Expressing Adaptation Strategies Using Adaptation Patterns

Today, there is a real challenge to enable personalized access to information. Several systems have been proposed to address this challenge including Adaptive Hypermedia Systems (AHSs). However, the specification of adaptation strategies remains a difficult task for creators of such systems. In this paper, we consider the problem of the definition of adaptation strategies at a high level. We present two main contributions: a typology of elementary adaptation patterns for the adaptation of navigation; and a process to generate adaptation strategies based on the use and the semi-automatic combination of patterns. We also describe how the generated adaptation strategies can be integrated into existing AHSs. A prototype has been implemented and an experiment in the e-learning domain has been conducted with a group of volunteers. This experiment shows that our pattern based approach for defining adaptation strategies is more suitable than those based on "traditional" AH languages

An Ontology Centric Architecture For Mediating Interactions In Semantic Web-Based E-Commerce Environments

Information freely generated, widely distributed and openly interpreted is a rich source of creative energy in the digital age that we live in. As we move further into this irrevocable relationship with self-growing and actively proliferating information spaces, we are also finding ourselves overwhelmed, disheartened and powerless in the presence of so much information. We are at a point where, without domain familiarity or expert guidance, sifting through the copious volumes of information to find relevance quickly turns into a mundane task often requiring enormous patience. The realization of accomplishment soon turns into a matter of extensive cognitive load, serendipity or just plain luck. This dissertation describes a theoretical framework to analyze user interactions based on mental representations in a medium where the nature of the problem-solving task emphasizes the interaction between internal task representation and the external problem domain. The framework is established by relating to work in behavioral science, sociology, cognitive science and knowledge engineering, particularly Herbert Simon’s (1957; 1989) notion of satisficing on bounded rationality and Schön’s (1983) reflective model. Mental representations mediate situated actions in our constrained digital environment and provide the opportunity for completing a task. Since assistive aids to guide situated actions reduce complexity in the task environment (Vessey 1991; Pirolli et al. 1999), the framework is used as the foundation for developing mediating structures to express the internal, external and mental representations. Interaction aids superimposed on mediating structures that model thought and action will help to guide the “perpetual novice” (Borgman 1996) through the vast digital information spaces by orchestrating better cognitive fit between the task environment and the task solution. This dissertation presents an ontology centric architecture for mediating interactions is presented in a semantic web based e-commerce environment. The Design Science approach is applied for this purpose. The potential of the framework is illustrated as a functional model by using it to model the hierarchy of tasks in a consumer decision-making process as it applies in an e-commerce setting. Ontologies are used to express the perceptual operations on the external task environment, the intuitive operations on the internal task representation, and the constraint satisfaction and situated actions conforming to reasoning from the cognitive fit. It is maintained that actions themselves cannot be enforced, but when the meaning from mental imagery and the task environment are brought into coordination, it leads to situated actions that change the present situation into one closer to what is desired. To test the usability of the ontologies we use the Web Ontology Language (OWL) to express the semantics of the three representations. We also use OWL to validate the knowledge representations and to make rule-based logical inferences on the ontological semantics. An e-commerce application was also developed to show how effective guidance can be provided by constructing semantically rich target pages from the knowledge manifested in the ontologies

Knowledge modelling of emerging technologies for sustainable building development

