represent real-world time values independently of the timepoints defined as part of the formalism. Finally, the strengths and extant boundaries of the chosen approach are discussed and suggestions are provided for improvements that could form the basis of future work.

The web of data has continued to expand thanks to the principles of Linked Data outlined by Tim Berners-Lee, increasing its impact on the semantic web both in its depth and range of data sources. Meanwhile traditional web applications and technologies, with a strong focus on user interaction, such as blogs, wikis, folksonomies-based systems, and content management systems have become an integral part of the World Wide Web. However the semantic web has not yet managed to fully harness these technologies, resulting in a lack of linked data coming from user-generated content. The high level aim of this thesis is to answer the question of whether semantic web applications can be configured to use existing technologies that encourage usergenerated content on the Web. This thesis proposes an approach to reusing user-generated content from folksonomybased systems in semantic web applications, allowing these applications to be configured to make use of the structure and associated reasoning power of the semantic web, but while being able to reuse the vast amount of data already existing in these folksonomy-based systems. It proposes two new methods of semantic web application development: (i) a reusable infrastructure for building semantic mashup applications that can be configured to make use of the proposed approach; and (ii) a approach to configuring traditional web content management systems (CMS) to maintain repositories of Linked Data. The proposed approach allows semantic web applications to make use of tagged resources, while also addressing some limitations of the folksonomy approach by using ontology reasoning to exploit the structured information held in domain ontologies. The reusable infrastructure provides a set of components to allow semantic web applications to be configured to reuse content from folksonomy-based systems, while also allowing the users of these systems to contribute to the semantic web indirectly via the proposed approach. The proposed Linked Data CMS approach provides a configurable tools for semantic web application developers to develop an entire website based on linked data, while allowing ordinary web users to contribute directly to the semantic web using familiar CMS tools. The approaches proposed in this thesis make use of lightweight ontology reasoning, which is both efficient and scalable, to provide a basis for the development of practical semantic web applications. The research presented in this thesis shows how the semantic web can reuse both folksonomies and content management systems from Web 2.0 to help narrow the gap between these two key areas of the web.

This thesis is motivated by the need for autonomous agents on the Semantic Web to be able to learn The Semantic Web is an effort for extending the existing Web with machine understandable information, thus enabling intelligent agents to understand the content of web-pages and help users carrying out tasks online. For such autonomous personal agents working on a world wide Semantic Web we make two observations. Firstly, every user is different and the Semantic Web will never cater for them all - - therefore, it is crucial for an agent to be able to learn from the user and the world around it to provide a personalised view of the web. Secondly, due to the immense amounts of information available on the world wide Semantic Web an agent cannot read and process all available data. We argue that to deal with the information overload a goal-directed approach is needed; an agent must be able to reason about the external world, the internal state and the actions available and only carry out the actions that help activate the current goal. In the first part of this thesis we explore the application of two machine learning techniques to Semantic Web data. Firstly, we investigate the classification of Semantic Web resources, we discuss the issues of mapping Semantic Web format to an input representation suitable for a selection of well-known algorithms, and outline the requirements for these algorithms to work well in a Semantic Web context. Secondly, we consider the clustering of Semantic Web resources. Here we focus on the definition of the similarity between two resources, and how we can determine what part of a large Semantic Web graph is relevant to a single resource. In the second part of the thesis we describe our goal-directed learning agent Smeagol. We present explicit definitions of the classification and clustering techniques devised in the first part of the thesis, allowing Smeagol to use a planning approach to create plans of actions that may fulfil a given top-level goal. We also investigate different ways that Smeagol can dynamically replan when steps within the initial plan fail and show that Smeagol can offer plausible learned answers to a given query, even when no explicit correct answer exists.

The massive use of Internet-based connectivity of devices such as smartphones and sensors has led to the emergence of Internet of Things(IoT). Healthcare is one of the areas that IoT-based applications deployment is becoming more successful. However, the deployment of IoT in healthcare faces one major challenge, the selection of IoT devices by stakeholders (for example, patients, caregivers, health professionals and other government agencies) given an amount of available IoT devices based on a disease(for ex-ample, Asthma) or various healthcare scenarios (for example, disease management, prevention and rehabilitation). Since healthcare stakeholders currently do not have enough knowledge about IoT, the IoT devices selection process has to proceed in a way that it allows users to have more detailed information about IoT devices for example, Quality of Service (QoS) parameters, cost, availability(manufacturer), device placement and associated disease. To address this challenge, this thesis work proposes, develops and validates a novel Semantic sEnsor sElection system(iSEE) for healthcare. This thesis also develops iSEE system prototype and Smart Healthcare Ontology(SHO). A Java application is built to allow users for querying our developed SHO in an efficient way.The iSEE system is evaluated based on query response time and the result-set for the queries. Further, we evaluate SHO using Competency Questions(CQs). The conducted evaluations show that our iSEE system can be used efficiently to support stakeholders within the healthcare domain.