Author(s)

Ying Wang¹, Jing Dong^2*, Peng Ren^2*, Ye Wang¹, Jingjing Cao¹

¹Center for Science & Technology Talents, MoST, Beijing, China.
²BNRist, DCST, RIIT, Tsinghua University, Beijing, China.

Construct a knowledge graph is time-consuming and the knowledge graph in the scientific domain requires extremely high labor costs due to it requires high prior knowledge to extract knowledge from resources. To build a scientific research knowledge graph, the most of input are papers, patent, the description of their project and some national program (such as National High Technology Research and Development Program of China, Major State Basic Research Development Program of China, General Program, Key Program and Major Program) which all of them are unstructured data, that make human participation are mostly necessary to measure the quality. In this paper, we design and proposed a framework using active learning; this framework can be used to extract entity and relation from unstructured science and technology research data. This framework combines the human and machine learning approach together, which is active learning, to help user extract entity from those unstructured data with less time cost. By using those data to construct a CKG as annotation label, it further implements active learning tools and helps the expert to rapidly annotate the data with high accuracy. Those knowledge graph constructed by this framework can be used to finding similar research area, finding similar researchers, finding popular research areas and so on.

Knowledge Graph, Human-in-the-Loop, Framework, Science and Technology

Wang, Y. , Dong, J. , Ren, P. , Wang, Y. and Cao, J. (2020) A Framework Using Active Learning to Rapidly Perform Named Entity Extraction and Relation Recognition for Science and Technology Knowledge Graph. Open Journal of Social Sciences, 8, 315-325. doi: 10.4236/jss.2020.89025.