OntoWeaver S: supporting the design of knowledge portals

This paper presents OntoWeaver-S, an ontology-based infrastructure for building knowledge portals. In particular, OntoWeaver-S is integrated with a comprehensive web service platform, IRS-II, for the publication, discovery, and execution of web services. In this way, OntoWeaver-S supports the access and provision of remote web services for knowledge portals. Moreover, it provides a set of comprehensive site ontologies to model and represent knowledge portals, and thus is able to offer high level support for the design and development process. Finally, OntoWeaver-S provides a set of powerful tools to support knowledge portals at design time as well as at run time

Enterprise application reuse: Semantic discovery of business grid services

Web services have emerged as a prominent paradigm for the development of distributed software systems as they provide the potential for software to be modularized in a way that functionality can be described, discovered and deployed in a platform independent manner over a network (e.g., intranets, extranets and the Internet). This paper examines an extension of this paradigm to encompass ‘Grid Services’, which enables software capabilities to be recast with an operational focus and support a heterogeneous mix of business software and data, termed a Business Grid - "the grid of semantic services". The current industrial representation of services is predominantly syntactic however, lacking the fundamental semantic underpinnings required to fulfill the goals of any semantically-oriented Grid. Consequently, the use of semantic technology in support of business software heterogeneity is investigated as a likely tool to support a diverse and distributed software inventory and user. Service discovery architecture is therefore developed that is (a) distributed in form, (2) supports distributed service knowledge and (3) automatically extends service knowledge (as greater descriptive precision is inferred from the operating application system). This discovery engine is used to execute several real-word scenarios in order to develop and test a framework for engineering such grid service knowledge. The examples presented comprise software components taken from a group of Investment Banking systems. Resulting from the research is a framework for engineering servic

An actionable knowledge discovery system in regular sports services

This work presents an actionable knowledge discovery system for real user needs with three steps. In the first step, it extracts and transforms existing data in the databases of the ERP and CRM systems of the sports facilities and loads them into a Data Warehouse. In a second phase, predictive models are applied to identify profiles more susceptible to abandonment. Finally, in the third phase, based on the previous models, experimental planning is carried out, with test and control groups, in order to find concrete actions for customer retention.info:eu-repo/semantics/publishedVersio

Approaches to Semantic Web Services: An Overview and Comparison

Abstract. The next Web generation promises to deliver Semantic Web Services (SWS); services that are self-described and amenable to automated discovery, composition and invocation. A prerequisite to this, however, is the emergence and evolution of the Semantic Web, which provides the infrastructure for the semantic interoperability of Web Services. Web Services will be augmented with rich formal descriptions of their capabilities, such that they can be utilized by applications or other services without human assistance or highly constrained agreements on interfaces or protocols. Thus, Semantic Web Services have the potential to change the way knowledge and business services are consumed and provided on the Web. In this paper, we survey the state of the art of current enabling technologies for Semantic Web Services. In addition, we characterize the infrastructure of Semantic Web Services along three orthogonal dimensions: activities, architecture and service ontology. Further, we examine and contrast three current approaches to SWS according to the proposed dimensions

Semantic Description, Publication and Discovery of Workflows in myGrid

The bioinformatics scientific process relies on in silico experiments, which are experiments executed in full in a computational environment. Scientists wish to encode the designs of these experiments as workflows because they provide minimal, declarative descriptions of the designs, overcoming many barriers to the sharing and re-use of these designs between scientists and enable the use of the most appropriate services available at any one time. We anticipate that the number of workflows will increase quickly as more scientists begin to make use of existing workflow construction tools to express their experiment designs. Discovery then becomes an increasingly hard problem, as it becomes more difficult for a scientist to identify the workflows relevant to their particular research goals amongst all those on offer. While many approaches exist for the publishing and discovery of services, there have been few attempts to address where and how authors of experimental designs should advertise the availability of their work or how relevant workflows can be discovered with minimal effort from the user. As the users designing and adapting experiments will not necessarily have a computer science background, we also have to consider how publishing and discovery can be achieved in such a way that they are not required to have detailed technical knowledge of workflow scripting languages. Furthermore, we believe they should be able to make use of others' expert knowledge (the semantics) of the given scientific domain. In this paper, we define the issues related to the semantic description, publishing and discovery of workflows, and demonstrate how the architecture created by the myGrid project aids scientists in this process. We give a walk-through of how users can construct, publish, annotate, discover and enact workflows via the user interfaces of the myGrid architecture; we then describe novel middleware protocols, making use of the Semantic Web technologies RDF and OWL to support workflow publishing and discovery

