INFRAWEBS BPEL-Based Editor for Creating the Semantic Web Services Description

INFRAWEBS project [INFRAWEBS] considers usage of semantics for the complete lifecycle of Semantic Web processes, which represent complex interactions between Semantic Web Services. One of the main initiatives in the Semantic Web is WSMO framework, aiming at describing the various aspects related to Semantic Web Services in order to enable the automation of Web Service discovery, composition, interoperation and invocation. In the paper the conceptual architecture for BPEL-based INFRAWEBS editor is proposed that is intended to construct a part of WSMO descriptions of the Semantic Web Services. The semantic description of Web Services has to cover Data, Functional, Execution and QoS semantics. The representation of Functional semantics can be achieved by adding the service functionality to the process description. The architecture relies on a functional (operational) semantics of the Business Process Execution Language for Web Services (BPEL4WS) and uses abstract state machine (ASM) paradigm. This allows describing the dynamic properties of the process descriptions in terms of partially ordered transition rules and transforming them to WSMO framework.

Applying Associative Classifier PGN for Digitised Cultural Heritage Resource Discovery

Resource discovery is one of the key services in digitised cultural heritage collections. It requires intelligent mining in heterogeneous digital content as well as capabilities in large scale performance; this explains the recent advances in classification methods. Associative classifiers are convenient data mining tools used in the field of cultural heritage, by applying their possibilities to taking into account the specific combinations of the attribute values. Usually, the associative classifiers prioritize the support over the confidence. The proposed classifier PGN questions this common approach and focuses on confidence first by retaining only 100% confidence rules. The classification tasks in the field of cultural heritage usually deal with data sets with many class labels. This variety is caused by the richness of accumulated culture during the centuries. Comparisons of classifier PGN with other classifiers, such as OneR, JRip and J48, show the competitiveness of PGN in recognizing multi-class datasets on collections of masterpieces from different West and East European Fine Art authors and movements.