Search Indexing with Document Structure Extraction
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Solution Overview
Problem
Existing search engines in enterprise computing environments face challenges in generating relevant search results for users, as they lack the ability to provide contextual information and are not tailored to the user's role within the organization, leading to inefficient search outcomes.
Innovation Solution
A search indexer extracts document structure and layout to identify key terms, populates a search index with contextual boundaries, and a search processor filters results based on user role, ensuring that relevant content is presented with the same formatting as the original document, enhancing search relevance and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a search engine searches for documents relevant to a search term, then search coverage is improved, but search result relevance to user role deteriorates
Solution Approach 1:
The system performs preliminary actions by extracting document structure and layout information before searching, and by pre-filtering search results based on user role characteristics. The search indexer analyzes documents in advance to identify key terms and their contextual boundaries, while the search processor is configured to apply user-role-based filtering, ensuring that relevance is optimized before results are presented to the user.
Solution Approach 2:
The system applies local quality by treating different parts of the search process with different qualities: document structure extraction focuses on spatial relationships and formatting, key term identification focuses on semantic meaning within contextual boundaries, and result filtering focuses on user role relevance. Each component operates with specialized quality requirements rather than uniform processing.
2Measurement precision
If contextual boundaries are extracted for key terms, then search result precision is improved, but processing complexity deteriorates
Solution Approach 1:
The system extracts only the essential contextual information needed for search precision without processing the entire document. The search indexer identifies key terms and extracts their contextual boundaries (surrounding text that provides meaning) while discarding irrelevant document content. This selective extraction maintains precision while reducing the complexity of data that needs to be processed and stored in the search index.
Solution Approach 2:
The document processing is segmented into distinct components: document structure extraction, key term identification, contextual boundary determination, and indexing. Each segment handles a specific aspect of the processing task, allowing the system to manage complexity by breaking down the overall process into manageable, specialized sub-tasks rather than processing everything uniformly.
3Measurement precision
If search results are filtered based on user role, then search result relevance is improved, but search processing time deteriorates
Solution Approach 1:
The system performs user role-based filtering as a preliminary action that is integrated into the search processing pipeline rather than as a separate post-processing step. The search processor is configured with user role information and applies filtering criteria based on the user's characteristics while results are being retrieved, combining relevance optimization with efficient processing to minimize time loss.
4Measurement precision
If document structure and layout are extracted, then contextual information quality is improved, but indexing complexity deteriorates
Solution Approach 1:
The search indexer extracts only the specific structural and layout information that is relevant for contextual understanding, such as document hierarchy, section headings, and spatial relationships of key terms. Rather than copying the entire document structure, the system selectively extracts and stores only the contextual boundary information needed for accurate search results, reducing indexing complexity while maintaining quality.
Solution Approach 2:
The indexing process is segmented into separate functional components: document structure analysis, key term extraction, contextual boundary identification, and index population. Each component handles a specific aspect of structural extraction, allowing the system to manage indexing complexity through modular processing rather than attempting to handle all structural information in a single complex operation.
Data Source
AI summary
A search client executable in a computing device transmits a search term to a search engine. The search client obtains a search result from the search engine. The search client generates a rendering of the search result using a document structure and a document layout provided by the search engine. The rendering of the search result comprises contextual content for the search term.