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.
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