Method, electronic device and computer-readable storage medium for extracting entity relationships

Patent metadata

Field	Value
Publication number	US-2025217594-A1
Application number	US-202519080056-A
Country	US
Kind code	A1
Filing date	Mar 14, 2025
Priority date	Dec 3, 2024
Publication date	Jul 3, 2025
Grant date	—

Abstract

A method, electronic device and computer-readable storage medium for extracting entity relationships, which relates to artificial intelligence technologies such as natural language processing, knowledge graphs, deep learning, and large language models. The method for extracting entity relationships includes: inputting a target long text into a target large language model to obtain a target keyword list based on an output result of the target large language model; inputting the target keyword list into multiple target relationship agents respectively to obtain multiple target regular expressions corresponding to different entity relationships based on output results of the multiple target relationship agents; and processing texts in a preset text set using the multiple target regular expressions to obtain entity relationship extraction results.

First claim

What is claimed is:

1 . A method for extracting entity relationships, comprising:
inputting a target long text into a target large language model to obtain a target keyword list based on an output result of the target large language model; inputting the target keyword list into multiple target relationship agents respectively to obtain multiple target regular expressions corresponding to different entity relationships based on output results of the multiple target relationship agents; and processing texts in a preset text set using the multiple target regular expressions to obtain entity relationship extraction results.

2 . The method according to claim 1 , wherein obtaining the multiple target regular expressions corresponding to different entity relationships based on the output results of the multiple target relationship agents comprises:
obtaining multiple candidate regular expressions corresponding to different entity relationships based on the output results of the multiple target relationship agents; and obtaining the multiple target regular expressions corresponding to different entity relationships based on the multiple candidate regular expressions corresponding to different entity relationships.

3 . The method according to claim 2 , wherein obtaining the multiple target regular expressions corresponding to different entity relationships based on the multiple candidate regular expressions corresponding to different entity relationships comprises:
for each target relationship agent, inputting a candidate regular expression corresponding to a current target relationship agent into the other target relationship agents respectively for the other target relationship agents to evaluate the candidate regular expression; and in response to determining that evaluation results output by the other target relationship agents are all passed, using the candidate regular expression as a target regular expression.

4 . The method according to claim 1 , wherein processing the text in the preset text set using the multiple target regular expressions to obtain the entity relationship extraction results comprises:
matching the multiple target regular expressions with the texts in the preset text set to obtain multiple matching texts; constructing a training dataset based on the multiple matching texts, entities in each matching text and entity relationships among the entities; training an initial entity relationship extraction model using the training dataset to obtain a target entity relationship extraction model; and inputting each text in the preset text set into the target entity relationship extraction model to obtain the entity relationship extraction result based on output results of the target entity relationship extraction model.

5 . The method according to claim 1 , wherein obtaining the target large language model comprises:
obtaining an initial large language model; repeatedly inputting an initial long text into the initial large language model to obtain multiple initial keyword lists output by the initial large language model; inputting the multiple initial keyword lists into a preset relationship agent respectively to obtain multiple initial regular expressions output by the preset relationship agent; selecting an optimal keyword list and a worst keyword list from the multiple initial keyword lists based on the multiple initial keyword lists and the multiple initial regular expressions; and training the initial large language model based on a positive and negative preference sample pair constituted by the optimal keyword list and the worst keyword list to obtain the target large language model.

6 . The method according to claim 5 , wherein selecting the optimal keyword list and the worst keyword list from the multiple initial keyword lists based on the multiple initial keyword lists and the multiple initial regular expressions comprises:
using a keyword ranking agent to rank the multiple initial keyword lists to obtain a first ranking result of the multiple initial keyword lists; using a regular expression ranking agent to rank the multiple initial regular expressions to obtain a second ranking result of the multiple initial keyword lists based on ranking results of the multiple initial regular expressions; and selecting the optimal keyword list and the worst keyword list from the multiple initial keyword lists based on the first ranking result and the second ranking result of the initial keyword lists.

7 . The method according to claim 1 , wherein inputting the target long text into the target large language model comprises:
obtaining a first prompt text; and inputting the first prompt text and the target long text into the target large language model.

8 . The method according to claim 1 , wherein inputting the target keyword list into the multiple target relationship agents respectively comprises:
obtaining a second prompt text; and inputting the second prompt text and the target keyword list into the multiple target relationship agents respectively.

9 . The method according to claim 3 , wherein inputting the candidate regular expression corresponding to the current target relationship agent into the other target relationship agents respectively comprises:
obtaining a third prompt text; and inputting the third prompt text and the candidate regular expression corresponding to the current target relationship agent into the other target relationship agents respectively.

10 . An electronic device, comprising:
at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform a method for extracting entity relationships, comprising: inputting a target long text into a target large language model to obtain a target keyword list based on an output result of the target large language model; inputting the target keyword list into multiple target relationship agents respectively to obtain multiple target regular expressions corresponding to different entity relationships based on output results of the multiple target relationship agents; and processing texts in a preset text set using the multiple target regular expressions to obtain entity relationship extraction results.

11 . The electronic device according to claim 10 , wherein obtaining the multiple target regular expressions corresponding to different entity relationships based on the output results of the multiple target relationship agents comprises:
obtaining multiple candidate regular expressions corresponding to different entity relationships based on the output results of the multiple target relationship agents; and obtaining the multiple target regular expressions corresponding to different entity relationships based on the multiple candidate regular expressions corresponding to different entity relationships.

12 . The electronic device according to claim 11 , wherein obtaining the multiple target regular expressions corresponding to different entity relationships based on the multiple candidate regular expressions corresponding to different entity relationships comprises:
for each target relationship agent, inputting a candidate regular expression corresponding to a current target relationship agent into the other target relationship agents respectively for the other target relationship agents to evaluate the candidate regular expression; and in response to determining that evaluation results output by the other target relationship agents are all passed, using the candidate regular expression as a

Assignees

• Beijing Baidu Netcom Sci & Tech Co Ltd

Inventors

• Zhou Jingbo
• Li Shuangli
• Xiong Hui

Classifications

• G06F40/30

  Semantic analysis · CPC title

• G06F40/295 Primary

  Named entity recognition · CPC title

• G06F16/3334 Primary

  Selection or weighting of terms from queries, including natural language queries · CPC title

• G06F16/313

  Selection or weighting of terms for indexing · CPC title

• G06N5/025 Primary

  Extracting rules from data · CPC title