GRAPHYP：一个具有多个社区子网络的科学知识图谱。对抗性信息路径中学术争议的检测
GRAPHYP: A Scientific Knowledge Graph with Manifold Subnetworks of Communities. Detection of Scholarly Disputes in Adversarial Information Routes
摘要：
已发表内容的认知多样性目前正在所有科学领域中扩展。然而，科学知识图谱（SKG）只提供了喂养知识生产的对立方向和科学争议的糟糕图片。在这篇文章中，我们解决的认知表示的研究活动的信息空间的设计的理解，以及相关的瓶颈，影响搜索接口，在结构化对象到图形的映射。我们提出，与SKG GRAPHYP，一种新的图形设计的几何架构，优化了“认知社区”的知识流形的检测，并表示替代路径的对抗性答案的研究问题，例如在学术争议的背景下。GRAPHYP提供了一种设计"认知社区流形子网络"的方法，它对研究领域中不同的搜索路径进行了分类。用户被检测到他们的搜索实践的多样性和分类的“认知社区”从他们的日志的科学文献的搜索历史的分析。实践的多样性是由三个节点组成的对称图形子网络的差异化使用度量表示的，具有以下三个参数：质量，强度和多样性。
英文摘要：
The cognitive manifold of published content is currently expanding in all areas of science. However, Scientific Knowledge Graphs (SKGs) only provide poor pictures of the adversarial directions and scientific controversies that feed the production of knowledge. In this Article, we tackle the understanding of the design of the information space of a cognitive representation of research activities, and of related bottlenecks that affect search interfaces, in the mapping of structured objects into graphs. We propose, with SKG GRAPHYP, a novel graph designed geometric architecture which optimizes both the detection of the knowledge manifold of "cognitive communities", and the representation of alternative paths to adversarial answers to a research question, for instance in the context of academic disputes. With a methodology for designing "Manifold Subnetworks of Cognitive Communities", GRAPHYP provides a classification of distinct search paths in a research field. Users are detected from the variety of their search practices and classified in "Cognitive communities" from the analysis of the search history of their logs of scientific documentation. The manifold of practices is expressed from metrics of differentiated uses by triplets of nodes shaped into symmetrical graph subnetworks, with the following three parameters: Mass, Intensity, and Variety.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
