轻量级关联数据

Erik Wilde, Yiming Liu
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引用次数: 5

摘要

Web的成功在很大程度上取决于它在实现跨各种边界的信息重用和集成方面的作用。超链接网络资源代表了内容和上下文的丰富信息织锦,有助于有效的知识共享和进一步的知识开发。然而,对于有效的内容发现和重用来说,Web的简单链接模型已经变得越来越不够了。与此同时,严谨但重量级的解决方案,如语义网,还没有获得大量采用。本文分析了现有关联数据方法的优缺点。它提出了一种新颖的轻量级体系结构,用于Web资源的上下文信息的建模、聚合、检索、管理和共享,该体系结构基于已建立的标准,旨在鼓励在Web上更有效和更健壮的信息重用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lightweight linked data
Much of the Web’s success rests with its role in enabling information reuse and integration across various boundaries. Hyperlinked Web resources represent a rich information tapestry of content and context, instrumental in effective knowledge sharing and further knowledge development. However, the Web’s simple linking model has become increasingly inadequate for effective content discovery and reuse. At the same time, rigorous but heavyweight solutions such as the Semantic Web have yet to garner critical mass in adoption. This paper analyzes the relative strengths and shortcomings of existing linked data approaches. It proposes a novel, lightweight architecture for the modeling, aggregation, retrieval, management, and sharing of contextual information for Web resources, based on established standards and designed to encourage more efficient and robust information reuse on the Web.
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