Semantic Search Engine Using Neural Network Embeddings
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Solution Overview
Problem
Traditional search engines face challenges in providing high-quality semantic matches and efficient retrieval from large databases, requiring significant computational resources and often relying on keyword-based methods that are inefficient for semantic searches.
Innovation Solution
A system utilizing machine learning models to generate semantic representations of text, create training datasets, and train models to predict documents with similar text to an input query, enabling deep semantic search engines for document stores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search engines use crawlers to download and index content from various web pages and sources, then they can provide comprehensive search coverage, but they require large-scale distributed computing infrastructure and significant computational resources to yield fast results
Solution Approach 1:
The patent segments the search process into two distinct phases: an offline phase where a neural network model is trained on a corpus of text data to learn semantic representations, and an online phase where the trained model performs rapid semantic similarity comparisons between queries and documents. This segmentation allows the computationally intensive work to be done once during training, enabling fast, resource-efficient queries thereafter.
Solution Approach 2:
The patent performs preliminary action by pre-training a neural network model on a large corpus of text data before actual search operations. The model learns to generate semantic representations and similarity scores in advance, so that during query processing, only lightweight forward propagation through the trained model is needed, rather than performing full-text analysis or complex indexing operations at query time.
2Productivity
If keyword based methods are used to improve search speed and reduce computational requirements, then processing becomes faster and more efficient, but the ability to provide high quality semantic matches deteriorates
Solution Approach 1:
The patent substitutes mechanical keyword-matching operations with a neural network-based semantic similarity system. Instead of comparing individual keywords or using simple string matching algorithms, the system uses a trained neural network to compute semantic representations and similarity scores, capturing nuanced meaning and context that keyword methods miss while maintaining computational efficiency through the pre-trained model.
3Measurement precision
If additional features such as providing high quality semantic matches and retrieval from a large database are implemented, then search accuracy improves, but computational resources required increase significantly
Solution Approach 1:
The patent changes the parameter representation from raw text or keyword vectors to dense semantic embeddings generated by a neural network. These embeddings transform high-dimensional text data into compact, meaningful vector representations that capture semantic relationships, enabling accurate similarity computation with reduced dimensionality and lower computational cost during query processing.
Data Source
AI summary
A method facilitating deep semantic search engines for document stores includes generating, by a system including a processor and using a first machine learning model component, semantic representation data for first excerpts of a document. The method also includes producing, by the system, a training dataset based on the document. The training dataset comprises samples, and respective ones of the samples include sample data, selected from the semantic representation data and associated with an excerpt of the first excerpts, and reference data indicative of the document. The method further includes training, by the system and using the training dataset, a second machine learning model component to predict a target document from a group of documents, including the document, having a second excerpt that matches an input query by at least a threshold amount.