SVO Query Tokenization for More Accurate Natural Language Search
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Solution Overview
Problem
Conventional search engines struggle to effectively utilize natural language queries by relying on keyword-based systems, failing to leverage the structural and semantic richness of human language for improved information retrieval.
Innovation Solution
A system and method that employs a natural language processing (NLP) analyzer to identify and classify subject-verb-object (SVO) triplets in text, generating hierarchical classifications and producing a token stream for enhanced search capabilities, allowing for the analysis of natural language documents and queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based search systems are used, then search engine simplicity is maintained, but search accuracy and relevance deteriorate
Solution Approach 1:
The search system segments natural language queries into subject-verb-object triplets, extracting semantic components from the natural language text. This segmentation allows the system to process and index queries based on their structural components rather than individual keywords, improving search accuracy while maintaining manageable system complexity through modular processing stages.
Solution Approach 2:
The patent introduces an intermediary representation layer between natural language queries and search indexing. By converting queries into standardized SVO triplet representations with hierarchical classifications, the system creates a bridge that translates diverse natural language inputs into a consistent search format, improving accuracy without requiring complex direct processing of all query variations.
2Measurement precision
If natural language processing is applied to capture structural and semantic nuances, then search relevance is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing by pre-establishing hierarchical classifications for subject, verb, and object components during the indexing phase. This preliminary action allows the actual search operation to rely on pre-computed classifications rather than performing complex natural language analysis in real-time, improving relevance while minimizing processing time during query execution.
Solution Approach 2:
The patent implements dynamic processing where the depth of SVO triplet analysis and hierarchical classification can be adjusted based on query complexity and user needs. The system can operate at different levels of processing depth, allowing fast approximate matches for simple queries while providing comprehensive semantic analysis for complex queries, thus balancing relevance and processing time.
3Adaptability or versatility
If hierarchical classifications are generated for all possible SVO variations, then search comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The system applies local quality by generating hierarchical classifications at specific levels of the SVO triplet structure rather than uniformly across all possible variations. By focusing classification efforts on the most significant and commonly occurring SVO patterns and relationships, the system achieves comprehensive search coverage for relevant queries while avoiding the complexity of classifying every possible edge case.
Data Source
AI summary
A system and computer-implemented method facilitate expansion of knowledge. The system allows for characterization of natural language documents and of search queries to locate those documents. A natural language processing (NLP) analyzer finds subject-verb-object (SVO) triplets in received text and assigns initial hierarchical classifications to word components of the SVO triplets. An SVO analyzer generates variation hierarchical classifications by varying the initial hierarchical classifications assigned, selects at least one hierarchical classification from the initial hierarchical classifications and variation hierarchical classifications, and produces a token stream of tokens. The tokens represent respective hierarchical classifications of the at least one hierarchical classification selected. The token stream produced may represent a natural language (NL) document to be stored to facilitate matching the NL document to a subsequently independently specified query. Alternatively, the token stream produced may represent a query and the token stream is used for generating a response to the query.