Triplestore Semantic Layer for Relational Database Context
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Solution Overview
Problem
Relational databases lack inherent semantic context for querying and data representation, making it difficult to perform efficient semantic searching and data manipulation.
Innovation Solution
The use of a triplestore, in conjunction with an ontology, allows for the translation of data from a relational database into a semantic format, enabling semantic searching and data manipulation through techniques like R2RML mapping and SPARQL queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is stored in a relational database, then data can be organized in tables with primary keys, but the data lacks inherent semantic context for querying and manipulation
Solution Approach 1:
The patent introduces a triplestore database as an intermediary layer between the relational database and the querying system. The triplestore translates relational data into semantic triples (subject-predicate-object) that preserve semantic context, enabling semantic searching and manipulation without altering the underlying relational database structure.
Solution Approach 2:
The patent changes the data representation parameter from traditional relational tables to semantic triples. This transformation allows the same data to be queried using semantic contexts and relationships, fundamentally changing how data can be accessed and manipulated while maintaining the original data storage.
2Ease of operation
If traditional relational databases are used, then data storage is efficient, but semantic searching and data manipulation become difficult
Solution Approach 1:
The triplestore acts as a mediator that enables semantic searching and manipulation operations. It provides a semantic layer that allows users to query data based on meaning and relationships rather than just table structures, while the underlying relational database maintains efficient storage.
Solution Approach 2:
The patent segments the data access function from the data storage function. The relational database handles efficient storage, while the triplestore handles semantic querying and manipulation. This segmentation allows each component to optimize for its specific purpose without compromising the other.
3Ease of operation
If data is translated into semantic format using triplestore, then semantic searching and manipulation are enabled, but system complexity increases
Solution Approach 1:
The triplestore serves as an intermediary that manages the complexity of semantic data translation. It handles the conversion between relational data and semantic triples, providing a unified interface for semantic operations while abstracting the underlying complexity from the user.
Solution Approach 2:
The triplestore provides multiple functions within a single system component: it stores semantic triples, performs semantic querying, enables data manipulation, and interfaces with the relational database. This multi-functionality consolidates complexity into a single manageable component rather than requiring separate systems for each function.
Data Source
AI summary
A method may include receiving data from a computing device requesting approval of a loan application; accessing time-series data associated with the user from a knowledge graph; building a feature vector based on the accessed time-series data; inputting the feature vector into a machine learning model; receiving a response from the output from the machine learning mode, the output indicating a level of approval for the user with respect to the loan application; and transmitting a response to the request based on the level of approval.