Ontology-Based Matchmaking and Composition of Business Processes

Abstract

This chapter shows how it is possible to use agents and Semantic Web technologies to deal with dynamic composition of business processes via an agent-based workflow system. The aim of the system is to discover composable processes at first among heterogeneous business processes that are running possibly under different Web servers and then execute them in the order specified by a planner to reach a complex requested goal. We proposed a framework of an Inference-based Semantic Composition Agent (SCA) of atomic business processes that employs process similarity matching and inference techniques. SCA synthesizes new services from existing ones in an automatic fashion. A powerful matching mechanism is needed to find fitting tasks in order to attain the required composition. An innovative Semantic Matching Step (SMS) of SCA helps to find the fitting tasks while constituting workflow to achieve required composition. Additionally, SCA composes available OWL-S atomic processes utilizing Revised Armstrong's Axioms (RAAs) in inferring functional dependencies. Experiments show that SCA System produces atomic process sequences as a workflow in achieving the required composition plan that satisfies user's requirements as a complex task. The novelty of the SCA System is that for the first time Armstrong's Axioms are revised and used for semantic-based planning and inferencing of services.