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A Semantic Knowledge Management System for Government Repositories

机译:政府存储库的语义知识管理系统

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Nowadays, in a knowledge-driven economy, the organizations, public or private, require to make an appropriate managing knowledge assets to sustain competitive edge in global markets or in governmental services. Advances in Information and Communications Technologies have supported innovations in Knowledge Management (KM). Since the KM has been recognized as one of the critical factors for obtaining organizational competitiveness. KM is among the promising areas for the application of Semantic Web. In this paper we propose an approach to the development of a technological platform for KM that integrates semantic Web technologies. This platform supports all processes required in knowledge management. Therefore, the platform enables the automatic identification of patterns and generation of the taxonomies from unstructured texts (documents). The platform consists of tools that allow storing documents (from a knowledge contributor interface), processing of source documents, i.e. identifying sentences, stop-words, identifying n-grams, and generating bags of words, denoting lexical patterns, taxonomic-relation extraction, and inference algorithms to retrieve knowledge from a set of repositories. These algorithms use the previous phases to retrieve knowledge more accurately derived from the semi-structured or structured information previously generated.
机译:如今,在知识驱动型经济中,公共或私营组织都需要做出适当的管理知识资产,以维持在全球市场或政府服务中的竞争优势。信息和通信技术的进步为知识管理(KM)的创新提供了支持。由于知识管理已被认为是获得组织竞争力的关键因素之一。 KM是语义Web应用的有前途的领域之一。在本文中,我们提出了一种开发集成语义Web技术的KM技术平台的方法。该平台支持知识管理所需的所有过程。因此,该平台可以自动识别模式,并根据非结构化文本(文档)生成分类法。该平台由多种工具组成,这些工具可用于存储文档(来自知识提供者界面),处理源文档,即识别句子,停用词,识别n-gram并生成单词袋,表示词汇模式,分类相关提取,和推理算法以从一组存储库中检索知识。这些算法使用先前的阶段来更准确地检索从先前生成的半结构化或结构化信息中得出的知识。

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