Semantic Service Description Framework for Efficient Service Discovery and Composition

Web services have been widely adopted as a new distributed system technology by industries in the areas of, enterprise application integration, business process management, and virtual organisation. However, lack of semantics in current Web services standards has been a major barrier in the further improvement of service discovery and composition. For the last decade, Semantic Web Services have become an important research topic to enrich the semantics of Web services. The key objective of Semantic Web Services is to achieve automatic/semi-automatic Web service discovery, invocation, and composition. There are several existing semantic Web service description frameworks, such as, OWL-S, WSDL-S, and WSMF. However, existing frameworks have several issues, such as insufficient service usage context information, precisely specified requirements needed to locate services, lacking information about inter-service relationships, and insufficient/incomplete information handling, make the process of service discovery and composition not as efficient as it should be. To address these problems, a context-based semantic service description framework is proposed in this thesis. This framework focuses on not only capabilities of Web services, but also the usage context information of Web services, which we consider as an important factor in efficient service discovery and composition. Based on this framework, an enhanced service discovery mechanism is proposed. It gives service users more flexibility to search for services in more natural ways rather than only by technical specifications of required services. The service discovery mechanism also demonstrates how the features provided by the framework can facilitate the service discovery and composition processes. Together with the framework, a transformation method is provided to transform exiting service descriptions into the new framework based descriptions. The framework is evaluated through a scenario based analysis in comparison with OWL-S and a prototype based performance evaluation in terms of query response time, the precision and recall ratio, and system scalability

A Context-based Framework and Method for Learning Object Description and Search

For the last decade, E-Learning has become an active research area. Many companies and organisations are now providing large amounts of online learning resources. These learning resources have covered most common education and learning areas and subjects and are always available so that the learners can access them from anywhere which has an Internet connection. Learners can flexibly choose the subjects they want and build up their own curriculum and study schedule. However, most of the online learning resources are poorly described and structured so causing huge problems in their use, search, organization, and management. To overcome the problems, we propose a novel and practical context-based semantic description framework which aims to describe information and knowledge about learning resources and their structures. Context-based semantic description is an effective way to extract knowledge from various aspects to depicting learning resources which are abstracted and termed as "Learning Objects". This framework consists of four parts: the definition of Learning Objects, a Context-based Semantic Description Model, an ontology, and learning concept dependency graphs. By using the Learning Object’s attributes and their various semantic relationships addressed in the proposed framework, we attempt to search and match a learner’s requirements against the description of Learning Objects provided by the framework with the help of knowledge from learning environments. A key step here is to compute semantic similarity using the modelled knowledge. The proposed work aims to support learners in using massive learning resources from the web to perform self-learning with or without the help of educators’ advice and instruction