Density-Based Search Algorithm for Unstructured Document Processing
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Solution Overview
Problem
Conventional search platforms struggle to provide accurate and relevant search results from unstructured documents, as they typically rely on 'chunking' approaches that only consider the beginning portions of documents, leading to missed relevant information.
Innovation Solution
A search algorithm management system that employs a density-based analysis to identify the most relevant portions of unstructured documents by partitioning search results into chunks based on keyword-matching density, allowing for the generation of featured snippets that include the highest relevance text, and enables merchants to select search algorithms and customize entity types for uniform data updates across multiple platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional chunking approach is used to search only beginning portions of documents, then search processing speed is improved, but search result accuracy deteriorates because relevant information may be located in other portions of the document
Solution Approach 1:
The document is segmented into multiple chunks of predetermined size, and the search is performed on all chunks rather than only the beginning portion. This allows the system to maintain processing efficiency while accessing the entire document content to improve search accuracy.
Solution Approach 2:
The search approach transitions from a one-dimensional sequential scan (only beginning portions) to a multi-dimensional approach where documents are divided into multiple segments and searched simultaneously across different portions, enabling both speed and accuracy.
2Measurement precision
If density-based analysis is applied to identify relevant portions of unstructured documents, then search result accuracy is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
Documents are pre-processed into chunks of predetermined size before the search query is received. This preliminary segmentation reduces the complexity of analyzing unstructured documents during the actual search operation, as the system only needs to process smaller, pre-divided units rather than entire documents.
Solution Approach 2:
The system changes the parameter of document segmentation by introducing a predetermined chunk size parameter. This transforms the complex task of analyzing entire unstructured documents into a series of simpler analyses of smaller segments, reducing overall processing complexity while maintaining accuracy.
Data Source
AI summary
A system and method to receive a search query input from an end user system, wherein the search query input relates to information associated with a merchant system. In view of the search query, a document associated with the merchant system is identified. A set of portions of the document are identified. A keyword-matching density level is determined for each portion of the set of portions of the document. A selected portion of the set of portions having a highest relative keyword-matching density level is identified. A graphical user interface including a search result responsive to the search query is generated, the graphical user interface including the selected portion of the document.