A specialized framework for projecting, synchronizing, and querying high-value knowledge subgraphs on resource-constrained edge and IoT devices.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install edge-knowledge-graph
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install edge-knowledge-graph using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Edge Knowledge Graph?
Edge Knowledge Graph is a sophisticated solution designed to bridge the gap between massive cloud-based knowledge bases and the limited hardware of edge devices. Rather than attempting to scale down entire datasets, this Openclaw Skills module uses a projection approach to extract task-specific subgraphs tailored for local environments. It ensures that critical reasoning and data relationships remain accessible even in offline or low-latency scenarios.
By focusing on scenario-driven projection and compressed storage, the skill enables sophisticated AI reasoning on IoT hardware and mobile devices without the overhead of traditional graph databases. It balances the trade-off between knowledge depth and hardware constraints, ensuring that only the most relevant entities and relations are maintained locally.
Edge Knowledge Graph Use Cases
- IoT devices requiring local knowledge reasoning during network outages.
- Mobile applications needing real-time knowledge queries with ultra-low latency.
- Lightweight knowledge management for distributed edge nodes.
- Personalized on-device knowledge graphs for enhanced user privacy and speed.
How Edge Knowledge Graph Works
- Scenario-Driven Projection: The system analyzes current tasks to identify essential entities and relations within a three-hop radius from the cloud graph.
- Frequency-Based Weighting: High-frequency query paths are preserved in detail, while low-frequency paths are abstracted to core skeletons.
- Compressed Storage: URIs are replaced with integer IDs and B+ trees are swapped for bitmap indexing to minimize memory footprint.
- Incremental Synchronization: The Edge Knowledge Graph uses a Change Vector protocol to sync only delta updates between the cloud and edge.
- Optimized Inference: Complex SPARQL queries are replaced with pre-calculated paths and compiled if-then rules for local execution.
Edge Knowledge Graph Setup
To integrate the Edge Knowledge Graph into your project, follow these steps:
# Install the Openclaw Skills edge-kg module
npm install @openclaw/skill-edge-kg
# Initialize the local projection engine
openclaw edge-kg init --source [cloud-endpoint]
# Configure the sync interval and projection depth
openclaw edge-kg config --depth 3 --sync-delta true
Edge Knowledge Graph Data Schema & Taxonomy
The Edge Knowledge Graph organizes data using a highly optimized schema for limited environments:
| Component | Format | Optimization Method |
|---|---|---|
| Entities | Integer ID Mapping | Replaces long URIs to save RAM |
| Relations | Bitmap Indexing | Fast bitwise operations instead of complex joins |
| Updates | RDF Diff (Gzip) | Achieves >90% compression for delta transmissions |
| Inference | Compiled Rules | OWL rules converted to simple if-then logic |
Edge Knowledge Graph Advanced Features
- Conflict Resolution: Merges local offline modifications with cloud updates using timestamp and priority-based logic.
- Diff-Patch Protocol: Minimizes bandwidth by calculating and transmitting only the Delta (diff) of the graph.
- Approximate Querying: Supports fuzzy subgraph matching to reduce computational load on low-power CPUs.
- CRDT Integration: Utilizes version vectors to ensure eventual consistency across distributed edge nodes.
SKILL.md
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