论文标题
kgmm-基于交织的人机协作的学术知识图的成熟模型
KGMM -- A Maturity Model for Scholarly Knowledge Graphs based on Intertwined Human-Machine Collaboration
论文作者
论文摘要
在过去的几年中，知识图（KG）在科学，商业和社会中的重要性越来越大。但是，大多数知识图是从现有来源提取或编译的。只有相对较少的示例是通过交织在一起的人机合作真正创建的知识图。同样，由于数据和知识图的质量至关重要，因此已经提出了许多数据质量评估模型。但是，他们没有考虑到交织在一起的人机策划知识图的具体方面。在这项工作中，我们提出了一个学术知识图（kgmm）的分级成熟度模型，该模型特别关注与数字库知识图的关节，进化策划有关的方面。我们的模型包括5个质量措施的5个成熟度阶段。我们在大规模的学术知识图策划中演示了我们的模型的实施。
Knowledge Graphs (KG) have gained increasing importance in science, business and society in the last years. However, most knowledge graphs were either extracted or compiled from existing sources. There are only relatively few examples where knowledge graphs were genuinely created by an intertwined human-machine collaboration. Also, since the quality of data and knowledge graphs is of paramount importance, a number of data quality assessment models have been proposed. However, they do not take the specific aspects of intertwined human-machine curated knowledge graphs into account. In this work, we propose a graded maturity model for scholarly knowledge graphs (KGMM), which specifically focuses on aspects related to the joint, evolutionary curation of knowledge graphs for digital libraries. Our model comprises 5 maturity stages with 20 quality measures. We demonstrate the implementation of our model in a large scale scholarly knowledge graph curation effort.
