In this paper, we propose Knowledge Graph (KG), an articulated underlying semantic structure, as a semantic bridge between humans, systems, and scientific knowledge. To illustrate our proposal, we focus on KG-based intelligent survey systems. In state-of-the-art systems, information is hard-coded or implicit, making it hard for researchers to reuse, customise, link, or transmit structured knowledge. Furthermore, such systems do not facilitate dynamic interaction based on semantic structure. We design and implement a knowledge-driven intelligent survey system which is based on knowledge graph, a widely used technology that facilitates sharing and querying hypotheses, survey content, results, and analyses. The approach is developed, implemented, and tested in the field of Linguistics. Syntacticians and morphologists develop theories of grammar of natural languages. To evaluate theories, they seek intuitive grammaticality (well-formedness) judgments from native speakers, which either support hypotheses or provide counter-evidence. Our preliminary experiments show that a knowledge graph-based linguistic survey can provide more nuanced results than the traditional document-based grammaticality judgment surveys by allowing for tagging and manipulation of specific linguistic variables.

中文翻译：

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面向开放科学的知识驱动智能测量系统

在本文中，我们提出了知识图谱 (KG)，一种清晰的底层语义结构，作为人类、系统和科学知识之间的语义桥梁。为了说明我们的建议，我们专注于基于 KG 的智能调查系统。在最先进的系统中，信息是硬编码的或隐式的，使研究人员难以重复使用、定制、链接或传输结构化知识。此外，这样的系统不利于基于语义结构的动态交互。我们设计并实现了一个基于知识图谱的知识驱动智能调查系统，知识图谱是一种广泛使用的技术，有助于共享和查询假设、调查内容、结果和分析。该方法在语言学领域得到开发、实施和测试。句法学家和形态学家发展了自然语言的语法理论。为了评估理论，他们从母语人士那里寻求直观的语法（格式良好）判断，这些判断要么支持假设，要么提供反证。我们的初步实验表明，通过允许标记和操纵特定语言变量，基于知识图的语言调查可以提供比传统的基于文档的语法判断调查更细微的结果。