Annotation Index System Using Snippet Segmentation for Search Efficiency
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Solution Overview
Problem
Current search engines face inefficiencies in indexing and retrieval processes, particularly in handling annotations, which affect query response times and accuracy due to limitations in weighting annotations, processing high-frequency annotations, and matching short documents.
Innovation Solution
A method and system for encoding an inverted list structure of annotation material, including forming a snippet index and a word index with per-annotation weighting, and using similarity functions for query-annotation matching, along with a two-level index structure for efficient storage and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional keyword indexing is used to index crawled material, then the search engine can provide comprehensive search capabilities, but the query response time increases and efficiency decreases due to the inability to effectively handle annotations and high-frequency terms
Solution Approach 1:
The patent segments the indexing process by creating separate index structures for different types of content elements (annotations, snippets, high-frequency terms) rather than treating all keywords uniformly. This allows the search engine to query optimized structures for different query types, improving response time while maintaining accuracy through specialized handling of each content type.
Solution Approach 2:
The patent changes the parameter of indexing by introducing document weights and annotation weights as additional dimensions to traditional keyword indexing. By incorporating these weight parameters, the system can prioritize relevant annotations and snippets in search results, improving both response time through filtered results and accuracy through weighted relevance ranking.
2Measurement precision
If all annotations are indexed with equal weight, then the indexing process is simple, but the search accuracy decreases because high-frequency annotations and important snippets cannot be distinguished
Solution Approach 1:
The patent applies local quality by assigning different weights to different annotations based on their characteristics (frequency, relevance, source). Instead of uniform weighting, each annotation receives a localized weight appropriate to its specific properties, enabling precise differentiation of importance while maintaining a manageable indexing structure through systematic weight assignment rules.
3Quantity of substance
If a comprehensive inverted index is created for all keywords, then search coverage is maximized, but memory requirements increase significantly
Solution Approach 1:
The patent extracts and separates high-frequency annotations and common snippets from the main inverted index into dedicated optimization structures. By taking out these frequently occurring elements and creating specialized index entries for them, the system maintains comprehensive search coverage while reducing the overall memory footprint of the index structure.
Solution Approach 2:
The patent discards redundant information by consolidating multiple identical or near-identical annotations into single index entries with aggregated weight values. This discarding of duplicate data reduces memory consumption while the weight aggregation recovers the search capability, allowing the system to efficiently handle high-frequency annotations without proportionally increasing memory requirements.
Data Source
AI summary
A method of encoding on a computer system for information retrieval in an inverted list structure of annotation includes collecting a group of documents and storing them in a digital format, determining a group of annotations referencing the group of documents, and forming a snippet index by grouping the group of annotations by unique annotation identifier. The method also includes forming a snippet dictionary which, for each unique annotation identifier, indexes a corresponding position in the snippet index for the group of annotations having that unique annotation identifier.