Rule-Based Intelligence on the Semantic Web: Implications for Military Capabilities

Rules are a key element of the Semantic Web vision, promising to provide a foundation for reasoning capabilities that underpin the intelligent manipulation and exploitation of information content. Although ontologies provide the basis for some forms of reasoning, it is unlikely that ontologies, by themselves, will support the range of knowledge-based services that are likely to be required on the Semantic Web. As such, it is important to consider the contribution that rule-based systems can make to the realization of advanced machine intelligence on the Semantic Web. This report aims to review the current state-of-the-art with respect to semantic rule-based technologies. It provides an overview of the rules, rule languages and rule engines that are currently available to support ontology-based reasoning, and it discusses some of the limitations of these technologies in terms of their inability to cope with uncertain or imprecise data and their poor performance in some reasoning contexts. This report also describes the contribution of reasoning systems to military capabilities, and suggests that current technological shortcomings pose a significant barrier to the widespread adoption of reasoning systems within the defence community. Some solutions to these shortcomings are presented and a timescale for technology adoption within the military domain is proposed. It is suggested that application areas such as semantic integration, semantic interoperability, data fusion and situation awareness provide the best opportunities for technology adoption within the 2015 timeframe. Other capabilities, such as decision support and the emulation of human-style reasoning capabilities are seen to depend on the resolution of significant challenges that may hinder attempts at technology adoption and exploitation within the 2020 timeframe

Blueprint model and language for engineering cloud applications

Abstract: The research presented in this thesis is positioned within the domain of engineering CSBAs. Its contribution is twofold: (1) a uniform specification language, called the Blueprint Specification Language (BSL), for specifying cloud services across several cloud vendors and (2) a set of associated techniques, called the Blueprint Manipulation Techniques (BMTs), for publishing, querying, and composing cloud service specifications with aim to support the flexible design and configuration of an CSBA.

Developing a kidney and urinary pathway knowledge base

<p>Abstract</p> <p>Background</p> <p>Chronic renal disease is a global health problem. The identification of suitable biomarkers could facilitate early detection and diagnosis and allow better understanding of the underlying pathology. One of the challenges in meeting this goal is the necessary integration of experimental results from multiple biological levels for further analysis by data mining. Data integration in the life science is still a struggle, and many groups are looking to the benefits promised by the Semantic Web for data integration.</p> <p>Results</p> <p>We present a Semantic Web approach to developing a knowledge base that integrates data from high-throughput experiments on kidney and urine. A specialised KUP ontology is used to tie the various layers together, whilst background knowledge from external databases is incorporated by conversion into RDF. Using SPARQL as a query mechanism, we are able to query for proteins expressed in urine and place these back into the context of genes expressed in regions of the kidney.</p> <p>Conclusions</p> <p>The KUPKB gives KUP biologists the means to ask queries across many resources in order to aggregate knowledge that is necessary for answering biological questions. The Semantic Web technologies we use, together with the background knowledge from the domain’s ontologies, allows both rapid conversion and integration of this knowledge base. The KUPKB is still relatively small, but questions remain about scalability, maintenance and availability of the knowledge itself.</p> <p>Availability</p> <p>The KUPKB may be accessed via <url>http://www.e-lico.eu/kupkb</url>.</p

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

Knowledge Reuse Through Electronic Knowledge Repositories: An Empirical Study And Ontological Improvement Effort For The Manufacturing Industry

Knowledge management adoption is growing, and will continue to grow in no small part because of its recent inclusion into the ISO 9001 quality standard. As organizations look towards ways in which to manage their knowledge, the codification of explicit knowledge through Knowledge Management Systems (KMS) and Electronic Knowledge Repositories (EKRs) will undoubtedly gain more interest. An EKR is a form of KMS that emphasizes the codification and storage of organizational expertise for the purposes of Knowledge Reuse (KRU). Unfortunately, the factors surrounding KRU are not well understood. While previous studies have viewed EKR usage from a narrow perspective, a broader and interconnected view of KRU via EKRs has yet to emerge. Additionally, while there have been numerous benefits linked to EKRs, there are still issues that limit their utility, particularly in the manufacturing arena where information complexity and geography have made it increasingly difficult to share knowledge. Hence, this research employed a two pronged approach. First, using a multi-theoretical perspective to model KRU via EKRs, a quantitative study was conducted and identified several socio-technical factors that predicted greater KRU. These factors had not been previously modeled within the context of KRU via EKRs, and hence add to both the theoretical and practical implications of the domain. Additionally, the KRU construct was also tied to a back end resulting outcome view that was informed by the Expectation Confirmation Model (ECM). Through this view, the research quantitatively validated that KRU not only predicted greater performance, but also impacted greater knowledge sharing and continuance of use. This ancillary benefit helps to reinforce the importance of EKRs in that additional gains are manifested along with the core component of KRU. Second, the research extended the capability of manufacturing EKRs by developing a holistic design and process based ontology that connects key concepts within these domains to provide an overall interconnected view. Additionally, to ensure the relevance of the ontology, a mature and globally recognized industry standard was used as the basis to develop it. The ontology was then formalized and tested via Semantic Web tools: Protege, RDF, and SPARQL. The results demonstrate an improved approach to knowledge recall by providing rich and accurate query returns. The ability to use standalone and federated queries to effectively cull the complexity of this interconnected domain is an enhancement to keyword based and traditional relational database approaches. Additionally, to assist with greater industry adoption a systematic and constructive approach for developing and operationalizing the ontology is provided. Finally, in the spirit of the program in which this dissertation is presented, rounding out the research effort are broader organizational management recommendations for overall knowledge management. Referencing industry targeted literature and syncing them with findings from these two research efforts, several pragmatic and sequentially logical approaches to knowledge management are offered

Model driven validation approach for enterprise architecture and motivation extensions

As the endorsement of Enterprise Architecture (EA) modelling continues to grow in diversity and complexity, management of its schema, artefacts, semantics and relationships has become an important business concern. To maintain agility and flexibility within competitive markets, organizations have also been compelled to explore ways of adjusting proactively to innovations, changes and complex events also by use of EA concepts to model business processes and strategies. Thus the need to ensure appropriate validation of EA taxonomies has been considered severally as an essential requirement for these processes in order to exert business motivation; relate information systems to technological infrastructure. However, since many taxonomies deployed today use widespread and disparate modelling methodologies, the possibility to adopt a generic validation approach remains a challenge. The proliferation of EA methodologies and perspectives has also led to intricacies in the formalization and validation of EA constructs as models often times have variant schematic interpretations. Thus, disparate implementations and inconsistent simulation of alignment between business architectures and heterogeneous application systems is common within the EA domain (Jonkers et al., 2003). In this research, the Model Driven Validation Approach (MDVA) is introduced. MDVA allows modelling of EA with validation attributes, formalization of the validation concepts and transformation of model artefacts to ontologies. The transformation simplifies querying based on motivation and constraints. As the extended methodology is grounded on the semiotics of existing tools, validation is executed using ubiquitous query language. The major contributions of this work are the extension of a metamodel of Business Layer of an EAF with Validation Element and the development of EAF model to ontology transformation Approach. With this innovation, domain-driven design and object-oriented analysis concepts are applied to achieve EAF model’s validation using ontology querying methodology. Additionally, the MDVA facilitates the traceability of EA artefacts using ontology graph patterns