HTML Table Normalization for Complex Spanning Cell Alignment
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Solution Overview
Problem
Current table conversion technologies, such as those converting HTML tables to 2D arrays, do not effectively support complex tables with spanning cells and defined format styles, which hinders accurate alignment and header detection essential for machine learning and knowledge management systems like IBM Watson.
Innovation Solution
A method and system that create a 2D array representation of HTML files with orthogonal rows and columns, copying values or object references for spanning cells, and performing deduplication based on row or column features to achieve vertical and horizontal alignment, enabling effective header detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple table conversion to 2D arrays is used, then the conversion process is simple and fast, but it cannot handle complex tables with spanning cells and format styles
Solution Approach 1:
The patent segments the table conversion process into multiple passes: first converting the table to a 2D array structure, then performing multiple alignment passes to handle spanning cells and format styles. Each pass processes specific aspects of alignment, gradually refining the result to handle complex table structures while maintaining efficiency.
Solution Approach 2:
The patent performs preliminary conversion to a 2D array representation before handling complex alignment issues. This preliminary step simplifies the data structure for processing, and subsequent passes then address the complexity of spanning cells and format styles, enabling both speed and accuracy.
2Measurement precision
If cell alignment is performed accurately in complex tables, then header detection accuracy improves, but the processing time and complexity increase
Solution Approach 1:
The alignment process is segmented into multiple passes, where each pass handles specific alignment tasks. This segmentation allows the system to achieve accurate header detection in complex tables by progressively refining alignment through dedicated passes rather than attempting to solve all alignment issues simultaneously.
Solution Approach 2:
The patent creates a copy of the table structure in a 2D array format, allowing alignment operations to be performed on the copy rather than the original complex structure. This copying enables efficient processing while maintaining the ability to handle complex table formats, reducing processing time while preserving accuracy.
3Measurement precision
If spanning cells are handled by copying values, then alignment accuracy improves, but data redundancy increases
Solution Approach 1:
The patent segments the table into orthogonal rows and columns, allowing spanning cells to be handled by copying values only where necessary. This segmentation enables precise alignment while minimizing redundant data by copying only the differences rather than replicating entire structures.
Solution Approach 2:
The patent applies local quality by copying values only in specific locations where spanning cells occur, rather than uniformly across the entire table. This selective copying approach maintains alignment accuracy where needed while avoiding unnecessary data redundancy in simple table regions.
Data Source
AI summary
A method, system and computer-usable medium are disclosed for finding vertically and horizontally aligned cells in a complex table structure. A file or document, such as an HTML file that defines a complex table includes spanning rows and columns is expanded into a two dimensional (2D) array or table with orthogonal rows and columns, where the spanning rows and columns included cells with copied values or object references. The expanded 2D array or table can be deduplicated row or column wise to determine header alignment of the table.