Knowledge Graph Table Processing Retaining Row Column Relationships
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Solution Overview
Problem
Existing methods for information extraction from complex tables in documents often lose layout information, leading to reduced accuracy in understanding unstructured content due to simple extraction of value fields without retaining row and column relationships.
Innovation Solution
A method and apparatus for processing data based on a knowledge graph that acquires and recognizes table content, determines row and column attributes using a preset table lexicon, and generates a quadruple list to retain contextual information, ensuring accurate extraction and retention of table relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple extraction of value fields is performed, then extraction speed is improved, but layout information is lost leading to reduced accuracy
Solution Approach 1:
The patent segments table extraction into multiple processing stages: initial value field extraction, row attribute identification, column attribute identification, and quadruple list generation. This segmentation allows parallel processing of different table components while maintaining their relationships, thus improving both speed and accuracy.
Solution Approach 2:
The patent transforms traditional two-dimensional table data into a four-dimensional quadruple structure (table_name, row_attribute, column_attribute, attribute_value). This dimensional expansion preserves layout information by explicitly encoding row and column relationships, enabling accurate reconstruction of table semantics while maintaining extraction efficiency.
2Loss of information
If row and column relationships are retained, then integrity of extracted content is improved, but processing complexity increases
Solution Approach 1:
The patent extracts row attributes and column attributes as separate identifiable entities from the table structure. By isolating these structural elements and representing them as distinct quadruples, the system maintains table integrity without requiring complex processing of the entire table structure at once, thus reducing processing complexity while preserving information完整性.
Solution Approach 2:
The quadruple list serves as an intermediary data structure that bridges the gap between raw table data and semantic understanding. Each quadruple encapsulates a complete semantic unit with its layout context, acting as a mediator that preserves row-column relationships without requiring complex ongoing processing, thereby maintaining integrity while simplifying subsequent operations.
3Measurement precision
If attribute identification based on lexicon matching is performed, then extraction accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary segmentation of table content into candidate row attributes and column attributes before full lexicon matching. This preliminary classification narrows down the search space for subsequent detailed matching, allowing accurate attribute identification through lexicon comparison while significantly reducing the overall processing time by avoiding exhaustive matching of all table cells.
Data Source
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AI summary
Disclosed are a method for processing data, an electronic device and a medium. The technical solution includes: acquiring (101, 301) a table to be processed and a corresponding table name; recognizing (102, 302) the table to acquire each cell content in the table; determining (103) a row attribute and a column attribute corresponding to each cell contents based on a matching degree between each cell content and a word segmentation in a preset table lexicon; and determining (104, 305) a quadruple list corresponding to the table based on the table name, the row attribute and the column attribute corresponding to each cell content.