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Investigating a Method for Automatic Construction and Population of Ontologies for Services: Performances and Limitations

机译:研究一种自动构建和填充服务本体的方法:性能和局限性

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Ontological engineering is a complex process, involving multidisciplinary skills. The Semantic Web, and more specifically Semantic Web Services spreading suffer from the difficulty of producing an ontology sufficiently detailed to be able to correctly describe the data flows exchanged between services. These data are often described using sector-specific vocabulary. Linking these descriptions to external knowledge sources capable of unifying them is often a complex process, requiring adequate sources to be found and properly used. In this paper, we investigate a method combining existing string distance measurement, NLP-analysis and clustering algorithms for automatic construction and population of an ontology. This method takes services capacities descriptions as only input, without external sources of knowledge. It is tested on a set of more than 10,000 services for 106,000 different measures to classify in an ontology, performances and limitations are exposed.
机译:本体工程是一个复杂的过程,涉及多学科技能。语义Web,尤其是语义Web Services的扩展存在难以生成足够详细的本体以难以正确描述服务之间交换的数据流的难题。这些数据通常使用特定领域的词汇进行描述。将这些描述链接到能够统一它们的外部知识资源通常是一个复杂的过程,需要找到足够的资源并正确使用。在本文中,我们研究了一种将现有字符串距离测量,NLP分析和聚类算法相结合的方法,用于自动构建和填充本体。此方法仅将服务能力描述作为输入,而无需外部知识来源。它在10,000多种服务的集合上进行了测试,用于106,000种不同的度量,以对本体进行分类,并公开了性能和局限性。

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