Syntactic Tagging for Multi-Dimensional Data Classification
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Solution Overview
Problem
Existing classification systems, such as those based on the Dewey decimal system, are one-dimensional and lack rules-based approaches, limiting the structured and unstructured data's usability and analysis capabilities.
Innovation Solution
Implementing a domain-specific syntax with syntactic tagging, which involves establishing rules to manage and relate data entities within a domain, enabling the creation of domain-specific coordinate systems and allowing for the identification and contextualization of data entities through syntactic tags.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional one-dimensional classification systems (e.g., Dewey decimal system) are used, then simplicity and ease of implementation are maintained, but data dimensionality and analytical capability are limited
Solution Approach 1:
The patent applies dimensionality change by transforming traditional one-dimensional classification into multi-dimensional syntactic tagging. Each data entity is tagged with multiple syntactic tags representing different dimensions (e.g., subject, object, action, attribute), enabling comprehensive analysis across multiple axes simultaneously while maintaining systematic organization.
Solution Approach 2:
The classification system is segmented into distinct syntactic components (tags) that can be independently assigned to data entities. Each syntactic tag represents a specific dimensional attribute, allowing the system to break down complex classification into manageable, reusable tagging units that can be combined in various configurations.
2Productivity
If rules-based domain-specific syntax is implemented, then data analysis and comparison capability are enhanced, but system complexity and implementation difficulty increase
Solution Approach 1:
The syntactic tag system is designed as a universal framework applicable across multiple domains. The same syntactic tagging mechanism can classify data entities in different domains (e.g., biological, mechanical, organizational) by defining domain-specific syntax rules, eliminating the need for separate classification systems for each domain while maintaining high analysis efficiency.
Solution Approach 2:
The system enables efficient data analysis by changing the parameter representation from traditional categorical classification to structured syntactic parameters. Each syntactic tag introduces specific parameters (e.g., subject type, object type, relationship type) that can be systematically queried, filtered, and analyzed, transforming unstructured data into parameterized information suitable for efficient computation.
3Adaptability or versatility
If syntactic tagging is applied to unstructured data, then data structuration and usability are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary syntactic tagging during data ingestion or preprocessing stages, establishing the structured syntax framework before analytical operations. By pre-applying syntactic tags and organizing data according to domain-specific syntax rules upfront, the system eliminates the need for repeated structuring operations during subsequent analysis, reducing overall processing time despite the initial tagging investment.
Data Source
AI summary
This application relates generally to defining a domain-specific syntax characterizing a functional information system and performing operations on data entities represented by the domain-specific syntax, including defining a domain-specific syntax, receiving and storing a domain-specific data entity, assigning a syntactic tag to the domain-specific data entity, and electronically storing the tag assigned to the data entity in the electronic data store so that the tag is logically linked to the stored data entity.