Ontology Generation for Enhanced Search
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Solution Overview
Problem
Conventional search methods and software are inadequate for efficiently finding and identifying similar terms, synonyms, and alternate terms within a specific domain of knowledge, leading to missed data and labor-intensive processes, especially when dealing with large numbers of terms or complex relationships.
Innovation Solution
A system and method that load seed entities into a processor's memory, use a look-up API to search for similar terms from a web-based database, cluster these terms based on concepts, and merge clusters to generate an ontology database for integration into a search engine, facilitating the organization and categorization of concepts within a domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional search methods are used, then search operations are simple, but data retrieval completeness deteriorates
Solution Approach 1:
The patent introduces an ontology as an intermediary layer between the search engine and domain data. The ontology contains structured knowledge about domain concepts, relationships, and terminology, enabling the search engine to understand semantic relationships and retrieve relevant data that conventional keyword matching would miss.
Solution Approach 2:
The patent performs preliminary action by automatically constructing domain-specific ontologies before search operations. The system pre-processes domain data to identify concepts, relationships, and terminology, organizing them into structured ontologies that enhance subsequent search operations without affecting the simplicity of the search interface.
2Measurement precision
If manual ontology construction is performed, then ontology accuracy improves, but time consumption and labor intensity increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically construct ontologies without human intervention. The ontology construction system autonomously analyzes domain data, identifies concepts and relationships, and generates structured ontologies, eliminating the need for manual ontology engineering while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical manual process of ontology construction with an automated computational system. Instead of researchers manually analyzing and organizing domain knowledge, the system uses algorithms to automatically extract concepts, relationships, and terminology from domain data, substituting human labor with automated processing.
3Productivity
If domain-specific search capabilities are added, then search effectiveness improves, but system complexity increases
Solution Approach 1:
The patent implements universality by designing the ontology construction system to handle multiple domains through a unified framework. The same system architecture and algorithms can be applied to different domains (e.g., medicine, law, engineering) by simply changing the input domain data, avoiding the need for separate specialized systems for each domain.
Data Source
AI summary
A method of generating an ontology for enhanced search is disclosed that includes an iterative process. The method includes loading seed entities in a memory and using a look-up API to search a first set of similar terms for each seed entity from a web-based database. The first set of similar terms is then clustered based on different concepts to obtain a first cluster of similar terms. The method further includes determining a second set of similar terms for each seed entity based on a category of each seed entity. The second set of similar terms is then clustered based on the different concepts to obtain a second cluster of similar terms. The first and second clusters are merged to generate the ontology of the seed entities. The iterative process is repeated until a saturation point is reached, at which point the generated ontology is considered complete.