Collaborative tagging supported knowledge discovery

Web 2.0 has brought about a new sort of user centred services which rely a great deal on flexible organizational capabilities designed for user-supplied organization. Collaborative tagging is especially interesting in this context and this article explores what this kind of organization in connection with some Web 2.0 principles means for knowledge discovery in various ways. To fully explore this, the article defines collaborative tagging and gives an overview of collaborative tagging in general, of services using it and of tags themselves. It concludes with mechanisms this kind of approach to knowledge organization provides for knowledge discovery

An empirical approach for semantic Web services discovery

Component retrieval/discovery is a well-established research direction in Software Engineering. With the surge of Service-Oriented Architecture (SOA), service discovery has become increasingly crucial. However, the public UDDI Business Registry the primary service discovery mechanism over the Internet has been shut down permanently since 2006. Moreover, keyword-based service discovery is insufficient in coping with complex discovery requirements posed by modern software developers. In this paper, we propose an empirical semantic based Web service discovery approach. It provides an automatic Web service discovery mechanism that can locate relevant Web services based on concepts rather than keywords. The major contribution of this paper is three fold. First we articulate three requirements that software developers often raise during the component/service development and discovery process. Next, we propose the application of Latent Semantic Analysis into the area of Web services discovery. To our best knowledge, little work has been done in this area which leverages concept-based Information Retrieval models in service discovery. Last, we provide a proof-of-concept system prototype that can suffice three specific requirements of semantic service discovery

Distributed Knowledge Discovery in Large Scale Peer-to-Peer Networks

Explosive growth in the availability of various kinds of data in distributed locations has resulted in unprecedented opportunity to develop distributed knowledge discovery (DKD) techniques. DKD embraces the growing trend of merging computation with communication by performing distributed data analysis and modeling with minimal communication of data. Most of the current state-of-the-art DKD systems suffer from the lack of scalability, robustness and adaptability due to their dependence on a centralized model for building the knowledge discovery model. Peer-to-Peer networks offer a better scalable and fault-tolerant computing platform for building distributed knowledge discovery models than client-server based platforms. Algorithms and communication protocols have been developed for file search and discovery services in peer-to-peer networks. The file search algorithms are concerned with identification of a peer and discovery of a file on that specified peer, so most of the current peer-to-peer networks for file search act as directory services. The problem of distributed knowledge discovery is different from file search services, however new issues and challenges have to be addressed. The algorithms and communication protocols for knowledge discovery deal with implementing algorithms by which every peer in the network discovers the correct knowledge discovery model, as if it were given the combined database. Therefore, algorithms and communication protocols for DKD mainly deal with distributed computing. The distributed computations are entirely asynchronous, impose very little communication overhead, transparently tolerate network topology changes and peer failures and quickly adjust to changes in the data as they occur. Another important aspect of the distributed computations in a peer-to-peer network is that most of the communication between peer nodes is local i.e. the knowledge discovery model is learned at each peer using information gathered from a very small neighborhood, whose size is independent of the size of the peer-to-peer network. The peer-to-peer constraints on data and/or computing are the hard ones, so the challenge is to show that it is still possible to extract useful information from the distributed data effectively and dependably. The implementation of a distributed algorithm in an asynchronous and decentralized environment is the hardest challenge. DKD in a peer-to-peer network raises issues related to impracticality of global communications and global synchronization, on-the-fly data updates, lack of control, accuracy of computation, the need to share resources with other applications, and frequent failure and recovery of resources. We propose a methodology based on novel distributed algorithms and communication protocols to perform DKD in a peer-to-peer network. We investigate the performance of our algorithms and communication protocols by means of analysis and simulations