Guiding Goal Modeling Using Scenarios
IEEE Transactions on Software Engineering
Larks: Dynamic Matchmaking Among Heterogeneous Software Agents in Cyberspace
Autonomous Agents and Multi-Agent Systems
IEEE Intelligent Systems
Semantic Matching of Web Services Capabilities
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Importing the Semantic Web in UDDI
CAiSE '02/ WES '02 Revised Papers from the International Workshop on Web Services, E-Business, and the Semantic Web
A model for web services discovery with QoS
ACM SIGecom Exchanges
Composing Web services on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
Ontology mapping: the state of the art
The Knowledge Engineering Review
QoS-Aware Middleware for Web Services Composition
IEEE Transactions on Software Engineering
Meteor-s web service annotation framework
Proceedings of the 13th international conference on World Wide Web
Constraint Driven Web Service Composition in METEOR-S
SCC '04 Proceedings of the 2004 IEEE International Conference on Services Computing
On automating Web services discovery
The VLDB Journal — The International Journal on Very Large Data Bases
Service Component: A Mechanism For Web Service Composition Reuse And Specialization
Journal of Integrated Design & Process Science
A high-level functional matching for semantic web services
ICSOC'05 Proceedings of the Third international conference on Service-Oriented Computing
Conceptual modeling approaches for dynamic web service composition
The evolution of conceptual modeling
On-demand conversation customization for services in large smart environments
IBM Journal of Research and Development
Static and dynamic adaptations for service-based systems
Information and Software Technology
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Service matching approaches trade precision for recall, creating the need for users to choose the correct services, which obviously is a major obstacle for automating the service discovery and aggregation processes. Our approach to overcome this problem, is to eliminate the appearance of false positives by returning only the correct services. As different users have different semantics for what is correct, we argue that the correctness of the matching results must be determined according to the achievement of users' goals: that only services achieving users' goals are considered correct. To determine such correctness, we argue that the matching process should be based primarily on the high-level functional specifications (namely goals, achievement contexts, and external behaviors). In this article, we propose models, data structures, algorithms, and theorems required to correctly match such specifications. We propose a model called G+, to capture such specifications, for both services and users, in a machine-understandable format. We propose a data structure, called a Concepts Substitutability Graph (CSG), to capture the substitution semantics of application domain concepts in a context-based manner, in order to determine the semantic-preserving mapping transformations required to match different G+ models. We also propose a behavior matching approach that is able to match states in an m-to-n manner, such that behavior models with different numbers of state transitions can be matched. Finally, we show how services are matched and aggregated according to their G+ models. Results of supporting experiments demonstrate the advantages of the proposed service matching approaches.