Concept Relationship Discovery in Online Service Databases
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Solution Overview
Problem
Current database analysis methods for discovering relationships between concepts are limited to specific databases and languages, requiring substantial manual intervention for updates and being difficult to transfer across languages, thus failing to provide accurate and scalable recommendations for user interaction.
Innovation Solution
A system that analyzes a database by counting concept hierarchies, classifying categories based on significance using page rank, and determining the most significant concepts across multiple levels, enabling the identification of related concepts and categories through a modular approach that includes disambiguation, similarity computation, and category ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional database analysis methods are used to discover relationships between concepts, then analysis can be performed for specific databases, but the methods require substantial manual intervention and are difficult to transfer across languages
Solution Approach 1:
The system automatically processes multilingual databases to discover concept relationships without requiring manual intervention. The automated pipeline includes data extraction, translation, concept identification, and relationship discovery components that work together to analyze concepts across multiple languages and databases independently
Solution Approach 2:
The system is designed to handle multiple languages and different database types universally. By implementing language translation components and multi-language concept identification, the same system architecture can process databases in different languages and domains without requiring language-specific customization
2Measurement precision
If case-by-case database analysis is performed for specific databases, then relationship discovery is possible for that database, but the application cannot be transferred to other languages or databases
Solution Approach 1:
Language translation serves as an intermediary component that enables the system to process concepts across different languages. The translation module converts concepts from various languages into a unified representation, allowing the relationship discovery algorithms to work universally across multilingual databases without requiring separate systems for each language
Solution Approach 2:
The system segments the database analysis process into independent modular components: data extraction, translation, concept identification, and relationship discovery. This segmentation allows each component to be independently optimized and reused across different languages and database types, enhancing the system's adaptability and versatility
3Reliability
If manual updates are performed for database resources, then concept relationships can be maintained, but substantial time and effort are required
Solution Approach 1:
The system implements continuous automated processing that regularly updates concept relationships in databases without requiring manual intervention. The automated pipeline continuously extracts, translates, analyzes, and updates concept relationships, ensuring the database remains current and accurate without incurring the time costs of manual updates
Data Source
AI summary
A system and method is provided to analyze a database of concepts organized into categories, wherein each concept is an online textual document, to determine a numerical relationship between the concepts and to determine a hierarchy for the categories.