Data Labeling System for Knowledge Graph Quality Evaluation
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Solution Overview
Problem
Current methods for evaluating knowledge graph data quality are inefficient due to decentralized processes, high management costs, inconsistent manual evaluation standards, and repetitive data processing, which hinder accurate and timely assessment as data complexity increases.
Innovation Solution
A data labeling method and apparatus that involves sampling a data source based on an evaluation task, generating a labeling task, sending it to a labeling device, and receiving labeled results, facilitating automatic evaluation and reducing data processing volume through random sampling and centralized management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual evaluation is used to assess data quality, then evaluation accuracy can be maintained, but evaluation efficiency is low and time-consuming is long
Solution Approach 1:
The system enables automatic self-evaluation of data quality through the evaluation task module that automatically samples data, generates labeling tasks, receives labeled results, and evaluates data quality without requiring manual intervention for each evaluation step
Solution Approach 2:
The patent replaces manual mechanical evaluation processes with an automated computer-based system that uses sampling algorithms, task generation mechanisms, and automated evaluation scripts to assess data quality
2Measurement precision
If all data is processed for evaluation, then evaluation accuracy is improved, but processing volume is large and efficiency is reduced
Solution Approach 1:
The system extracts only the necessary subset of data for evaluation through random sampling based on evaluation tasks, separating the evaluation process from processing all data, thus maintaining accuracy while reducing processing volume
Solution Approach 2:
Instead of evaluating all data (excessive action), the system performs partial evaluation by sampling only the required amount of data needed for accurate quality assessment, avoiding unnecessary processing
3Ease of operation
If decentralized evaluation processes are used, then flexibility is improved, but management costs are high and standards are inconsistent
Solution Approach 1:
The patent merges decentralized evaluation operations into a centralized management framework where the evaluation task module coordinates all evaluation activities, standardizes processes, and manages resources uniformly across the system
Data Source
AI summary
A data labeling method, apparatus and system are provided. The method includes: sampling a data source according to an evaluation task for the data source to obtain sampled data; generating a labeling task from the sampled data; sending the labeling task to a labeling device; and receiving a labeled result of the labeling task from the labeling device. As such, an automatic evaluation of data can be implemented by using the evaluation task, and evaluation efficiency is improved.