Context-Driven Search Using Semantic Similarity for Query Accuracy
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Solution Overview
Problem
Keyword-based information retrieval systems fail to provide accurate results when the search corpus does not contain the specific keywords of the query, leading to suboptimal search outcomes.
Innovation Solution
Implementing a context-driven, AI-based search system that utilizes natural language processing and machine learning models to analyze the context and relationships between text segments, generating and ranking search results based on semantic similarity rather than keyword matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based search techniques are used, then the search system is simple and fast, but the search accuracy deteriorates when the search corpus does not feature the specific keywords of interest
Solution Approach 1:
The patent replaces the mechanical keyword-matching system with an AI-based semantic analysis system. Instead of relying on exact keyword matches between queries and corpus, the system uses natural language processing and machine learning models to understand the meaning and context of queries, transforming the search mechanism from literal matching to semantic understanding.
Solution Approach 2:
The patent changes the fundamental parameter of search from keyword presence to semantic similarity. By transforming the search criterion from exact word matching to contextual understanding using AI models, the system can retrieve relevant information even when specific keywords are absent from the corpus, thereby improving search accuracy without being constrained by keyword availability.
2Adaptability or versatility
If keyword-based search is used, then the system requires minimal processing, but the ability to understand query intent and context is lost
Solution Approach 1:
The patent applies preliminary action by pre-training AI models on large corpora to learn semantic relationships and contextual patterns before actual search operations. This pre-processing enables the system to quickly perform semantic analysis during search without requiring extensive computational resources for each individual query, as the heavy lifting of learning contextual relationships has already been accomplished during the training phase.
Data Source
AI summary
Systems, methods, and apparatus for context-drive search are disclosed. An example apparatus includes memory to store machine-readable instructions, and at least one processor to execute the machine-readable instructions to at least tokenize text included in a query for content into text portions, encode the text portions into respective vectors, organize the text portions based on natural language similarity of the text portions, the natural language similarity based on the respective vectors, and generate one or more search results based on the organized text portions, and rank the one or more search results for presentation on a computing device.