Hierarchical Tree Structure for Context-Aware Information Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional information extraction techniques, such as keyword search and wrapper induction, fail to recognize context and incorporate synonyms, leading to irrelevant results due to the lack of uniform representation across different information sources.
Innovation Solution
A method and system that utilize a tree structure to identify nodes of interest and extract sub-trees, traversing upward paths to relate each node to its ancestors, and select records containing relevant information based on pre-defined contexts, thereby filtering out irrelevant data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword search is used to extract information from various sources, then the search can be conducted based on user-provided keywords, but the technique generates a significant number of irrelevant results because it does not recognize the context in which the keyword is being used
Solution Approach 1:
The patent transforms the flat keyword matching approach into a hierarchical tree structure analysis. Instead of treating keywords in isolation, the system builds a tree representation of the data set where keywords are nodes connected through hierarchical relationships. This dimensional transformation allows the system to evaluate keywords in the context of their ancestral paths, filtering out irrelevant results by checking whether the keyword appears in a contextually appropriate hierarchical path rather than simply counting occurrences.
2Ease of operation
If conventional search techniques are used, then the search process is simple, but the techniques fail to incorporate the synonyms and connotations of the keywords that are rife in natural language content
Solution Approach 1:
The patent creates a universal tree structure that can accommodate multiple types of relationships simultaneously - hierarchical relationships, synonym relationships, and contextual relationships. The tree structure serves multiple functions: it organizes data hierarchically, captures synonym connections through parallel branches, and enables contextual analysis through path traversal. This multi-functional structure allows the system to handle synonyms and connotations without complicating the user interface or search initiation process.
3Ease of manufacture
If wrapper induction technique is used to extract information, then information can be extracted using pre-defined templates, but the technique does not work efficiently because different sources of information are not represented in a uniform format and there is a lack of common structural features across various sources
Solution Approach 1:
The patent develops a universal tree structure representation that can accommodate diverse information sources with different formats and structures. Instead of requiring separate templates for each source type, the system transforms various data formats into a common tree structure where the hierarchical relationships and contextual connections are preserved. This universal representation enables efficient information extraction across heterogeneous sources while maintaining adaptability to their unique characteristics.
Data Source
AI summary
The present invention provides a method and a system for identifying relevant information in a data set. The method involves the identification of nodes of interest in a tree structure. A node of interest is a node that contains information, which is relevant to a pre-defined context. The method further involves the step of iteratively extracting sub-trees from the tree structure and identifying records in the extracted sub-trees. The sub-tree is a hierarchical structure that shows the relationship of each node of interest with its ancestor nodes in the tree structure. Each record is a group of sub-tree nodes and contains at least one node of interest.