Automated Schema Generation via NLP Entity Scoring
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Solution Overview
Problem
Current data modeling and schema creation are primarily manual processes, leading to inefficiencies and the potential for generating incorrect schemas if experts do not accurately understand the data or domain, resulting in inefficient or unusable systems.
Innovation Solution
A method using natural language processing to identify and score entity relationships within a corpus of data, comparing them to a second corpus to generate a schema that represents the identified entities and relationships, thereby automating the schema generation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual schema creation by experts is used, then schema accuracy depends on expert understanding, but the process is inefficient and time-consuming
Solution Approach 1:
The system automatically generates schemas by having the data speak for itself through NLP analysis. The schema generation process serves itself by extracting entities and relationships directly from the corpus without requiring external expert intervention, thus eliminating the time-consuming manual process while maintaining accuracy through automated semantic analysis.
Solution Approach 2:
The manual mechanical process of expert schema creation is replaced with an automated NLP-based system. The patent substitutes human expert analysis with computational language processing techniques including entity recognition, relationship extraction, and semantic analysis to generate schemas automatically, thereby resolving the contradiction between accuracy and time efficiency.
2Reliability
If manual schema creation is used, then experts can understand data context, but the process is prone to errors when experts lack accurate understanding
Solution Approach 1:
The data corpus performs self-analysis through automated NLP processing. The system extracts entities, attributes, and relationships directly from the text without relying on external expert interpretation, thereby eliminating errors caused by incomplete expert understanding while maintaining reliability through systematic computational analysis of the actual data content.
Solution Approach 2:
The system incorporates scoring mechanisms that evaluate the quality and reliability of extracted entities and relationships. By comparing multiple extractions and assigning confidence scores, the system provides feedback loops that improve schema correctness automatically, reducing errors without complicating the generation process.
3Productivity
If automated NLP schema generation is implemented, then efficiency and accuracy improve, but the system complexity increases
Solution Approach 1:
The schema generation system is divided into distinct modular components: entity recognition module, relationship extraction module, attribute identification module, and schema assembly module. Each component handles a specific aspect of the analysis independently, improving efficiency through specialized processing while managing complexity through modular architecture that allows independent development and testing of each segment.
Data Source
AI summary
In a method for generating a schema for a corpus of data, a first corpus of data is received, wherein the first corpus of data includes unstructured text. A processor identifies a set of one or more entity relationships within the first corpus of data, wherein an entity relationship comprises a first entity, a second entity, and a specified relationship between the entities. A processor compares the set of one or more entity relationships to a second corpus of data, wherein the second corpus of data includes text of a subject matter different than the corpus of data. A processor determines a score for each entity relationship based on the comparison to the second corpus of data. A processor generates a schema for the first corpus of data based on the score for each entity relationship of the set of one or more entity relationships.