Knowledge Graph Virtual Edges for Dynamic Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing knowledge graph inference methods require constructing actual connecting edges when inference requirements change, leading to increased costs and wasteful data storage.
Innovation Solution
A knowledge graph-based inference method that allows users to define virtual edge generation rules, including node and relationship constraints, to determine virtual edges between nodes without actual connecting edges, enabling dynamic inference without relying on complete graph reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If actual connecting edges are constructed in the knowledge graph to meet inference requirements, then inference reliability is improved, but device complexity and storage costs increase
Solution Approach 1:
The patent creates virtual edges that copy the functional characteristics of actual connecting edges without physically constructing them in the knowledge graph. These virtual edges are defined by generation rules that replicate the connectivity patterns needed for inference, allowing the system to achieve inference reliability while avoiding the complexity of constructing and storing complete graph data structures.
2Adaptability or versatility
If actual connecting edges are constructed when inference requirements change, then adaptability to new inference needs is improved, but loss of time and increased storage costs occur
Solution Approach 1:
The patent implements dynamic virtual edge generation where edges are created on-demand based on inference requirements rather than being statically constructed. The virtual edge generation rules allow the system to adapt to changing inference needs by dynamically determining which virtual edges to create, eliminating the time-consuming process of reconstructing the entire knowledge graph when requirements change.
Solution Approach 2:
The patent pre-defines virtual edge generation rules that encode the logic for creating edges based on node attributes and relationships. These rules are prepared in advance and can be quickly applied when inference requirements change, avoiding the need to perform complex graph construction operations at inference time.
3Reliability
If complete knowledge graph data is constructed and stored, then inference completeness is improved, but memory space consumption increases
Solution Approach 1:
The patent extracts only the essential connectivity logic from complete graph structures and embeds it in virtual edge generation rules. Instead of storing and processing complete graph data with all possible edges, the system stores compact generation rules that can produce virtual edges as needed, significantly reducing memory space consumption while maintaining inference completeness.
Data Source
AI summary
A knowledge graph-based inference method includes: receiving an inference request from a user equipment, where the inference request includes a user-defined virtual edge generation rule, and the generation rule includes node constraints on a start node and an end node of a virtual edge, and a relationship constraint on a relationship between the start node and the end node; determining, based on the generation rule, the virtual edge between a first node and a second node that do not have an actual connecting edge in the knowledge graph; and performing, based on the virtual edge, graph inference specified in the inference request.