Relational Database Semantic Query Translation System

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Solution Overview

Problem

Existing approaches fail to seamlessly integrate legacy relational databases with the Semantic Web framework, particularly in transforming relational data into RDF and enabling SPARQL queries, leading to inconsistencies and inefficiencies.

Innovation Solution

A system and method that transforms relational databases' SQL schema into Semantic Web languages like RDF, RDFS, or OWL, creating a synthesized domain model, and translates SPARQL queries into equivalent SQL queries, utilizing a SQL query optimizer to execute queries efficiently without materializing RDF triples.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If relational databases are transformed into RDF format to enable Semantic Web integration, then compatibility with Semantic Web frameworks is improved, but system complexity and data transformation overhead increase

Engineering Contradiction:

ImproveSemantic Web compatibilityVSAvoidSystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces a mediator layer consisting of mapping files and query translation components that bridge relational databases and Semantic Web frameworks. Instead of fully transforming the database into RDF, the system uses mapping files to define correspondences between relational tables and RDF concepts, allowing SPARQL queries to be translated into SQL queries that execute directly on the relational database, thus achieving Semantic Web compatibility without complete format transformation

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system segments the integration task into distinct components: mapping file generation, query translation, and result formatting. The mapping process is divided into schema mapping and data mapping phases, allowing each component to be developed and optimized independently, reducing overall system complexity while maintaining Semantic Web interoperability

Inventive Principle:

Principle #1Segmentation

2Speed

If RDF triples are materialized to enable direct SPARQL querying, then query execution speed is improved, but memory consumption and storage requirements increase

Engineering Contradiction:

ImproveQuery execution speedVSAvoidMemory consumption

Core Design Contradiction:

SpeedVSQuantity of substance

Solution Approach 1:

Instead of translating SPARQL queries to execute on materialized RDF triples, the system inverts the approach by translating SPARQL queries into SQL queries that execute directly on the relational database. This eliminates the need to materialize RDF triples in memory, maintaining query execution efficiency while avoiding the memory overhead of storing triple representations

Inventive Principle:

Principle #13The other way round (Inversion)

Solution Approach 2:

The system creates a virtual copy of the RDF data model through mapping files that describe the correspondence between relational schema and RDF ontology. This virtual model allows SPARQL queries to be translated and executed on the original relational data without physically duplicating the data in RDF format, thus avoiding memory consumption while maintaining query capability

Inventive Principle:

Principle #26Copying

3Stability of the object's composition

If complete schema transformation to Semantic Web format is performed, then data model consistency is improved, but transformation time and processing overhead increase

Engineering Contradiction:

ImproveData model consistencyVSAvoidTransformation time

Core Design Contradiction:

Stability of the object's compositionVSLoss of time

Solution Approach 1:

The system performs preliminary schema mapping to generate mapping files that define the correspondence between relational schema and RDF ontology before data transformation. These mapping files are cached and reused for subsequent queries, ensuring data model consistency without repeating the transformation process, thus reducing transformation time while maintaining consistency

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Instead of performing complete schema transformation for every query, the system uses partial transformation by relying on pre-generated mapping files for commonly accessed data models. The mapping process is performed once during setup or when schema changes occur, rather than repeatedly for each query, significantly reducing processing overhead while maintaining data model consistency

Inventive Principle:

Principle #16Partial or excessive action

4Productivity

If SPARQL queries are translated to SQL queries, then execution efficiency on relational databases is improved, but query language flexibility is reduced

Engineering Contradiction:

ImproveQuery execution efficiencyVSAvoidQuery language flexibility

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system changes the parameter of query execution by translating SPARQL queries into SQL queries with equivalent semantics. The translation process preserves the logical structure and intent of the original SPARQL query while adapting it to SQL syntax and execution model, allowing efficient execution on relational databases without losing the flexibility of SPARQL query formulation

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11409802B2System for accessing a relational database using semantic queries

Publication Date: 2022.08.09 SERVICENOW INC

AI summary

The invention is a system for integrating data sets organized in one organization type with data sets organized in a second organization type so that data queries submitted to be processed in the manner of the first organization type can be translated into queries usable by the data set in the second data organization type and the results returned to satisfy the first query.