Domain-Agnostic Knowledge Extraction via Semantic Linking
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Solution Overview
Problem
Current data management solutions require extensive training on specialized domains, are expensive, and rely on human experts, making them domain-specific and inefficient for extracting knowledge from multiple heterogeneous data sources, and they often replicate irrelevant or outdated data.
Innovation Solution
A system for real-time, domain-agnostic data extraction, integration, and management using semantic linking, distantly supervised machine learning, and entity resolution, which leverages public semantic knowledge bases to automate the construction of knowledge graphs and reduce the need for expert-annotated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized machine learning models are trained for knowledge extraction in specific domains, then extraction accuracy in that domain is improved, but the model becomes irrelevant for other domains and applications
Solution Approach 1:
The patent applies a universal knowledge extraction model that can operate across multiple domains without requiring domain-specific training. The model uses a generic transformer architecture with pre-trained language models that can be adapted to any domain through prompt engineering and few-shot learning, eliminating the need for separate specialized models for each domain while maintaining high extraction accuracy.
Solution Approach 2:
The system dynamically adjusts extraction parameters and model configurations based on the target domain and task requirements. By changing operational parameters rather than retraining the entire model, the system adapts to different domains efficiently, maintaining both accuracy and versatility across applications.
2Measurement precision
If human experts manually annotate training data for specialized domains, then extraction precision is improved, but the cost and time required increase significantly
Solution Approach 1:
The system performs self-annotation by automatically generating training data and labels using pre-trained language models and knowledge graphs. Instead of requiring human experts to manually annotate data, the system uses its own capabilities to create labeled datasets, significantly reducing the time and cost of data preparation while maintaining high extraction precision through automated quality control.
Solution Approach 2:
The system pre-processes and pre-annotates data using automated methods before actual extraction tasks. By performing preliminary actions of data cleaning, structuring, and initial labeling automatically, the system eliminates the need for time-consuming manual annotation while preserving the quality needed for precise knowledge extraction.
3Ease of operation
If data is replicated into a uniform repository for analysis, then data accessibility is improved, but irrelevant or outdated data may be included
Solution Approach 1:
The system maintains data quality and relevance at the source level rather than relying on replicated copies. By accessing data directly from its original heterogeneous sources and applying domain-specific transformation layers, the system ensures that only relevant and up-to-date data is used for analysis, eliminating the problem of irrelevant or outdated replicated data while maintaining accessibility through standardized interfaces.
4Measurement precision
If domain-specific applications are built using specialized models, then extraction accuracy in that domain is improved, but the effort required for building and maintaining increases
Solution Approach 1:
The system segments the knowledge extraction process into independent, reusable components: a universal transformer model, domain adapters, prompt templates, and knowledge graph modules. This segmentation allows each component to be developed and optimized independently, reducing overall system building complexity while maintaining high domain-specific accuracy through modular composition.
Solution Approach 2:
The system introduces intermediate layers such as domain adapters and prompt engineering frameworks that bridge the universal model and domain-specific requirements. These intermediaries simplify the connection between general models and specific applications, reducing the complexity of building and maintaining domain-specific systems while preserving extraction accuracy.
Data Source
AI summary
A system and method of extracting knowledge from a plurality of documents by at least one processor may include: receiving a domain-specific schema data structure, comprising a definition of one or more domain entity types; using at least one first machine-learning (ML) based model to fetch one or more mentions from the plurality of documents; using at least one second ML model to extract, from the one or more mentions, at least one domain entity that corresponds to the one or more domain entity types; and integrating the at least one extracted domain entity into a knowledge graph, based on the domain schema.