Health knowledge graph provides an ideal technical means to integrate heterogeneous data resources and enhance knowledge-based services. There are many challenges for the construction of health knowledge graph such as complex concepts and relationships, various medical standards, heterogeneous data structures, poor data quality, highly accurate and interpretable services, etc.

In this paper, firstly, we propose Health Knowledge Graph Builder (HKGB), an end-to-end platform which could be used to construct disease-specific and extensible health knowledge graphs from multiple sources. Secondly, we analyze the capabilities and requirements of clinicians, design the tasks to involve the clinicians and implement a clinician-in-the-loop toolset to integrate the clinicians prior knowledge into the construction of health knowledge graphs. Thirdly, we design an extensible mechanism to add new diseases to an existing knowledge graph. Fourthly, we present a quantitative effort estimation algorithm to quantitatively evaluate the effort of clinicians during the construction, and use it to calculate the workloads such as 44.27 person days for knee osteoarthritis domain. Finally, we have developed several knowledge graph based tools to facilitate real applications.

健康知识图为整合异构数据资源和增强基于知识的服务提供了理想的技术手段。健康知识图的构建面临许多挑战，例如复杂的概念和关系，各种医学标准，异构数据结构，数据质量差，准确性高且可解释的服务等。

在本文中，首先，我们提出了健康知识图生成器（HKGB），这是一个端到端平台，可用于从多个来源构造特定疾病和可扩展的健康知识图。其次，我们分析了临床医生的能力和需求，设计了让临床医生参与的任务，并实施了“临床医生在环”工具集，以将临床医生的先验知识整合到健康知识图中。第三，我们设计了一种可扩展的机制来将新疾病添加到现有的知识图中。第四，我们提出了一种定量工作量估计算法，以定量评估临床医生在施工过程中的工作量，并使用它来计算诸如膝骨关节炎领域的工作量（例如44.27人日）。最后，