Meaning Taxonomy Search Engine for Document Semantic Extraction
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Solution Overview
Problem
Conventional search engines require users to manually sift through search results to identify relevant documents, as they primarily rely on keyword indexing, leading to inefficient and varied results due to differences in index building processes across engines.
Innovation Solution
A search engine that analyzes content to extract complex meaning by creating a meaning taxonomy, defining syntactic structures, and associating documents with concepts, allowing users to navigate and access relevant information through a structured framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines use keyword indexing to search documents, then the search process is simple and fast, but the search results lack accuracy and require manual sifting by users
Solution Approach 1:
The patent segments the search process into multiple stages: keyword matching, syntactic structure identification, and semantic meaning extraction. Each stage processes specific aspects of the document, dividing the complex task of accurate search into manageable components that improve overall precision without overwhelming system complexity
Solution Approach 2:
The patent introduces syntactic structures and semantic meaning entities as intermediaries between keywords and search results. These intermediaries bridge the gap between simple keyword matching and accurate semantic understanding, enabling more precise search results while maintaining a structured processing framework
2Measurement precision
If users manually assess search results to identify relevant documents, then search accuracy can be improved, but the time and effort required increases significantly
Solution Approach 1:
The patent performs preliminary extraction and organization of semantic meaning from documents before the search query is executed. By pre-processing documents to identify and structure their semantic content, the system reduces the time users need to spend assessing results, as the heavy lifting of meaning extraction is already completed
Solution Approach 2:
The patent replaces the mechanical process of manual document assessment with automated semantic analysis. The system uses computational methods to extract and evaluate semantic meaning, substituting human cognitive effort with automated processing that achieves similar or better accuracy without the time cost
3Adaptability or versatility
If different search engines use different index building processes, then each engine has its own characteristics, but search results become inconsistent and users must try multiple engines
Solution Approach 1:
The patent creates a universal semantic framework that can process and standardize results from different search engines. By translating various search results into a common semantic structure, the system maintains the versatility of different engines while ensuring consistent result composition across all sources
Data Source
AI summary
Systems and methods are provided for an expert search engine. In one embodiment, a method may employ a computer based system to define a meaning taxonomy including one or more meaning loaded entities, associate documents to the meaning taxonomy based on a set of expert rules and the syntactic structures included in the documents and provide the results to a user. In another embodiment, a system includes interfaces for meaning taxonomy, expert rule and search criteria information. The information provided by these interfaces is then used to determine how a set of documents maps to meaning taxonomy to assist the user in locating documents that are most interesting from a meaning perspective and to provide a user with an interface to navigate the meaning taxonomy as it applies to the set of documents.