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Automated knowledge source selection and service composition

机译:自动化的知识来源选择和服务组合

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We introduce a new combinatorial problem referred to as the component set identification problem, arising in the context of knowledge discovery, information integration, and knowledge source/service composition. The main motivation for studying this problem is the widespread proliferation of digital knowledge sources and services. Considering a granular knowledge domain consisting of a large number of individual bits and pieces of domain knowledge (properties) and a large number of knowledge sources and services that provide mappings between sets of properties, the objective of the component set identification problem is to select a minimum cost combination of knowledge sources that can provide a joint mapping from a given set of initially available properties (initial knowledge) to a set of initially unknown properties (target knowledge). We position the component set identification problem relative to other combinatorial problems and provide a classification scheme for the different variations of the problem. The problem is next modeled on a directed graph and analyzed in terms of its complexity. The directed graph representation is then augmented and transformed into a time-expanded network representation that is subsequently used to develop an exact solution procedure based on integer programming and branch-and-bound. We enhance the solver by developing preprocessing techniques. All findings are supported by computational experiments.
机译:我们引入了一个新的组合问题,称为组件集标识问题,它是在知识发现,信息集成和知识源/服务组合的背景下产生的。研究此问题的主要动机是数字知识源和服务的广泛扩散。考虑到一个粒度知识领域,它由大量单个领域的知识点(属性)和大量知识源和提供属性集之间映射的服务组成,因此组件集识别问题的目的是选择一个可以提供从给定的一组初始可用属性(初始知识)到一组初始的未知属性(目标知识)的联合映射的知识源的最低成本组合。我们将组件集标识问题相对于其他组合问题定位,并为问题的不同变化提供分类方案。接下来,将问题建模在有向图上,并根据其复杂性进行分析。然后,将有向图表示形式进行扩充并转换为时间扩展的网络表示形式,随后将其用于基于整数编程和分支定界方法开发精确的求解过程。我们通过开发预处理技术来增强求解器。所有发现均得到计算实验的支持。

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