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An Expressive and Efficient Solution to the Service Selection Problem

机译:一种高效,高效的服务选择问题解决方案

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Given the large number of Semantic Web Services that can be created from online sources by using existing annotation tools, expressive formalisms and efficient and scalable approaches to solve the service selection problem are required to make these services widely available to the users. In this paper, we propose a framework that is grounded on logic and the Local-As-View approach for representing instances of the service selection problem. In our approach, Web services are semantically described using LAV mappings in terms of generic concepts from an ontology, user requests correspond to conjunctive queries on the generic concepts and, in addition, the user may specify a set of preferences that are used to rank the possible solutions to the given request. The LAV formulation allows us to cast the service selection problem as a query rewriting problem that must consider the relationships among the concepts in the ontology and the ranks induced by the preferences. Then, building on related work, we devise an encoding of the resulting query rewriting problem as a logical theory whose models are in correspondence with the solutions of the user request, and in presence of preferences, whose best models are in correspondence with the best-ranked solutions. Thus, by exploiting known properties of modern SAT solvers, we provide an efficient and scalable solution to the service selection problem. The approach provides the basis to represent a large number of real-world situations and interesting user requests.
机译:鉴于可以使用现有的批注工具从在线资源中创建大量的语义Web服务,因此需要表达性的形式主义以及有效且可扩展的方法来解决服务选择问题,以使这些服务对用户广泛可用。在本文中,我们提出了一个基于逻辑和Local-As-View方法的框架,用于表示服务选择问题的实例。在我们的方法中,Web服务是使用LAV映射根据来自本体的一般概念在语义上进行描述的,用户请求对应于对一般概念的联合查询,此外,用户可以指定一组优先级,这些优先级用于对优先级进行排序。给定请求的可能解决方案。 LAV公式使我们可以将服务选择问题转换为查询重写问题,该问题必须考虑本体中的概念与首选项所引起的等级之间的关系。然后,在相关工作的基础上,我们将结果查询重写问题的编码设计为一种逻辑理论,其模型与用户请求的解决方案相对应,并且在存在偏好的情况下,其最佳模型与最佳解决方案相对应。排名解决方案。因此,通过利用现代SAT求解器的已知属性,我们为服务选择问题提供了有效且可扩展的解决方案。该方法提供了表示大量现实情况和有趣的用户请求的基础。

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