SentenceClass Semantic Graph Modeling for Context-Preserving 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 semantic interoperability through SQL or Datalog queries, and supporting workflows for data acquisition, transformation, and document creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If domain-specific knowledge is stored in traditional relational models, then data can be stored efficiently, but join operations are required to extract relations which complicates queries and reduces efficiency
Solution Approach 1:
The patent segments the relational data model into two distinct components: SentenceClassLabels (containing relation templates) and SentenceClassData (containing instance data). This segmentation allows relations to be stored explicitly as first-class citizens rather than requiring join operations to extract them, thereby improving query efficiency while maintaining data organization.
Solution Approach 2:
The patent introduces SentenceClassLabels as an intermediary layer between data storage and relation extraction. These labels serve as templates that define the structure and meaning of relations, allowing the system to store and query relations directly without requiring complex join operations across multiple tables.
2Loss of information
If RelationalPhrases are used to convey domain-specific knowledge, then context and meaning are preserved, but the system complexity increases to handle template instantiation and manipulation
Solution Approach 1:
The patent applies preliminary action by pre-defining RelationPhrase templates in the SentenceClassLabels table with their structure, variables, and semantic meaning established in advance. This allows the system to store contextual knowledge efficiently and instantiate specific relations by simply binding values to predefined templates, reducing the complexity of on-the-fly template creation and manipulation.
3Adaptability or versatility
If a semantic data storage system is implemented to preserve context and meaning, then interoperability between disparate information systems is improved, but the implementation complexity and resource requirements increase
Solution Approach 1:
The patent implements a universal data model where SentenceClassLabels serve multiple functions: they define relation templates, store domain knowledge, enable type checking, and facilitate query generation. This multi-functionality allows the same structural framework to handle diverse domain-specific knowledge representations, improving interoperability without proportionally increasing implementation complexity.
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.