Knowledge Graph Perspective Generation with Lazy Parallel Transformations
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Solution Overview
Problem
Existing graph database and analytics tools lack computational speedups, scalability, and visual feedback, limiting their effectiveness in managing and analyzing large-scale heterogeneous graph data.
Innovation Solution
A method and system for generating knowledge graph perspectives using metagraph transformations that are lazily evaluated and executed with parallel matrix-linear algebra operations, reducing complexity and run-time through property graph transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing graph database tools (Neo4j, TigerGraph) are used, then basic graph storage and querying are supported, but computational speed and scalability for large-scale analytics are limited
Solution Approach 1:
The patent replaces traditional graph traversal algorithms with linear algebra operations (matrix multiplication, eigenvalue decomposition) to perform graph analytics. This substitution enables parallel computation using standard high-performance linear algebra libraries, dramatically improving computational speed for large-scale graph processing while maintaining manageable system complexity.
2Ease of operation
If comprehensive graph analysis tools are used, then analytical capabilities are improved, but visual feedback and intuitive manipulation capabilities are lacking
Solution Approach 1:
The patent implements visual feedback mechanisms that display intermediate results and graph transformations in real-time during analytics execution. This allows users to monitor progress, adjust parameters, and understand complex graph relationships intuitively, improving ease of operation without sacrificing analysis efficiency through the underlying parallel computation engine.
3Adaptability or versatility
If graph transformations and projections are performed, then graph structure modification capabilities are improved, but lack of lazy evaluation support increases computational burden
Solution Approach 1:
The patent implements lazy evaluation for graph transformations and projections, where transformation plans are prepared and optimized in advance but actual computation is deferred until results are needed. This allows complex graph transformations to be composed and manipulated symbolically without immediate computational overhead, reducing the computational burden while maintaining high versatility.
Data Source
AI summary
There is disclosed a method for generating perspective(s) (300) of a knowledge graph (302). The method comprises receiving the knowledge graph from a database, wherein the knowledge graph comprises vertices at least partially connected together by edges; generating a property graph (202) of the knowledge graph, wherein the property graph comprises vertices (204A-C) at least partially connected together by edges (206A-D); creating metagraph transformation(s) to be executed on the property graph for generating perspective(s) of knowledge graph, wherein each of metagraph transformation(s) is lazily evaluated on property graph; adjusting metagraph transformation(s) based on lazy evaluation of the metagraph transformation(s) on the property graph; executing metagraph transformation(s) on the property graph, based on execution of matrix-linear algebra operations parallelly for each of metagraph transformation(s), on device(s); and generating multigraph(s) for depicting perspective(s) of the knowledge graph based on a corresponding transformed property graph (200) generated from execution of each of metagraph transformation(s) on property graph.