Collaboration platforms provide a dynamic environment where the content is subject to ongoing evolution through expert contributions. The knowledge embedded in such platforms is not static as it evolves through incremental refinements – or micro-contributions. Such refinements provide vast resources of tacit knowledge and experience. In our previous work, we proposed and evaluated a Semantic and Time-dependent Expertise Profiling (STEP) approach for capturing expertise from micro-contributions. In this paper we extend our investigation to structured micro-contributions that emerge from an ontology engineering environment, such as the one built for developing the International Classification of Diseases (ICD) revision 11. We take advantage of the semantically related nature of these structured micro-contributions to showcase two major aspects: (i) a novel semantic similarity metric, in addition to an approach for creating bottom-up baseline expertise profiles using expertise centroids; and (ii) the application of STEP in this new environment combined with the use of the same semantic similarity measure to both compare STEP against baseline profiles, as well as to investigate the coverage of these baseline profiles by STEP.

中文翻译：

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在协作知识管理平台的上下文中使用语义相似性度量在不同粒度级别对专业知识进行建模

协作平台提供了一个动态环境，在该环境中，内容会通过专家的贡献不断演变。嵌入在此类平台中的知识不是静态的，因为它是通过增量改进或微贡献而发展的。这种改进提供了大量的隐性知识和经验资源。在我们之前的工作中，我们提出并评估了一种语义和时间相关专业知识分析 (STEP) 方法，用于从微观贡献中获取专业知识。在本文中，我们将研究扩展到从本体工程环境中出现的结构化微贡献，例如为开发国际疾病分类 (ICD) 修订版 11 而构建的那个。我们利用这些结构化微的语义相关性质- 展示两个主要方面的贡献：(i) 除了使用专业知识质心创建自下而上的基线专业知识档案的方法之外，还有一种新的语义相似性度量；(ii) STEP 在这个新环境中的应用，结合使用相同的语义相似性度量来比较 STEP 与基线配置文件，以及调查 STEP 对这些基线配置文件的覆盖范围。