Graph Data Translation for Scalable Deductive Reasoning

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Solution Overview

Problem

Graph databases lack comprehensive support for programming logic languages that enable deductive reasoning, hindering the deduction of new information, especially in fields requiring semantic analysis, as they lack a system and method for transforming and using graph data into a standard schema suitable for such languages.

Innovation Solution

A system and method for analyzing graph databases using intelligent reasoning systems, involving a stream processing engine, translation service, and sharding service to transform graph data into relational data suitable for programming logic languages, enabling deductive reasoning with a semantic reasoner to deduce new information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If graph databases are used for data storage, then efficiency for handling large data sets with explicit relationships is improved, but support for programming logic languages enabling deductive reasoning deteriorates

Engineering Contradiction:

Improvehandling efficiency of large data setsVSAvoidsupport for programming logic languages

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system segments the data processing workflow into distinct components: a graph database component for efficient storage and relationship management, and a separate translation service component that converts graph data into relational fact tables suitable for logic programming languages. This segmentation allows each component to excel at its specific function while working together to achieve both high efficiency and broad language support.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The translation service acts as an intermediary between the graph database and programming logic languages. It receives graph data from the database, transforms it into a relational schema with fact tables, and outputs data in a format that logic programming languages can process for deductive reasoning. This intermediary layer enables compatibility between the two previously incompatible systems.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If graph data is transformed into relational data for deductive reasoning, then compatibility with programming logic languages is improved, but processing latency and congestion increase

Engineering Contradiction:

Improvecompatibility with programming logic languagesVSAvoidprocessing latency

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary transformation of graph data into relational fact tables in advance, before deductive reasoning queries are executed. The translation service pre-processes the graph data and stores it in a relational format that is optimized for logic programming operations, so that when queries arrive, the data is already in the appropriate format, reducing query processing latency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The translation process is designed to be dynamic and adaptive, adjusting the transformation strategy based on the specific characteristics of the graph data and the requirements of the target logic programming language. This allows the system to optimize processing time for different data types and query patterns, minimizing latency while maintaining compatibility.

Inventive Principle:

Principle #15Dynamics

3Adaptability or versatility

If comprehensive transformation of graph data is implemented, then deductive reasoning capability is improved, but system complexity increases

Engineering Contradiction:

Improvedeductive reasoning capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The translation service is designed as a universal component that can handle multiple types of graph data formats and transform them into a standardized relational schema suitable for various programming logic languages. This multi-functionality reduces the need for multiple specialized transformation tools, thereby managing system complexity while maintaining broad deductive reasoning capability across different data types and languages.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12536162B2System and method for analysis of graph databases using intelligent reasoning systems

Publication Date: 2026.01.27 QOMPLX INC

AI summary

A system for analyzing graph databases using intelligent reasoning systems including scalable collection of, and transformation of, graph data into facts suitable for use with programming logic languages doing deductive reasoning. A graph analyzer ingests disparate graph data from across the Internet and transforms the graph data into a fact table. In order to reduce latency and processing congestion, a stream processing engine and sharding strategy are employed to ensure scalability through parallelized processing of programming logic queries. Transformed graph data, now relational data, is utilized with programming logic languages that allow for hypothetical queries whereby an inference engine can deduce new information to satisfy such a query. Furthermore, the self-contained nature of inputs, outputs, and transformations of the system means strict data provenance can be observed and adhered to.