Semantic Keyword Mapping for Buyer-Seller Search Matching
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Solution Overview
Problem
Current computer-aided search technologies face challenges in accurately narrowing search results to the intended scope due to the ambiguity of keywords, leading to false hits and out-of-scope citations, especially in large search spaces like the web, as they fail to reliably map human-specified values and characteristics to the correct attributes in entity descriptions.
Innovation Solution
A method and apparatus that maps lexical keywords into entity description semantics using a computer program and data storage mechanisms, allowing buyers to confirm unambiguous descriptions by scoring hierarchical category descriptions, suggesting attributes, and enabling dynamic user feedback to refine searches effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lexical associations are injected iteratively into computer-aided searches, then the search space is narrowed rapidly, but the occurrence of false hits is not reliably eliminated
Solution Approach 1:
The system implements feedback loops where search results are continuously evaluated and used to refine subsequent search queries. The computer-aided search engine analyzes returned citations, identifies false hits, and adjusts lexical associations accordingly, creating an iterative improvement process that simultaneously narrows search space and reduces false positives
Solution Approach 2:
The system dynamically changes search parameters including lexical associations, query weighting, and matching criteria based on accumulated search experience and feedback. By adjusting these parameters iteratively, the system optimizes both search efficiency and accuracy, resolving the contradiction between rapid narrowing and false hit elimination
2Ease of operation
If a pure textual search is used with lexical refinements, then the search process is simple, but the mapping between keywords and attributes is ambiguous
Solution Approach 1:
The system introduces an intermediary layer of semantic analysis and attribute mapping between the user's lexical keywords and the search database. This intermediary process automatically interprets keywords, infers intended attributes, and constructs refined search queries, maintaining simplicity for users while achieving precise mapping through computational semantics
Solution Approach 2:
The system replaces manual keyword-attribute mapping with automated computational processes including natural language processing, semantic analysis, and machine learning algorithms. This substitution maintains ease of operation by eliminating manual intervention while dramatically improving mapping precision through intelligent algorithms
3Measurement precision
If lexical search terms are refined through human-computer interactions, then the search becomes more specific, but out-of-scope citations still score hits
Solution Approach 1:
The system adds additional evaluation dimensions beyond simple keyword matching, including semantic relevance, contextual analysis, and attribute-based filtering. By evaluating search results across multiple dimensions simultaneously, the system maintains high specificity while filtering out out-of-scope citations that might match refined keywords but fail on other relevance criteria
Data Source
AI summary
One embodiment involves a method and apparatus for mapping lexical keywords into entity description semantics in order to create unambiguous buyer-confirmed descriptions of entities. The method described herein relies on a computer program and some mechanism for computer data storage.