Semantic Information Based Web Service Discovery

Web service discovery is an important part of Web service applications. With the increase of the Web services, there come the problems that we must face to. The technology of traditional Web service discovery is based on the matching foundation of key words by combining WSDL and UDDI standardization. However, it is short of the description of semantic information, which means a low level of intelligence. Thus, it leads to low exactness and completeness. In the light of this issue, the paper draws the concept of semantic information into the research of Web service discovery. Through WordNet, it expands service description, raises semantic information, and makes semantic match possible. The paper puts forward a model based on semantic for Web service discovery, analyzes the function structure and the relationship between component parts of the model, moreover, it describes the work flow of the model. At the same time, the paper enlarges UDDI information and gives a matching algorithm on the similarity calculation through semantic analysis when finding the matched service

Multidimensional Information-Based Web Service Representation Method

With the development of service-oriented architecture (SOA) technology, the amount of Web service is increasing. Clustering or classifying Web services correctly are an effective way to improve the quality of Web service discovery and the efficiency of Web service composition. However, the existing Web service modeling methods (such as latent Dirichlet allocation topic model) are difficult to obtain accurate and effective Web service representation from a sparse Web service dataset for Web service clustering. To solve this problem, this paper proposes a multi-dimensional information-based Web service representation method (MISR). First, it generates word vectors which contain topic and semantic information implicit in Web service description with Gaussian mixture model and Word2Vec. Then, the MISR algorithm combines tag-word relationship, popularity, and co-occurrence information together for generating multi-dimensional information Web service representation. Web service clustering and Web service classification are used for evaluating the effectiveness of MISR. Based on a real-world dataset of API services, the experiment results show that compared with LDA, Word2Vec, Doc2Vec, WT-LDA, HDP-SOM, GWSC, the proposed method has 38.8%, 54.5%, 15.3%, 33.3%, 44.7%, 9.7% improvement in Micro-F1 value

Contextualized B2B Registries

Abstract. Service discovery is a fundamental concept underpinning the move towards dynamic service-oriented business partnerships. The business process for integrating service discovery and underlying registry technologies into business relationships, procurement and project management functions has not been examined and hence existing Web Service registries lack capabilities required by business today. In this paper we present a novel contextualized B2B registry that supports dynamic registration and discovery of resources within management contexts to ensure that the search space is constrained to the scope of authorized and legitimate resources only. We describe how the registry has been deployed in three case studies from important economic sectors (aerospace, automotive, pharmaceutical) showing how contextualized discovery can support distributed product development processes

A Requirement-centric Approach to Web Service Modeling, Discovery, and Selection

Service-Oriented Computing (SOC) has gained considerable popularity for implementing Service-Based Applications (SBAs) in a flexible\ud and effective manner. The basic idea of SOC is to understand users'\ud requirements for SBAs first, and then discover and select relevant\ud services (i.e., that fit closely functional requirements) and offer\ud a high Quality of Service (QoS). Understanding users’ requirements\ud is already achieved by existing requirement engineering approaches\ud (e.g., TROPOS, KAOS, and MAP) which model SBAs in a requirement-driven\ud manner. However, discovering and selecting relevant and high QoS\ud services are still challenging tasks that require time and effort\ud due to the increasing number of available Web services. In this paper,\ud we propose a requirement-centric approach which allows: (i) modeling\ud users’ requirements for SBAs with the MAP formalism and specifying\ud required services using an Intentional Service Model (ISM); (ii)\ud discovering services by querying the Web service search engine Service-Finder\ud and using keywords extracted from the specifications provided by\ud the ISM; and(iii) selecting automatically relevant and high QoS services\ud by applying Formal Concept Analysis (FCA). We validate our approach\ud by performing experiments on an e-books application. The experimental\ud results show that our approach allows the selection of relevant and\ud high QoS services with a high accuracy (the average precision is\ud 89.41%) and efficiency (the average recall is 95.43%)

Grid service discovery with rough sets

Copyright [2008] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.The computational grid is evolving as a service-oriented computing infrastructure that facilitates resource sharing and large-scale problem solving over the Internet. Service discovery becomes an issue of vital importance in utilising grid facilities. This paper presents ROSSE, a Rough sets based search engine for grid service discovery. Building on Rough sets theory, ROSSE is novel in its capability to deal with uncertainty of properties when matching services. In this way, ROSSE can discover the services that are most relevant to a service query from a functional point of view. Since functionally matched services may have distinct non-functional properties related to Quality of Service (QoS), ROSSE introduces a QoS model to further filter matched services with their QoS values to maximise user satisfaction in service discovery. ROSSE is evaluated in terms of its accuracy and efficiency in discovery of computing services

The Semantic Automated Discovery and Integration (SADI) Web service Design-Pattern, API and Reference Implementation

Background. 
The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community.

Description. 
SADI – Semantic Automated Discovery and Integration – is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services “stack”, SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers.

Conclusions.
SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behavior we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services can be explored in a manner very similar to data housed in static triple-stores, thus facilitating the intersection of Web services and Semantic Web technologies