Phrase-Based Indexing System for Semantic Search Accuracy
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Solution Overview
Problem
Conventional information retrieval systems index documents based on individual words rather than concepts, failing to capture topical relationships between phrases and requiring extensive computational resources to manage all possible phrases, which limits their ability to identify and rank documents accurately based on semantic meaning.
Innovation Solution
An information retrieval system that identifies and indexes documents using phrases, predicting the co-occurrence of related phrases through a prediction measure like information gain, allowing for the creation of clusters that represent semantically meaningful groupings and improve search results by ranking documents based on their relevance to query phrases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system indexes all possible phrases (e.g., 3.2×10^26 possible five-word phrases), then complete phrase coverage is achieved, but memory and computational resources become insufficient
Solution Approach 1:
The patent applies local quality by transitioning from uniform term-weighting to position-dependent weighting, where different positions in a phrase contribute differently to the weight calculation. This allows the system to capture local contextual relationships within phrases without requiring exhaustive indexing of all possible phrase combinations, thereby improving phrase coverage while managing computational resources efficiently.
Solution Approach 2:
The system performs preliminary action by pre-calculating and storing phrase weights during the indexing phase, rather than computing them during query processing. This allows the search engine to efficiently retrieve pre-computed phrase weights and apply them during searching, significantly reducing the computational burden during query execution while maintaining comprehensive phrase analysis capabilities.
2Device complexity
If conventional systems index documents based on individual terms, then indexing simplicity is maintained, but the ability to capture topical relationships and semantic meaning is lost
Solution Approach 1:
The patent applies segmentation by dividing the document into overlapping phrases of various lengths (bigrams, trigrams, etc.) rather than treating it as a sequence of individual terms. Each phrase is then weighted based on its positional information and frequency, allowing the system to capture topical relationships and semantic meaning while maintaining a structured approach to indexing that balances complexity and information retention.
3Quantity of substance
If the system uses predetermined phrases selected by human operators, then computational resources are reduced, but the ability to capture emerging and dynamic phrases is limited
Solution Approach 1:
The patent applies dynamics by implementing a dynamic phrase weighting system that automatically adapts to emerging phrases and changing usage patterns. The system continuously analyzes document frequencies and positional relationships, updating phrase weights accordingly. This allows the system to capture emerging phrases and dynamic language usage without requiring manual curation, while maintaining computational efficiency through automated weight calculation based on observed patterns.
Data Source
AI summary
An information retrieval system uses phrases to index, retrieve, organize and describe documents, analyzing documents and storing the results of the analysis as phrase data. Phrases are identified that predict the presence of other phrases in documents. Documents are the indexed according to their included phrases. Related phrases and phrase extensions are also identified. Changes to existing phrase data about a document collection submitted by a user is captured and analyzed, and the existing phrase data is updated to reflect the additional knowledge gained through the analysis.