摘要翻译：
我们从MKM的角度研究为软件工程项目创建的文档集合的形式化。我们分析了文档和集合标记格式如何处理一个开放的、多维的主要和次要分类和关系空间。我们表明，基于RDFA的MKM格式扩展，使用灵活的“元数据”关系，引用不同维度的特定词汇表，非常适合对此进行编码并将其投入服务。这种形式化的知识可以用于丰富交互式文档浏览，支持对文档和集合的多维元数据查询，以及将链接的数据导出到语义网，从而支持进一步的重用。
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英文标题：
《Dimensions of Formality: A Case Study for MKM in Software Engineering》
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作者：
Andrea Kohlhase and Michael Kohlhase and Christoph Lange
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Digital Libraries        数字图书馆
分类描述：Covers all aspects of the digital library design and document and text creation. Note that there will be some overlap with Information Retrieval (which is a separate subject area). Roughly includes material in ACM Subject Classes H.3.5, H.3.6, H.3.7, I.7.
涵盖了数字图书馆设计和文献及文本创作的各个方面。注意，与信息检索（这是一个单独的主题领域）会有一些重叠。大致包括ACM课程H.3.5、H.3.6、H.3.7、I.7中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Software Engineering        软件工程
分类描述：Covers design tools, software metrics, testing and debugging, programming environments, etc. Roughly includes material in all of ACM Subject Classes D.2, except that D.2.4 (program verification) should probably have Logics in Computer Science as the primary subject area.
涵盖设计工具、软件度量、测试和调试、编程环境等。大致包括ACM所有主题课程D.2的材料，除了D.2.4（程序验证）可能应该有计算机科学中的逻辑作为主要主题领域。
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英文摘要：
  We study the formalization of a collection of documents created for a Software Engineering project from an MKM perspective. We analyze how document and collection markup formats can cope with an open-ended, multi-dimensional space of primary and secondary classifications and relationships. We show that RDFa-based extensions of MKM formats, employing flexible "metadata" relationships referencing specific vocabularies for distinct dimensions, are well-suited to encode this and to put it into service. This formalized knowledge can be used for enriching interactive document browsing, for enabling multi-dimensional metadata queries over documents and collections, and for exporting Linked Data to the Semantic Web and thus enabling further reuse.
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PDF链接：
https://arxiv.org/pdf/1004.5071