Semantic-based taxonomic categorization of Web services

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Proceedings of the 1st International Workshop on Semantic Matchmaking and Resource Retrieval, SMR 06With the envisioned proliferation of Web services available on the WWW and private repositories, new and better support techniques are needed for service discovery and organization to stay manageable. Service classification under hierarchic taxonomies is commonly a key feature for properly organizing service repositories in a rational way, as well as a good foundation for sophisticated retrieval techniques. In this paper, a heuristic approach for the semi-automatic classification of (semantic) Web services is proposed, based on matching new unclassified services to previously classified ones in a given corpus. This hypothesis is validated by an experimental test and the comparison with results achieved by other approaches.This research was supported by the Spanish Ministry of Industry, Tourism and Commerce (CDTI05-0436) and the Ministry of Science and Education (TIN2005-0685). Thanks are due to Rubén Lara for all his help and feedback on the research presented

On the Combination of Textual and Semantic Descriptions for Automated Semantic Web Service Classification

Abstract Semantic Web services have emerged as the solution to the need for automating several aspects related to service-oriented architectures, such as service discovery and composition, and they are realized by combining Semantic Web technologies and Web service standards. In the present paper, we tackle the problem of automated classification of Web services according to their application domain taking into account both the textual description and the semantic annotations of OWL-S advertisements. We present results that we obtained by applying machine learning algorithms on textual and semantic descriptions separately and we propose methods for increasing the overall classification accuracy through an extended feature vector and an ensemble of classifiers

METEOR-S Web Service Annotation Framework with Machine Learning Classification

Researchers have recognized the need for more expressive descriptions of Web services. Most approaches have suggested using ontologies to either describe the Web services or to annotate syntactical descriptions of Web services. Earlier approaches are typically manual, and the capability to support automatic or semi-automatic annotation is needed. The METEOR-S Web Service Annotation Framework (MWSAF) created at the LSDIS Lab at the University of Georgia leverages schema matching techniques for semi-automatic annotation. In this paper, we present an improved version of MWSAF. Our preliminary investigation indicates that, by replacing the schema matching technique currently used for the categorization with a Naïve Bayesian Classifier, we can match web services with ontologies faster and with higher accuracy

Final CONNECT Architecture

Interoperability remains a fundamental challenge when connecting heterogeneous systems which encounter and spontaneously communicate with one another in pervasive computing environments. This challenge is exasperated by the highly heterogeneous technologies employed by each of the interacting parties, i.e., in terms of hardware, operating system, middleware protocols, and application protocols. The key aim of the CONNECT project is to drop this heterogeneity barrier and achieve universal interoperability. Here we report on the revised CONNECT architecture, highlighting the integration of the work carried out to integrate the CONNECT enablers developed by the different partners; in particular, we present the progress of this work towards a finalised concrete architecture. In the third year this architecture has been enhanced to: i) produce concrete CONNECTors, ii) match networked systems based upon their goals and intent, and iii) use learning technologies to find the affordance of a system. We also report on the application of the CONNECT approach to streaming based systems, further considering exploitation of CONNECT in the mobile environment

K.: METEOR-S Web Service Annotation Framework with Machine Learning Classification

Researchers have recognized the need for more expressive descriptions of Web services. Most approaches have suggested using ontologies to either describe the Web services or to annotate syntactical descriptions of Web services. Earlier approaches are typically manual, and capability to support automatic or semi-automatic annotation is needed. The METEOR-S Web Service Annotation Framework (MWSAF) created at the LSDIS Lab at the University of Georgia leverages schema matching techniques for semi-automatic annotation. In this paper, we present an improved version of MWSAF. Our preliminary investigation indicates that, by replacing the schema matching technique currently used for the categorization with a Naïve Bayesian Classifier, we can match web services with ontologies faster and with higher accuracy

Dynamic Connector Synthesis: Principles, Methods, Tools and Assessment

CONNECT adopts a revolutionary approach to the seamless networking of digital systems, that is, onthe- fly synthesis of the connectors via which networked systems communicate. Within CONNECT, the role of the WP3 work package is to devise automated and efficient approaches to the synthesis of such emergent connectors, provided the behavioral specification of the components to be connected. Thanks to WP3 scientific and technology development, emergent connectors can be synthesized on the fly as networked systems get discovered, implementing the necessary mediation between networked systems' protocols, from application down to middleware layers. This document being the final report about WP3 achievements, it outlines both: (i) specific contributions over the reporting period, and (ii) overall contributions in the area of automated, on-the-fly protocol mediation, from theory to supporting tool

A semantic framework for event-driven service composition

Title from PDF of title page, viewed on September 14, 2011VitaDissertation advisor: Yugyung LeeIncludes bibliographical references (p. 289-329)Thesis (Ph.D)--School of Computing and Engineering. University of Missouri--Kansas City, 2011Service Oriented Architecture (SOA) has become a popular paradigm for designing distributed systems where loosely coupled services (i.e. computational entities) can be integrated seamlessly to provide complex composite services. Key challenges are discovery of the required services using their formal descriptions and their coherent composition in a timely manner. Most service descriptions are written in XML-based languages that are syntactic, creating linguistic ambiguity during service matchmaking. Furthermore, existing models that implement SOA have mostly middleware-controlled synchronous request/replybased runtime binding of services that incur undesirable service latency. In addition, they impose expensive state monitoring overhead on the middleware. Some newer event-driven models introduce asynchronous publish/subscribe-based event notifications to consumer applications and services. However, they require an event-library that stores definitions of all possible system events, which is impractical in an open and dynamic system. The objective of this study is to efficiently address on-demand consumer requests with minimum service latency and maximum consumer utility. It focuses on semantic eventdriven service composition. For efficient semantic service discovery, the dissertation proposes a novel service learning algorithm called Semantic Taxonomic Clustering (STC). The algorithm utilizes semantic service descriptions to cluster services into functional categories for pruning search space during service discovery and composition. STC utilizes a dynamic bit-encoding algorithm called DL-Encoding that enables linear time bit operationbased semantic matchmaking as compared to expensive reasoner-based semantic matchmaking. The algorithm shows significant improvement in performance and accuracy over some of the important service category algorithms reported in the literature. A novel user-friendly and computationally efficient query model called Desire-based Query Model (DQM) is proposed for formally specifying service queries. STC and DQM serve as the building block for the dual framework that is the core contribution of this dissertation: (i) centralized ALNet (Activity Logic Network) platform and (ii) distributed agentbased SMARTSPACE platform. The former incorporates a middleware controlled service composition algorithm called ALNetComposer while the latter includes the SmartDeal purely distributed composition algorithm. The query response accuracy and performance were evaluated for both the algorithms under simulated event-driven SOA environments. The experimental results show that various environmental parameters, such as domain diversity and scope, size and complexity of the SOA system, and dynamicity of the SOA system, significantly affect accuracy and performance of the proposed model. This dissertation demonstrates that the functionality and scalability of the proposed framework are acceptable for relatively static and domain specific environments as well as large, diverse, and highly dynamic environments. In summary, this dissertation addresses the key design issues and problems in the area of asynchronous and pro-active event-driven service composition.Introduction -- Research background -- Semantic service matchmaking & query modeling -- Service organization by learning service category -- ALNet: event-driven platform for service composition -- SMARTSPACE: distributed multi-agent based event-handeling -- Conclusion & future wor