US 12,339,919 B2
Method of processing triple data, method of training triple data processing model, device, and medium
Zhaoji Wang, Beijing (CN); Fang Huang, Beijing (CN); Ye Jiang, Beijing (CN); Yabing Shi, Beijing (CN); Chunguang Chai, Beijing (CN); and Yong Zhu, Beijing (CN)
Assigned to Beijing Baidu Netcom Science Technology Co., Ltd., Beijing (CN)
Filed by Beijing Baidu Netcom Science Technology Co., Ltd., Beijing (CN)
Filed on Sep. 23, 2022, as Appl. No. 17/934,876.
Claims priority of application No. 202111118746.9 (CN), filed on Sep. 23, 2021.
Prior Publication US 2023/0016403 A1, Jan. 19, 2023
Int. Cl. G06F 16/00 (2019.01); G06F 16/9537 (2019.01); G06F 40/226 (2020.01); G06F 40/30 (2020.01)
CPC G06F 16/9537 (2019.01) [G06F 40/226 (2020.01); G06F 40/30 (2020.01)] 13 Claims
OG exemplary drawing
 
1. A method of processing triple data, comprising:
performing a triple data extraction on text data to obtain a plurality of field data;
normalizing the plurality of field data to determine target triple data, wherein the target triple data contains entity data, entity relationship data, and association entity data; and
verifying a confidence level of the target triple data to obtain a verification result,
wherein the verifying the confidence level of the target triple data to obtain the verification result comprises:
searching for web data related to the target triple data by using the target triple data as a search term;
generating target feature data according to the web data; and
inputting the target feature data into a triple data processing model, so as to obtain the verification result,
wherein the triple data processing model is obtained by training, and the training comprises:
performing a triple data extraction on text training data to obtain a plurality of training field data;
normalizing the plurality of training field data to determine target training triple data, wherein the target training triple data contains training entity data, training entity relationship data, and training association entity data; and
training the triple data processing model by using the target training triple data and a label for the target training triple data, so as to obtain a trained triple data processing model, wherein the label indicates a confidence level of the target training triple data,
wherein the training the triple data processing model by using the target training triple data and a label for the target training triple data, so as to obtain a trained triple data processing model, comprises:
searching for sample web data related to the target training triple data by using the target training triple data as a search term; and
generating target training feature data according to the sample web data, so as to train the triple data processing model by using the target training feature data and the label to obtain the trained triple data processing model.