Semantic Layer for Unstructured Text Association
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Solution Overview
Problem
Existing systems for extracting information from text in business intelligence applications are time-consuming and require extensive customization, making them inefficient and resource-intensive, especially in environments with limited resources, and often miss pertinent information.
Innovation Solution
A computer-readable storage medium with executable instructions that receives a semantic abstraction of an underlying data source, parses unstructured text into units, matches dimension values with the text units, and stores indications of matches, allowing for efficient association of unstructured text with structured data using existing business intelligence infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems perform intensive standalone analysis of text to extract information, then information extraction capability is improved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent introduces an intermediary layer (semantic layer with dimensions and dimension values) between the unstructured text and the structured data source. This intermediary enables efficient matching by pre-organizing data relationships, avoiding intensive standalone text analysis while maintaining accurate information extraction capability
Solution Approach 2:
The system performs preliminary organization of data into a semantic layer with predefined dimensions and dimension values before text processing. This pre-structured framework allows for rapid matching during text analysis, reducing processing time while preserving extraction accuracy
2Measurement precision
If existing systems perform intensive standalone analysis of text, then information extraction capability is improved, but system complexity and customization requirements increase
Solution Approach 1:
The patent creates a universal semantic layer framework that can be applied across different data sources and text types. The dimension-value structure provides a standardized interface that works with various data formats, reducing system complexity and eliminating the need for extensive customization while maintaining extraction capability
3Measurement precision
If existing systems perform intensive standalone analysis of text, then information extraction capability is improved, but resource consumption increases
Solution Approach 1:
The semantic layer acts as an intermediary that pre-organizes data relationships, enabling efficient text matching without requiring resource-intensive standalone analysis. This intermediary structure reduces computational resources needed while maintaining extraction accuracy
Solution Approach 2:
By pre-organizing data into dimensions and dimension values before text processing, the system eliminates the need for repeated intensive analysis during text processing, significantly reducing resource consumption while preserving extraction capability
Data Source
AI summary
A computer readable storage medium includes executable instructions to receive a semantic abstraction describing at least one underlying data source. The semantic abstraction includes at least one dimension with at least one dimension value. Unstructured text is parsed into parsed text units. A dimension value is matched to a parsed text unit to form matched content. An indication of the matched content is stored.