Semantic Text Search via Sentence Tree Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current search engines rely on text-based searches that fail to capture the meaning of search terms with multiple definitions or interpretations, leading to unsatisfactory results, especially when multiple search terms are used, as they do not account for the encoded meaning in the text or the intended meaning of the user's query.
Innovation Solution
A semantic search system that converts text corpora into sentence trees and matches search queries with these trees, allowing for the retrieval of documents based on the semantic meaning, including refinements and sentiment extraction, by utilizing additional knowledge bases and linguistic constructs to enhance precision and recall.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text-based search is used, then search speed and simplicity are maintained, but search precision and relevance deteriorate when search terms have multiple definitions or when multiple terms are used
Solution Approach 1:
The patent introduces sentence trees as an intermediary structure between traditional keyword search and semantic understanding. The text corpus is converted into sentence trees that capture grammatical relationships and semantic meaning, while search queries are also converted into query trees. The matching process operates at the tree level rather than simple keyword matching, enabling semantic search without requiring complex natural language processing pipelines. This intermediary representation resolves the contradiction by providing structured semantic information that improves precision while maintaining relative system simplicity.
2Adaptability or versatility
If simple keyword matching is used, then system complexity is low, but the ability to understand encoded meaning in text and user intent deteriorates
Solution Approach 1:
The patent segments text into sentences and further segments sentences into grammatical components represented as trees. Each sentence is parsed into a sentence tree with nodes representing grammatical elements and edges representing relationships. This segmentation allows the system to capture semantic structure at multiple levels (sentence level and grammatical element level) without requiring monolithic complex processing. The segmented tree structures enable versatile semantic understanding while keeping individual processing steps manageable.
3Measurement precision
If multi-term queries are interpreted as simple logical combinations, then processing speed is maintained, but search relevance and focus deteriorate
Solution Approach 1:
The patent performs preliminary conversion of the entire text corpus into sentence trees before search operations. This preprocessing step creates a structured representation that captures semantic relationships in advance. During search, queries are converted to query trees and matched against the pre-built sentence tree index. This preliminary action eliminates the need for complex real-time semantic analysis during query processing, maintaining fast search performance while achieving high relevance through semantic matching at the tree level.
Data Source
AI summary
A system for performing semantic search receives an electronic text corpus and separates the text corpus into a plurality of sentences. The system parses and converts each sentence into a sentence tree. The system receives a search query and matches the search query with one or more of the sentence trees.