In the quest for improved performance of buildings and mitigation of climate change, governments are encouraging the use of innovative sustainable building technologies. Consequently, there is now a large amount of information and knowledge on sustainable building technologies over the web. However, internet searches often overwhelm practitioners with millions of pages that they browse to identify suitable innovations to use on their projects. It has been widely acknowledged that the solution to this problem is the use of a machine-understandable language with rich semantics - the semantic web technology. This research investigates the extent to which semantic web technologies can be exploited to represent knowledge about sustainable building technologies, and to facilitate system decision-making in recommending appropriate choices for use in different situations. To achieve this aim, an exploratory study on sustainable building and semantic web technologies was conducted. This led to the use of two most popular knowledge engineering methodologies - the CommonKADS and "Ontology Development 101" in modelling knowledge about sustainable building technology and PV -system domains. A prototype system - Photo Voltaic Technology ONtology System (PV -TONS) - that employed sustainable building technology and PV -system domain knowledge models was developed and validated with a case study. While the sustainable building technology ontology and PV -TONS can both be used as generic knowledge models, PV -TONS is extended to include applications for the design and selection of PV -systems and components. Although its focus was on PV -systems, the application of semantic web technologies can be extended to cover other areas of sustainable building technologies. The major challenges encountered in this study are two-fold. First, many semantic web technologies are still under development and very unstable, thus hindering their full exploitation. Second, the lack of learning resources in this field steepen the learning curve and is a potential set-back in using semantic web technologies

SYSTEMS ENGINEERING DESIGN AND TRADEOFF ANALYSIS WITH RDF GRAPH MODELS

As engineering systems become increasingly complex the need for automation arises. This thesis proposes a multi-level framework for design of a home theater system cast as a component-selection design problem. It explores the extent to which the resource description framework (RDF) and Python can be used in a software pipeline for systems engineering design and trade-off analysis. The software pipeline models and visualizes RDF graphs, implements inference rules for the step-by-step selection of design component combinations that satisfy system requirements, identifies non-inferior Pareto-Optimal design solutions, and tracks the size of the RDF graphs during execution of the pipeline. The use of RDF and Python for automation provides a simplified replacement for present-day Semantic Web tools and technologies

Investigating the role of knowledge management in driving the development of an effective business process architecture

Business Process Architecture (BPA) modelling methods are not dynamic and flexible enough to effectively respond to changes. This may create a barrier that contributes to a lack of knowledge and learning capabilities which can affect the BPA regarding its support for a sustainable competitive advantage in an organisation. New business challenges are driving business enterprises to adopt Knowledge Management (KM) as one means of making a positive difference to their performance and competitiveness. However, shortcomings still remain in utilising knowledge management in business processes where efforts were mostly directed towards the integration of knowledge management with business process management but not including BPAs. The idea of applying KM as a memory to be timely retrieved and updated as needed is no longer sufficient. The resource-based view suggests a number of key factors to be investigated and taken into consideration during the development of knowledge management systems. These key factors are known as Knowledge Management Enablers (KMEs). KMEs are crucial for representing KM and understanding how knowledge is created, shared and disseminated. They are also essential to identify available assets and resources, and to clarify how organisational capabilities are created and utilised.This research is aimed at investigating the role of the knowledge management enablers in the development of an effective process architecture. An effective process architecture needs to be dynamic and supports a sustainable competitive advantage in an organisation. Identifying the KMEs, selecting an appropriate BPA method, aligning these KMEs with this method as well as undertaking a critical evaluation of this alignment are the main objectives set for this research. In order to accomplish the research aim and objectives, a resource-based and semantic-enriched framework, namely the KMEOntoBPA has been designed using KMEs to drive the process of BPA development. Organisational structure, culture, information technology, leadership, knowledge context and business repository have been selected as representatives of the KMEs. The object-based BPA modelling, specifically the semantically enriched Riva BPA (srBPA) method, has been adopted in order to embrace the knowledge resources generated by KMEs and utilise them in the derivation and re-configuration of its constitutional elements. These knowledge resources are employed as business objects. They are considered as Candidate Essential Business Entities (CEBEs) in the Riva method, that characterise or represent a form of business of an organisation. The Design Science Research Methodology (DSRM) is used to guide the research phases with an emphasis on the design and development, demonstration and evaluation of the research framework. The KMEOntoBPA has been demonstrated using sufficient and representative core banking case studies of the Treasury, Deposits and Financing. These case studies have been applied to the DSRM iterations beginning with the Treasury as the 1st case study, followed by the Deposits and the Financing case studies.The results have revealed that KMEs utilisation provides an agile generation of representative CEBEs and their corresponding Riva BPA elements, which reflect the real business in each of the core banking business studies. This research also demonstrated the semantic Riva BPA method as an appropriate object-based method that is well aligned with KMEs in exploiting knowledge resources for the development of a dynamic BPA with reference to robustness and learning capabilities. In addition to these results, the research framework, i.e, the KMEOntoBPA has shown an understanding of the flow of knowledge in the bank and has provided several possible advantages such as the accuracy of service delivery and the improvement of the financial control. It also supports the sources of sustainable competitive advantage (SCA): technical capabilities, core competences and social capital.Finally, a number of significant contributions and artefacts have been attained. For example, there is the aKMEOnt which is the abstract ontology that utilises six KMEs in this research to investigate the effectiveness of using such KMEs in driving the development of the BPA. These contributions along with the research results provide a guide to future research directions such as using the aKMEOnt in the development of different business process modelling and deriving the Enterprise Information Architecture (EIA) and Service Oriented Architecture (SOA)

