Semantic Data Modeling With Relational Phrases for Simpler Queries
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Solution Overview
Problem
Existing systems struggle to effectively represent and manipulate domain-specific knowledge in a manner that preserves context and meaning, particularly in the form of RelationalPhrases, which are essential for programmatic exploration and interoperability between disparate information systems.
Innovation Solution
A semantic data storage system utilizing a semantic database model that includes SentenceClassLabels and SentenceClassData tables, allowing for the storage and manipulation of RelationalPhrases, enabling programmatic instantiation and formation of structured documents, and supporting workflows for data acquisition, transformation, and integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored in traditional relational models, then data storage is efficient, but join operations are required to extract relations which increases query complexity
Solution Approach 1:
The patent pre-computes and stores relational information directly alongside entity data in the database schema. Relations are established during data insertion rather than being computed during queries, eliminating the need for complex join operations and improving query efficiency.
Solution Approach 2:
The patent introduces a new dimensional structure to traditional relational databases by adding relation storage capabilities at the entity level. This allows the system to store not only entity attributes but also their relationships in a unified structure, transforming the query operation from multi-table joins to single-table retrievals.
2Loss of information
If semantic data is stored with contextual relations, then meaning and context are preserved, but data storage complexity increases
Solution Approach 1:
The patent merges entity data and relational data into a unified storage structure. Instead of separating facts into different tables requiring joins, the system combines entity attributes and their relationships into a single integrated model, preserving semantic context while simplifying the data access operation.
Solution Approach 2:
The patent introduces an intermediary relational layer that connects entities with their contextual information. This intermediary structure stores relations as first-class citizens in the database, allowing semantic meaning to be preserved without requiring complex multi-table relationships.
3Reliability
If relations are stored separately from entities, then data normalization is achieved, but the need for join operations increases query time
Solution Approach 1:
The patent pre-establishes relational connections during data insertion by storing relation identifiers alongside entity data. This preliminary action ensures data integrity through proper relational linking while eliminating the need for time-consuming join operations during query execution.
Data Source
AI summary
The invention relates to a semantic data acquisition, storage and presentation system comprising SentenceClasses wherein SentenceClasses comprise SentenceClassLabels and SentenceClassData, each such label in the form of a RelationalPhrase or an alias thereof, and data, for each class, organized in conformance with the class-defining relation, as expressed in the form of the RelationalPhrase. Instantiation of SentenceClasses, by replacing substitution variables in RelationalPhrases with associated items of SentenceClassData, produces Sentences that provides context to the instantiated items. The system of the invention also comprises methods and processes including for: acquiring, transforming and recording data; operating on SentenceClasses including forming new SentenceClasses; and instantiating, rendering and presenting Sentences in human or machine-readable format.