面向知识服务的档案数据语义化重组

摘要

档案知识服务的智能化能力与档案数据的语义化程度紧密相连。本文基于档案知识组织现状和语义网与关联数据的思想,分析了面向知识服务进行语义化重组的必要性,提出档案语义化重组需要从数据形式、资源描述、关系表达和聚集效率四个方面满足机器的可读、可理解、可推理和自动化要求,进而构建由数据提供层、语义描述层和知识聚合层三个核心层次构成的语义化重组模型。档案机构在语义化重组的实施过程中,可以从需求分析、知识建模、知识加工和知识发布四个环节展开,并通过测试与迭代,改进数据重组质量。

Abstract

：The intelligence ability of archives knowledge service is closely related to the semantic degree of archives data. Based on the development of archives knowledge organization, semantic Web and linked open data, we analyze the necessity of semantic restructuring, and propose four key aspects including data formalization, resource description, relationship expression and aggregate efficiency, to meet machine's readable, understandable, inferable and automation requirements, and then construct the model with three layers of data provider layer, semantic description layer and knowledge aggregation layer. We suggest that semantic restructuring can be carried out through requirement analysis, knowledge modeling, processing and publishing, and improve the data quality by testing and iteration.

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