Knowledge Graph Entity Disambiguation for Text Analysis
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Solution Overview
Problem
Existing information processing systems lack the granularity and ability to discover complex relationships between entities in vast amounts of text-based data, making it challenging to organize, analyze, and synthesize information effectively.
Innovation Solution
A system that creates a knowledge graph by identifying and linking entities and their relationships within a corpus of text documents, using deep neural networks and pre-trained language models like BERT for entity recognition, co-reference resolution, and relationship classification, allowing users to interact and correct relationships for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing search engines and information processing systems are used, then basic keyword search capabilities are provided, but the level of granularity and ability to discover complex relationships between entities is insufficient
Solution Approach 1:
The system segments the information processing task into distinct components: entity recognition, relationship extraction, and visualization. By breaking down the complex analysis into manageable segments, the system achieves high granularity in entity relationship discovery while keeping each component's complexity可控
Solution Approach 2:
The patent introduces an intermediary knowledge graph structure that mediates between raw text data and final analysis results. This intermediary layer enables complex relationship discovery without requiring the entire system to handle all complexity simultaneously, thus improving granularity while managing system complexity
2Loss of information
If manual analysis of vast amounts of text-based data is performed, then detailed entity relationships can be discovered, but the time and resources required become prohibitively large
Solution Approach 1:
The system implements self-service through automated entity recognition and relationship extraction algorithms that process text data without human intervention. The knowledge graph automatically builds and updates itself by analyzing document corpora, ensuring complete entity relationship extraction while eliminating manual analysis time
Solution Approach 2:
The system performs preliminary action by pre-processing text data to identify entities and their relationships before full analysis. This preliminary extraction of structured information from unstructured text enables rapid subsequent queries and analysis, reducing both information loss and time requirements
3Productivity
If automated entity recognition and relationship extraction are implemented, then processing speed and scalability are improved, but accuracy and ability to disambiguate entities may be reduced
Solution Approach 1:
The system incorporates feedback mechanisms where extracted relationships are validated and refined through multiple processing passes. The knowledge graph structure allows for iterative improvement of entity disambiguation accuracy while maintaining high processing speed through efficient data structures and algorithms
Solution Approach 2:
The system applies dynamics by making the entity recognition and relationship extraction process adaptive rather than static. The knowledge graph dynamically adjusts to new information and corrects disambiguation errors as more data becomes available, thereby maintaining both high productivity and improving precision over time
Data Source
AI summary
A data-processing system identifies entities and relationships between entities that are recited in a set of documents. By identifying differently-named entities that share similar sets of relationships, the system is able to identify differently-named, but identical entities recited in the set of documents. The resulting entity and relationship map may be displayed to an end-user for intelligence analysis. In some examples, the end-user may make corrections to the relationship map which can then be used by the system to improve the inferences produced.