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A Top-down Method of Extraction Entity Relationship Triples and Obtaining Annotated Data

Zhiqiang Hu1, Zheng Ma1, Jun Shi1, Zhipeng Li1, Xun Shao1,2, Yangzhao Yang1,*, Yong Liao1, Zhenyuan Gao1, Jie Zhang1

1 Shenzhen CyberAray Network Technology Co., Ltd., Shenzhen, 518042, China
2 School of Regional Innovation and Social Design Engineering, Kitami Institute of Technology, Kitami, 090-8507, Japan

* Corresponding Author: Yangzhao Yang. Email:

Journal of Quantum Computing 2022, 4(1), 13-22.


The extraction of entity relationship triples is very important to build a knowledge graph (KG), meanwhile, various entity relationship extraction algorithms are mostly based on data-driven, especially for the current popular deep learning algorithms. Therefore, obtaining a large number of accurate triples is the key to build a good KG as well as train a good entity relationship extraction algorithm. Because of business requirements, this KG’s application field is determined and the experts’ opinions also must be satisfied. Considering these factors we adopt the top-down method which refers to determining the data schema firstly, then filling the specific data according to the schema. The design of data schema is the top-level design of KG, and determining the data schema according to the characteristics of KG is equivalent to determining the scope of data’s collection and the mode of data’s organization. This method is generally suitable for the construction of domain KG. This article proposes a fast and efficient method to extract the top-down type KG’s triples in social media with the help of structured data in the information box on the right side of the related encyclopedia webpage. At the same time, based on the obtained triples, a data labeling method is proposed to obtain sufficiently high-quality training data, using in various Natural Language Processing (NLP) information extraction algorithms’ training.


Cite This Article

Z. Hu, Z. Ma, J. Shi, Z. Li, X. Shao et al., "A top-down method of extraction entity relationship triples and obtaining annotated data," Journal of Quantum Computing, vol. 4, no.1, pp. 13–22, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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