Ontology learning for Semantic Web Services

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 18/10/2010.The expansion of Semantic Web Services is restricted by traditional ontology engineering methods. Manual ontology development is time consuming, expensive and a resource exhaustive task. Consequently, it is important to support ontology engineers by automating the ontology acquisition process to help deliver the Semantic Web vision. Existing Web Services offer an affluent source of domain knowledge for ontology engineers. Ontology learning can be seen as a plug-in in the Web Service ontology development process, which can be used by ontology engineers to develop and maintain an ontology that evolves with current Web Services. Supporting the domain engineer with an automated tool whilst building an ontological domain model, serves the purpose of reducing time and effort in acquiring the domain concepts and relations from Web Service artefacts, whilst effectively speeding up the adoption of Semantic Web Services, thereby allowing current Web Services to accomplish their full potential. With that in mind, a Service Ontology Learning Framework (SOLF) is developed and applied to a real set of Web Services. The research contributes a rigorous method that effectively extracts domain concepts, and relations between these concepts, from Web Services and automatically builds the domain ontology. The method applies pattern-based information extraction techniques to automatically learn domain concepts and relations between those concepts. The framework is automated via building a tool that implements the techniques. Applying the SOLF and the tool on different sets of services results in an automatically built domain ontology model that represents semantic knowledge in the underlying domain. The framework effectiveness, in extracting domain concepts and relations, is evaluated by its appliance on varying sets of commercial Web Services including the financial domain. The standard evaluation metrics, precision and recall, are employed to determine both the accuracy and coverage of the learned ontology models. Both the lexical and structural dimensions of the models are evaluated thoroughly. The evaluation results are encouraging, providing concrete outcomes in an area that is little researched

OpenUP/MDRE: A Model-Driven Requirements Engineering Approach for Health-Care Systems

The domains and problems for which it would be desirable to introduce information systems are currently very complex and the software development process is thus of the same complexity. One of these domains is health-care. Model-Driven Development (MDD) and Service-Oriented Architecture (SOA) are software development approaches that raise to deal with complexity, to reduce time and cost of development, augmenting flexibility and interoperability. However, many techniques and approaches that have been introduced are of little use when not provided under a formalized and well-documented methodological umbrella. A methodology gives the process a well-defined structure that helps in fast and efficient analysis and design, trouble-free implementation, and finally results in the software product improved quality. While MDD and SOA are gaining their momentum toward the adoption in the software industry, there is one critical issue yet to be addressed before its power is fully realized. It is beyond dispute that requirements engineering (RE) has become a critical task within the software development process. Errors made during this process may have negative effects on subsequent development steps, and on the quality of the resulting software. For this reason, the MDD and SOA development approaches should not only be taken into consideration during design and implementation as usually occurs, but also during the RE process. The contribution of this dissertation aims at improving the development process of health-care applications by proposing OpenUP/MDRE methodology. The main goal of this methodology is to enrich the development process of SOA-based health-care systems by focusing on the requirements engineering processes in the model-driven context. I believe that the integration of those two highly important areas of software engineering, gathered in one consistent process, will provide practitioners with many benets. It is noteworthy that the approach presented here was designed for SOA-based health-care applications, however, it also provides means to adapt it to other architectural paradigms or domains. The OpenUP/MDRE approach is an extension of the lightweight OpenUP methodology for iterative, architecture-oriented and model-driven software development. The motivation for this research comes from the experience I gained as a computer science professional working on the health-care systems. This thesis also presents a comprehensive study about: i) the requirements engineering methods and techniques that are being used in the context of the model-driven development, ii) known generic but flexible and extensible methodologies, as well as approaches for service-oriented systems development, iii) requirements engineering techniques used in the health-care industry. Finally, OpenUP/MDRE was applied to a concrete industrial health-care project in order to show the feasibility and accuracy of this methodological approach.Loniewski, G. (2010). OpenUP/MDRE: A Model-Driven Requirements Engineering Approach for Health-Care Systems. http://hdl.handle.net/10251/11652Archivo delegad

Connecting Business and IT - A Model-driven Web Service based Approach

Service-oriented architectures focus on the alignment of business processes and the supporting information technology. To facilitate the alignment and to decrease administration costs we propose linking of SOA services to enterprise models respectively target state organisational models by using semantically enriched conceptual modelling languages. Acting on language meta-level means in this context the introduction of service related constructs into widely used visual modelling grammars. Vision is the situation dependent adaptation of software functionality to the actual business process requirements. An efficient adaptation is crucial in order to be able to respond quickly to environmental or system induced change

Automatic generation of user interfaces from rigorous domain and use case models

Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

An intelligent framework for dynamic web services composition in the semantic web

As Web services are being increasingly adopted as the distributed computing technology of choice to securely publish application services beyond the firewall, the importance of composing them to create new, value-added service, is increasing. Thus far, the most successful practical approach to Web services composition, largely endorsed by the industry falls under the static composition category where the service selection and flow management are done a priori and manually. The second approach to web-services composition aspires to achieve more dynamic composition by semantically describing the process model of Web services and thus making it comprehensible to reasoning engines or software agents. The practical implementation of the dynamic composition approach is still in its infancy and many complex problems need to be resolved before it can be adopted outside the research communities. The investigation of automatic discovery and composition of Web services in this thesis resulted in the development of the eXtended Semantic Case Based Reasoner (XSCBR), which utilizes semantic web and AI methodology of Case Based Reasoning (CBR). Our framework uses OWL semantic descriptions extensively for implementing both the matchmaking profiles of the Web services and the components of the CBR engine. In this research, we have introduced the concept of runtime behaviour of services and consideration of that in Web services selection. The runtime behaviour of a service is a result of service execution and how the service will behave under different circumstances, which is difficult to presume prior to service execution. Moreover, we demonstrate that the accuracy of automatic matchmaking of Web services can be further improved by taking into account the adequacy of past matchmaking experiences for the requested task. Our XSCBR framework allows annotating such runtime experiences in terms of storing execution values of non-functional Web services parameters such as availability and response time into a case library. The XSCBR algorithm for matchmaking and discovery considers such stored Web services execution experiences to determine the adequacy of services for a particular task. We further extended our fundamental discovery and matchmaking algorithm to cater for web services composition. An intensive knowledge-based substitution approach was proposed to adapt the candidate service experiences to the requested solution before suggesting more complex and computationally taxing AI-based planning-based transformations. The inconsistency problem that occurs while adapting existing service composition solutions is addressed with a novel methodology based on Constraint Satisfaction Problem (CSP). From the outset, we adopted a pragmatic approach that focused on delivering an automated Web services discovery and composition solution with the minimum possible involvement of all composition participants: the service provider, the requestor and the service composer. The qualitative evaluation of the framework and the composition tools, together with the performance study of the XSCBR framework has verified that we were successful in achieving our goal

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page