Per-Document Index for Semantic Search Context Preservation
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Solution Overview
Problem
Traditional search ranking algorithms rely solely on keywords, failing to capture contextual information, leading to less relevant search results as data sizes increase and user intent is not adequately met.
Innovation Solution
Generating a per-document index (PDI) that preserves semantic and contextual information by translating and encoding document terms, allowing for efficient storage and retrieval of contextually relevant data, which can be combined with inverted indices to provide more accurate search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional inverted index is used for search, then search speed is fast, but contextual information is lost and search relevance decreases
Solution Approach 1:
The document is segmented into multiple sections (e.g., title, body, metadata) and each section is independently encoded into the PDI. This segmentation allows the system to preserve contextual information from different parts of the document while maintaining efficient storage and retrieval capabilities.
Solution Approach 2:
The PDI is generated in advance during document processing, storing encoded contextual information before search queries are executed. This preliminary encoding action enables fast search-time retrieval of contextual information without incurring significant search-time penalties, resolving the contradiction between information preservation and search speed.
2Measurement precision
If rich per-document information is stored to improve search quality, then search accuracy improves, but storage requirements increase
Solution Approach 1:
The encoding process transforms original document terms into compressed representations (e.g., using term frequencies, positional information, and section identifiers). This parameter transformation reduces the storage volume required to represent rich per-document information while preserving the semantic and contextual meaning needed for accurate search ranking.
3Measurement precision
If contextual information is analyzed for each search query, then search relevance improves, but search time increases
Solution Approach 1:
Contextual information is encoded into the PDI structure during document processing, organizing it in a manner that enables efficient retrieval. When a search query is executed, the pre-encoded contextual information can be quickly accessed and applied to ranking calculations without requiring time-consuming analysis of the original document text, thus maintaining fast search times while improving relevance.
Data Source
AI summary
Methods, computer systems, and computer-storage medium for generating a per-document index used for semantic searching is provided. A document is received and parsed into a plurality of section. Each term in each section is translated in order to at least one of a cache index or a term identifier. Subsequent to translating the terms, each section is separately group encoded to generate the per-document index. The per-document index is stored in association with a data store.