Knowledge Graph Construction Using Generative AI Across Domains
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Solution Overview
Problem
Existing knowledge graph construction techniques for medical imaging scanners are domain-specific and lack generalizability across different technical domains, requiring extensive retraining for each domain, which is time-consuming and effort-intensive.
Innovation Solution
Utilize generative text-to-text neural networks to construct knowledge graphs by iteratively executing a design discovery tree associated with the medical imaging scanner, enabling broader generalizability and reducing the need for extensive retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain-specific techniques are used to construct knowledge graphs, then the knowledge graph accurately represents domain-specific technical information, but the system lacks generalizability across different domains and requires extensive retraining
Solution Approach 1:
The patent applies universality by training a single neural network model on multi-domain technical documents (medical imaging, semiconductor manufacturing, aerospace) to construct knowledge graphs across different domains. The model learns domain-agnostic representations that can be applied universally, eliminating the need for separate domain-specific models while maintaining accuracy through its ability to handle diverse technical vocabularies and relationships.
2Loss of information
If domain-specific techniques are used to construct knowledge graphs, then the knowledge graph captures detailed domain knowledge, but extensive retraining is required for each domain which is time-consuming and effort-intensive
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network model on a large corpus of multi-domain technical documents before deployment. This preliminary training equips the model with general technical knowledge and domain-agnostic representation capabilities, so that when deployed to new domains, it can construct accurate knowledge graphs immediately without requiring extensive retraining, thus saving time and effort.
3Ease of operation
If traditional knowledge graph construction methods are used, then the system can handle structured data, but it struggles with unstructured technical documents and natural language queries
Solution Approach 1:
The patent replaces traditional mechanical parsing and structuring methods with a neural network-based system that can naturally process unstructured text. The neural network automatically extracts entities, relationships, and attributes from unstructured technical documents and converts them into structured knowledge graph representations, eliminating the need for manual structuring while maintaining ease of operation.
Data Source
AI summary
Systems or techniques that facilitate knowledge graph construction via generative artificial intelligence are provided. In various embodiments, a system can access a plurality of electronic documents associated with design or fabrication of a medical imaging scanner. In various aspects, the system can construct a knowledge graph representing the plurality of electronic documents, by iteratively executing a generative text-to-text neural network on a design discovery tree associated with the medical imaging scanner. In various instances, the system can access a natural language query regarding the medical imaging scanner and can convert, via execution of another neural network, the natural language query to a structured query. In various cases, the system can execute the structured query over the knowledge graph, thereby yielding an electronic answer to the natural language query.