Knowledge Graph Data Labeling with Automated Correction
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Solution Overview
Problem
Conventional methods for entity identification in knowledge graph construction rely heavily on manual data labeling, which is inefficient and costly, requiring significant human effort.
Innovation Solution
A data labeling method that uses a knowledge graph to remotely acquire data, perform data cleaning and pre-labeling, and apply labeling correction by monitoring entity labeling actions to identify omitted and erroneously labeled entities, thereby reducing human intervention and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual labeling is used, then labeling quality can be controlled, but labeling efficiency is low and human cost is high
Solution Approach 1:
The system performs preliminary actions by automatically acquiring data from knowledge graphs, cleaning data, and generating initial labels before human review. This pre-processing reduces the amount of manual work needed while maintaining quality through subsequent correction mechanisms.
Solution Approach 2:
The system implements feedback loops where labeled data is reviewed and corrected, then the correction results feed back into the labeling system to improve future automatic labeling. This continuous improvement mechanism increases automation effectiveness over time.
2Reliability
If automatic labeling is used, then labeling efficiency increases, but labeling accuracy decreases due to errors and omissions
Solution Approach 1:
The system monitors labeling actions and uses feedback to identify and correct errors and omissions in automatic labeling, thereby improving accuracy while maintaining high productivity through automated correction processes.
Solution Approach 2:
The system performs self-correction by automatically detecting labeling errors and omissions through monitoring mechanisms, then self-correcting without requiring manual intervention for each correction, thus maintaining both accuracy and efficiency.
3Manufacturing precision
If human labeling is used, then labeling quality is high, but time consumption and human cost are excessive
Solution Approach 1:
The system performs preliminary data acquisition, cleaning, and initial labeling actions automatically, reducing the time humans would need to spend on these routine tasks while maintaining quality through subsequent review and correction processes.
Solution Approach 2:
The system performs self-service by automatically executing labeling tasks that would otherwise require human intervention, including data acquisition, cleaning, initial labeling, and correction of errors and omissions, thereby eliminating time consumption while maintaining quality through automated monitoring and correction mechanisms.
Data Source
AI summary
A data labeling method and device and a computer-readable storage medium. The method includes: based on a knowledge graph, remotely acquiring data to be labeled; performing data cleaning and pre-labeling to the data to be labeled, to obtain pre-labeled data; and performing labeling correction to the pre-labeled data.