Knowledge Graph Completion Using LLMs for Missing AI Pipeline Metadata
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Solution Overview
Problem
Existing AI model training processes face inefficiencies due to incomplete data in AI pipeline metadata knowledge graphs, leading to resource-intensive processing and suboptimal performance.
Innovation Solution
A system that identifies and supplements missing data elements in AI pipeline metadata knowledge graphs by extracting information from manuscript files using a large language model (LLM), iteratively updating the knowledge graph with intent identification and extraction processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional AI model training processes are used with incomplete knowledge graph data, then the system can operate with existing data resources, but computational resource usage increases and processing efficiency decreases
Solution Approach 1:
The system performs preliminary extraction of missing data elements from manuscript files and populates the knowledge graph before AI model training begins. This advance preparation ensures that the knowledge graph is complete with all necessary nodes, edges, and properties, eliminating the need for resource-intensive processing during actual model training operations.
Solution Approach 2:
The patent introduces an intermediary process between data storage and AI model training that involves extracting missing elements from manuscript files, identifying their semantic meaning using intent identification, and integrating them into the knowledge graph. This intermediary layer ensures data completeness without requiring additional computational resources during the main training process.
2Reliability
If incomplete data elements are used in the knowledge graph, then the system structure remains simple, but the quality of data sources deteriorates and model performance decreases
Solution Approach 1:
The system extracts only the specific missing data elements needed to complete the knowledge graph from manuscript files, rather than processing entire documents. This targeted extraction approach improves data quality by ensuring all necessary nodes, edges, and properties are present, while maintaining system simplicity through focused, selective data retrieval.
Solution Approach 2:
The system automatically identifies missing data elements in the knowledge graph and retrieves them from manuscript files without requiring manual intervention. The intent identification process automatically determines the semantic meaning of extracted elements, and the system self-updates the knowledge graph, maintaining high data quality without increasing operational complexity.
3Loss of information
If manual completion of knowledge graph data is attempted, then data completeness may improve, but the time and labor resources required increase significantly
Solution Approach 1:
The patent replaces manual mechanical processes of data extraction and knowledge graph completion with an automated computational system. The system uses intent identification to automatically extract missing data elements from manuscript files and integrate them into the knowledge graph, achieving complete data coverage without manual time investment.
Solution Approach 2:
The system implements a feedback mechanism where it continuously monitors the knowledge graph for missing data elements, automatically retrieves them from manuscript files, and updates the graph. This closed-loop process ensures complete data coverage while minimizing time resources through automation and iterative improvement.
Data Source
AI summary
Systems and methods are provided for completing a knowledge graph associated with an artificial intelligence pipeline. For example, the system may determine a missing data element in a knowledge graph, initiate an extraction process from a manuscript file associated with identifying the missing data element in the knowledge graph, initiate an intent identification process of the manuscript file that generates a label for a cluster of terms of the manuscript file, provide the cluster of terms from the manuscript file and the label as input to a large language model (LLM), and iteratively update the knowledge graph with output from the LLM.