An Ontology-Based Approach for Closed-Loop Product Lifecycle Management

The main goal of the Product Lifecycle Management (PLM) is the management of all the data associated to a product during its lifecycle. Lifecycle data is being generated by events and actions (of various lifecycle agents which are humans and/or software systems) and it is distributed along the product's lifecycle phases: Beginning of Life (BOL) including design and manufacturing, Middle of Life (MOL) including usage and maintenance and End of Life (EOL) including recycling, disposal or other options. Closed-Loop PLM extends the meaning of PLM in order to close the loop of the information among the different lifecycle phases. The idea is that information of MOL could be used at the EOL stage to support deciding the most appropriate EOL option (especially to make decision for re-manufacturing and re-use) and combined with the EOL information it could be used as feedback in the BOL for improving the new generations of the product. Several PLM models have been developed utilising various technologies and methods towards providing aspects of the Closed-Loop PLM concept. Ontologies are rapidly becoming popular in various research fields. There is a tendency both in converting existing models into ontology-based models, and in creating new ontology-based models from scratch. The aim of this dissertation is to include the advantages and features provided by the ontologies into PLM models towards achieving Closed-Loop PLM. Hence, an ontology model of a Product Data and Knowledge Management Semantic Object Model for PLM has been developed. The transformation process of the model into an ontology-based one, using Web Ontology Language-Description Logic (OWL-DL), is described in detail. The background and the motives for converting existing PLM models to ontologies are also provided. The new model facilitates several of the OWL-DL capabilities, while maintaining previously achieved characteristics. Furthermore, case studies based on various application scenarios, are presented. These case studies deal with data integration and interoperability problems, in which a significant number of reasoning capabilities is implemented, and highlight the utilisation of the developed model. Moreover, in this work, a generic concept has been developed, tackling the time treatment in PLM models. Time is the only fundamental dimension which exists along the entire life of an artefact and it affects all artefacts and their qualities. Most commonly in PLM models, time is an attribute in parts such as "activities" and "events" or is a separate part of the model ("four dimensional models"). In this work the concept is that time should not be one part of the model, but it should be the basis of the model, and all other elements should be parts of it. Thus, we introduce the "Duration of Time concept". According to this concept all aspects and elements of a model are parts of time. Case studies demonstrate the applicability and the advantages of the concept in comparison to existing methodologies

An ontology-based semantic building post-occupancy evaluation framework and its application

