Search Tolerance via Pre-built Correction Dictionary
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Solution Overview
Problem
Semantic search engines fail to provide accurate results when search terms contain misspellings, typographical errors, or partial search terms, especially for domain-specific or proper nouns, leading to 'no results found' returns.
Innovation Solution
A media search module processes media content metadata to identify misspellings, typographical errors, and partial search terms, creating a dictionary that maps incorrect search strings to correct ones, allowing for improved search tolerance and accurate results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic search algorithms are used to improve search accuracy, then search results become more accurate, but the system fails when search terms contain misspellings or typographical errors
Solution Approach 1:
The system performs preliminary actions by pre-processing media content metadata to extract names, unigrams, bigrams, and potential misspellings before the actual search occurs. This allows the system to prepare correction mappings in advance, enabling it to handle misspelled queries without requiring real-time complex analysis during the search operation.
Solution Approach 2:
The patent introduces an intermediary correction dictionary that maps incorrect search strings to correct search strings. This intermediary layer sits between the user's misspelled query and the semantic search algorithm, translating the misspelled terms into their correct forms before the semantic search processes them, thus resolving the contradiction between accuracy and tolerance.
2Adaptability or versatility
If spell checkers and type ahead features are added to handle misspellings, then search tolerance improves, but the system becomes more complex
Solution Approach 1:
The patent extracts the spell-checking and correction functionality from the main search system by processing media content metadata separately to identify names, unigrams, bigrams, and misspellings. This extraction allows the correction dictionary to be generated independently, reducing the complexity of the main search algorithm while maintaining high search tolerance.
Solution Approach 2:
The system creates a copy of the media content metadata structure (including names, unigrams, bigrams, and misspellings) to build a correction dictionary. This copying approach allows the system to store correction mappings without duplicating the entire media database, reducing memory requirements and system complexity while maintaining comprehensive correction capabilities.
3Adaptability or versatility
If the system processes media content metadata to create correction dictionaries, then search tolerance for misspellings improves, but processing time increases
Solution Approach 1:
The system performs the heavy processing of media content metadata extraction, name identification, and correction dictionary generation as a preliminary action during content indexing. This pre-processing approach moves the time-consuming tasks away from the search operation, allowing actual search queries to execute quickly without the overhead of analyzing and correcting terms in real-time.
Solution Approach 2:
The patent processes only the necessary portions of media content metadata (names, unigrams, bigrams, and potential misspellings) rather than analyzing every aspect of the entire media library. This partial processing approach creates sufficient correction coverage for most queries while minimizing the time and computational resources required for indexing and search operations.
Data Source
AI summary
Concepts and technologies disclosed herein are directed to increasing misspelling, typographical, and partial search tolerance for search terms. A system can obtain media content metadata associated with media content. The system can identify a plurality of names within the media content metadata and remove a plurality of stop words from the media content metadata. The system can determine a plurality of unigrams and a plurality of bigrams from the media content metadata. The system can determine a plurality of misspellings of the media content metadata. The system can compile the plurality of names, the plurality of stop words, the plurality of unigrams, the plurality of bigrams, and the plurality of misspellings as results. The system can determine a plurality of incorrect search strings based upon the results and can create a dictionary including the plurality of incorrect search strings mapped to a plurality of correct search strings.