Integrated Knowledge Graph Nodes for Processing Speed
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Solution Overview
Problem
Knowledge graphs face performance challenges, particularly in large-scale applications, due to lower-than-desired algorithm speeds when processing a large number of nodes and edges, despite existing hardware and software approaches.
Innovation Solution
Incorporating computing and communication capabilities into the knowledge graph, integrating storage, computing, and communication, and removing database-only restrictions, enabling each entity node to have processing and communication capabilities, including message passing, pooling, and learning functions, and using neuromorphic hardware for enhanced performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional knowledge graph structures are used with separate database and processing components, then data storage and query capabilities are maintained, but processing speed and algorithm performance deteriorate when handling large numbers of nodes and edges
Solution Approach 1:
The patent combines storage, computing, and communication capabilities into integrated entity nodes within the knowledge graph structure. Each entity node becomes a self-contained unit with embedded processing power, eliminating the need for separate database servers and application servers. This merging of functions directly addresses the performance bottleneck by enabling parallel processing across distributed nodes while maintaining data locality.
Solution Approach 2:
The knowledge graph is segmented into autonomous entity nodes that can independently process queries and perform computations. Each node is divided into functional components (storage unit, processing unit, communication unit) that operate semi-independently. This segmentation enables parallel execution of algorithms across multiple nodes, significantly improving processing speed for large-scale knowledge graphs.
2Productivity
If computing and communication capabilities are integrated into each entity node, then processing speed and parallelism improve, but device complexity and implementation difficulty increase
Solution Approach 1:
The entity node is designed as a universal multi-functional unit that can perform storage, processing, communication, and even machine learning operations. This standardized multi-functional design simplifies implementation by using the same basic building block throughout the system, rather than requiring specialized components for each function. The universal node architecture makes the system easier to manufacture and deploy at scale.
Solution Approach 2:
Entity nodes are equipped with self-service capabilities including autonomous query processing, local data storage, and direct peer-to-peer communication. Nodes can independently execute algorithms and make decisions without requiring centralized coordination, which reduces system complexity and eases implementation. The self-organizing nature of the distributed nodes automatically handles scaling and fault tolerance.
3Measurement precision
If machine learning algorithms are applied to learn relationship status between entities, then inference accuracy and knowledge discovery improve, but computational overhead and processing time increase
Solution Approach 1:
Machine learning models are pre-trained during an offline phase to learn relationship patterns and dependencies between entity types. The trained models are then embedded within entity nodes, enabling fast inference during online query processing. This preliminary action separates the computationally intensive training phase from the time-sensitive inference phase, achieving high accuracy without adding significant overhead to real-time operations.
Solution Approach 2:
The system applies machine learning selectively rather than uniformly across all entity pairs. ML algorithms are used to infer relationships for specific entity types or in specific contexts where the computational benefit outweighs the overhead. This partial application of ML maintains high inference accuracy for critical relationships while avoiding unnecessary computational waste for straightforward queries.
Data Source
AI summary
A computational knowledge graph comprises a plurality of entity nodes and one or more functional connections. Each of the plurality of entity nodes has an entity type, entity characteristics and storage, and processing and communication capability. Each of the one or more functional connections associates one of the plurality of entity nodes to another of the plurality of entity nodes. Each functional connection has a connectivity level parameter that is determined by at least one of: calculating connectivity by a sequence of message passing operations between the entities associated by the connection and ii) learning the connectivity level through inference. A method implements the computational knowledge graph.