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonCatering to sustainable development in Architecture, Engineering and Construction (AEC) industry, many building performance evaluation (BPE) schemas have been developed to support building assessment and aim to narrow down the performance gap. Post-Occupancy Evaluation (POE), viewed as a sub-process of BPE, is a systematic method to obtain feedback on building performance in use. However, building evaluation is a complex and knowledge-intensive process with scattered and fragmented knowledge, it is time-consuming and error-prone to acquire explicit knowledge. Benefiting from the advantages of Semantic Web technology in knowledge conceptualization, ontology, as the core of the Semantic Web, has been widely taken as an effective method for knowledge management, information representation and extraction, and logical inference in the AEC industry, especially in the BPE field. However, most of the existing ontologies in the AEC industry are lightweight ontologies that mainly focus on building a structured system to represent the specific domain knowledge or information, without developing formal axioms and constraints to provide higher expressivity. Moreover, the research focus of ontology in building assessment is mainly on energy-related fields, and there is not a comprehensive POE ontology yet, especially with the focus on building occupant satisfaction, which is the starting point of this research. This research develops an ontology-based post-occupancy evaluation framework dedicated to building performance assessment, with the ultimate aim of optimizing building operation and improving building occupants' use experience quality and well-being. In the developed framework, a heavyweight ontology is developed to structure the fragmented building performance assessment knowledge in the POE domain. In POE ontology, the building occupants' needs for building performance are generalized and classified, and the corresponded building performance assessment knowledge is formalized. In addition, a set of SWRL (Semantic Web Rule Language) rules and SQWRL (Semantic Query-Enhanced Web Rule Language) query rules are developed based on the benchmarking evaluation axioms to enable automatic rule-based reasoning and query in different identified application scenarios. This ontology model enables effective POE-related knowledge retrieving and sharing, and promotes its implementation in the POE domain. To validate the developed framework, a case study is carried out facilitated by the Building Use Studies (BUS) Methodology to illustrate its feasibility and effectiveness in different application scenarios. This research concludes that the proposed ontology-based POE framework has the capability to conduct a multi-objective and multi-criteria POE assessment at the building operation stage and provide a multi-criteria optimised solution

Developing a computational framework for explanation generation in knowledge-based systems and its application in automated feature recognition

A Knowledge-Based System (KBS) is essentially an intelligent computer system which explicitly or tacitly possesses a knowledge repository that helps the system solve problems. Researches focusing on building KBSs for industrial applications to improve design quality and shorten research cycle are increasingly attracting interests. For the early models, explanability is considered as one of the major benefits of using KBSs since that most of them are generally rule-based systems and the explanation can be generated based on the rule traces of the reasoning behaviors. With the development of KBS, the definition of knowledge base is becoming much more general than just using rules, and the techniques used to solve problems in KBS are far more than just rule-based reasoning. Many Artificial Intelligence (AI) techniques are introduced, such as neural network, genetic algorithm, etc. The effectiveness and efficiency of KBS are thus improved. However, as a trade-off, the explanability of KBS is weakened. More and more KBSs are conceived as black-box systems that do not run transparently to users, resulting in loss of trusts for the KBSs. Developing an explanation model for modern KBSs has a positive impact on user acceptance of the KBSs and the advices they provided. This thesis proposes a novel computational framework for explanation generation in KBS. Different with existing models which are usually built inside a KBS and generate explanations based on the actual decision making process, the explanation model in our framework stands outside the KBS and attempts to generate explanations through the production of an alternative justification that is unrelated to the actual decision making process used by the system. In this case, the knowledge and reasoning approaches in the explanation model can be optimized specially for explanation generation. The quality of explanation is thus improved. Another contribution in this study is that the system aims to cover three types of explanations (where most of the existing models only focus on the first two): 1) decision explanation, which helps users understand how a KBS reached its conclusion; 2) domain explanation, which provides detailed descriptions of the concepts and relationships within the domain; 3) software diagnostic, which diagnoses user observations of unexpected behaviors of the system or some relevant domain phenomena. The framework is demonstrated with a case of Automated Feature Recognition (AFR). The resulting explanatory system uses Semantic Web languages to implement an individual knowledge base only for explanatory purpose, and integrates a novel reasoning approach for generating explanations. The system is tested with an industrial STEP file, and delivers good quality explanations for user queries about how a certain feature is recognized