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SUMMARYThe clustering and sorting behavior of ants, as well as the foraging behavior of birds in nature represented sources of inspiration for designing clustering methods applicable in computer science. This paper investigates how biologically-inspired clustering methods can be adapted to cluster Semantic Web services aiming at the efficiency of the discovery process. The methods consider the semantic similarity between services as the main clustering criterion. To measure the semantic similarity between two services, we propose a matching method that evaluates the degree of match between the semantic description of the two services. We have tested the biologically-inspired clustering methods on the SAWSDL service retrieval test collection (SAWSDL-TC) benchmark, and we have comparatively evaluated their performance using the Dunn index and the Average-Item Cluster Similarity metric, the latter being introduced in this paper.
This paper presents a technique for semantic Web service composition inspired by the behavior of ants. The proposed technique combines a service composition graph model with the ant colony optimization metaheuristic to select the optimal composition solution. In our approach, we have considered as selection criteria the QoS attributes of the services and the semantic quality of the connections between the services involved in a composition solution.
The Visual Model Query Language (VMQL) has been invented with the objectives (1) to make it easier for modelers to query models effectively, and (2) to be universally applicable to all modeling languages. In previous work, we have applied VMQL to UML, and validated the first of these two claims. In this paper, we apply VMQL to the Business Process Modeling Notation (BPMN) to evaluate the second claim. We explore the adaptations required, and re-evaluate the usability of VMQL in this context. We find similar results to earlier work, thus both supporting our claims and establishing the usability of VMQL beyond the realm of UML.
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