TY  - EJOU
AU  - Hu, Zhiqiang 
AU  - Ma, Zheng 
AU  - Shi, Jun 
AU  - Li, Zhipeng 
AU  - Shao, Xun 
AU  - Yang, zhao 
AU  - Liao, Yong 
AU  - Gao, Zhenyuan 
AU  - Zhang, Jie 

TI  - A Top-down Method of Extraction Entity Relationship Triples and Obtaining Annotated Data
T2  - Journal of Quantum Computing

PY  - 2022
VL  - 4
IS  - 1
SN  - 2579-0145

AB  - 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.
KW  - Entity relationship triples; knowledge graph; top-down; social media; data labeling

DO  - 10.32604/jqc.2022.026785