Unified Knowledge Base for Data Tag Standardization

The patent introduces a knowledge base as an intermediary layer between raw data tags and unified knowledge representation. The knowledge base receives tags from multiple data sources, standardizes them through mapping to controlled vocabularies, and outputs consistent knowledge representations. This mediator resolves the contradiction by preserving the richness of original tags while enforcing consistency through standardized mapping relationships.

The patent transforms tag parameters by mapping them to standardized categories and types. Instead of using raw tags directly, the system changes the parameters of tags by assigning them to standardized vocabularies (e.g., mapping different movie title formats to a unified title category). This parameter transformation enables both data enrichment from diverse sources and consistency through standardized representations.

The patent segments the tag categorization task into discrete, manageable components: tag parsing, category mapping, relationship inference, and knowledge base updates. Each component handles a specific aspect of the complexity, making the overall system more manageable. The segmentation allows independent optimization of each module while maintaining unified knowledge representation.

The system implements self-service through automatic tag categorization and relationship deduction. Instead of requiring manual annotation for each tag, the system automatically learns category mappings and relationships from the data itself. This self-service mechanism reduces the need for complex manual configuration while maintaining high unification precision through automated pattern recognition.

The patent performs preliminary action by pre-establishing category vocabularies and mapping templates before processing actual tags. These pre-defined structures enable rapid automatic mapping without requiring real-time decision-making. The preliminary configuration of categorization rules allows the system to process tags at high speed while maintaining consistency through the pre-established frameworks.

The patent replaces manual mechanical tag mapping with automated computational processes. Instead of human operators manually mapping tags, the system uses algorithmic pattern recognition, statistical modeling, and logical inference to automatically categorize tags. This substitution maintains the precision of manual mapping while achieving much higher processing speeds and automation levels.

The patent applies partial action by focusing relationship deduction on specific, high-value object pairs and relationships rather than attempting to analyze all possible relationships. The system identifies and processes only the most relevant relationships based on pre-defined criteria and business logic, reducing computational complexity while maintaining effective data integration for the most important use cases.

The patent implements nesting by organizing relationship deduction within the existing knowledge base structure. Relationship inference is nested within the category mapping process, and both are nested within the tag processing pipeline. This nested architecture allows relationship deduction to leverage existing computational resources and data structures, reducing overall computational complexity while enhancing data integration capabilities.