Semantic Search Using Single-Source Model
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Solution Overview
Problem
Conventional semantic search methods face challenges in handling sparse data, large datasets, and determining optimal parameters, leading to sub-optimal search results and high computation times, especially in full-fledged desktop search scenarios.
Innovation Solution
A single-source semantic model is used, derived from a reference source like Wikipedia, to compute similarity and relevance metrics for documents and search queries, allowing for efficient ranking of search results across data stores without requiring prior knowledge of concepts or dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional semantic search methods use SVD or probabilistic models with predefined k dimensions, then search accuracy can be improved, but the computation time and resource requirements increase dramatically
Solution Approach 1:
The patent pre-computes and stores document embeddings in a reduced-dimensional semantic space before search queries are received. By performing the computationally intensive dimensionality reduction and embedding operations in advance, the system avoids repeating these calculations for each search query, thus improving search accuracy while reducing real-time computation time
Solution Approach 2:
The patent creates simplified copies of documents in the form of fixed-dimensional embeddings that capture semantic meaning. These embedding vectors serve as compact representations that can be quickly compared during search operations, maintaining search accuracy while dramatically reducing the computational complexity of actual search queries
2Quantity of substance
If users have hundreds of gigabytes of data to index, then the completeness and coverage of the search system is improved, but the index size becomes huge and computation times become inexcusable
Solution Approach 1:
The patent transforms documents from their original high-dimensional text representation into fixed-dimensional dense embeddings. This parameter transformation compresses the data representation while preserving semantic information, allowing the system to handle large volumes of data with reduced storage requirements and faster processing times
Solution Approach 2:
The patent replaces traditional text-based indexing mechanisms with neural network-based embedding generation. This substitution enables the system to process and compress large datasets more efficiently by leveraging the parallel processing capabilities of neural networks to generate compact semantic representations
3Ease of manufacture
If users have few or no files to create an index, then the system setup is simpler, but the sparsity of data leads to sub-optimal search results
Solution Approach 1:
The patent implements a dual-mode system that can function effectively with either small or large datasets. By pre-training on external corpora and offering flexible configuration options, the system adapts to different data volumes while maintaining search quality, making it universally applicable across diverse deployment scenarios
Solution Approach 2:
The patent introduces pre-trained external corpora as an intermediary knowledge source that supplements sparse user data. By incorporating knowledge from external sources during the embedding process, the system compensates for data sparsity and maintains search result quality even when user datasets are small
4Measurement precision
If the number of concepts k is optimized for each individual dataset, then search accuracy is improved, but it becomes difficult to find the optimal parameter and increases system complexity
Solution Approach 1:
The patent fixes the dimensionality parameter to a predetermined value and uses pre-trained embedding models, eliminating the need for manual optimization of the k parameter. This approach maintains search accuracy by leveraging transfer learning while significantly reducing the complexity of system configuration and parameter tuning
Data Source
AI summary
Techniques for providing semantic search of a data store are disclosed. A similarity metric of a document comprising the data store to a concept represented in a semantic model derived at least in part from a reference source that includes content not included in the data store is determined. A relevance metric of a search query to the concept is computed. The similarity metric and the relevance metric are used to determine, at least in part, a ranking of the document with respect to the search query.