Semantic Forward Search Indexing for Publication Corpus
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Solution Overview
Problem
Current indexing techniques fail to capture semantic meaning, resulting in incomplete and inadequate search results in large publication corpora.
Innovation Solution
The implementation of semantic indexing using machine-learned models that project search queries and publication titles into a shared semantic vector space, enabling the identification of closest matches based on semantic meaning, and the use of forward and reverse semantic search indexes to enhance search efficiency and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword-based indexing techniques are used, then the indexing process is simple and fast, but the search results fail to capture semantic meaning and are incomplete
Solution Approach 1:
The patent transforms the indexing approach by changing the parameter representation from discrete keywords to continuous semantic vectors in a multi-dimensional space. Machine-learned models project both search queries and publication titles into this shared vector space, enabling semantic similarity measurement through geometric distance calculations rather than exact keyword matching.
Solution Approach 2:
The patent introduces semantic vector representations as an intermediary between the search query and the publication corpus. Instead of directly comparing keywords, the system uses machine-learned vector embeddings that capture latent semantic relationships, serving as a mediator that bridges the gap between textual input and meaningful search results.
2Measurement precision
If semantic vector space projection is implemented, then comprehensive semantic search results are achieved, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs semantic vector projection and index construction in advance before actual search operations. By pre-computing the semantic representations and organizing them in the vector space during an offline phase, the system reduces the computational burden during online search, allowing for faster query processing while maintaining semantic accuracy.
Solution Approach 2:
The patent divides the search process into distinct segments: query embedding generation, vector similarity computation, and result ranking. This segmentation allows each component to be optimized independently, with machine-learned models handling semantic understanding and efficient algorithms managing the large-scale vector comparisons in the publication corpus.
Data Source
AI summary
Embodiments of the present disclosure relate generally to semantic indexing to improve search results of a large corpus. Some embodiments identify one or more closest matches between (i) a search semantic vector that corresponds to a search query, the search semantic vector based on a first machine-learned model that projects the search query into a semantic vector space, and (ii) a plurality of publication vectors corresponding to respective publications in the publication corpus, the plurality of publication vectors based on a second machine-learned model that projects the plurality of publication vectors into the semantic vector space.