Semantic Content Extension via Vector Region Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for extending content on web pages are inefficient and inaccurate, as they rely on human editors or computer programs to manually add related information, which often fails to target specific subjects within a document and lacks semantic relevance.
Innovation Solution
The method involves dividing content into regions, calculating vectors for each region, determining relevance scores with a terms vector table, and selecting and rendering extending terms with the highest scores to provide semantically relevant information seamlessly integrated with the original content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human editors manually add related information to content, then the relevance and accuracy of extending content can be improved, but the efficiency and productivity are significantly reduced
Solution Approach 1:
The system enables automatic content extension by dividing content into regions, calculating vectors for each region, and selecting extending terms based on semantic relevance scores, eliminating the need for manual human editors while maintaining high relevance accuracy through algorithmic semantic analysis
Solution Approach 2:
The manual mechanical process of human editors reviewing and adding content is replaced with an automated computational system that uses vector calculations and relevance scoring to automatically generate and integrate extending terms, dramatically improving efficiency while preserving accuracy
2Productivity
If computer programs automatically search for and add related information, then the efficiency and productivity are improved, but the relevance accuracy and measurement precision deteriorate due to inability to target specific subjects
Solution Approach 1:
The system divides the content into multiple content regions, allowing the automatic program to process and extend each region independently based on its specific subject matter, thereby maintaining high relevance accuracy while preserving computational efficiency through targeted processing
Solution Approach 2:
The system calculates vectors for each content region and selects extending terms specific to each region's semantic content, ensuring that the automatic extension process maintains high relevance accuracy by tailoring the extending terms to the local context of each content region rather than applying generic extensions
3Quantity of substance
If extending content is provided for the whole document, then the coverage is comprehensive, but the ability to target specific subjects with different meanings is lost
Solution Approach 1:
The system segments the document into multiple content regions, enabling comprehensive coverage of the entire document while simultaneously allowing each region to be extended with subject-specific terms that accurately reflect the unique meaning of that particular region
Solution Approach 2:
The system applies different extending terms to different content regions based on their specific semantic content, ensuring that each region receives tailored extensions that match its subject matter while the system as a whole maintains comprehensive document coverage
Data Source
AI summary
The present invention provides a method and system for extending content based on the semantic meaning of content. It divides content into multiple content regions and finds words and/or phrases that are semantically relevant to the current content region and appends these words and/or phrases to the current content region as extended content. The extended content matches semantically with the original content in such a seamless way that users may think it is a part of the content.