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Edge Knowledge Graph
by
KingOfZhao
· GitHub ↗
· v1.0.0
· MIT-0
210
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0
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1
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Install in OpenClaw
/install edge-knowledge-graph
Description
Build and query focused knowledge graph subgraphs locally on resource-limited edge devices for low-latency, offline-aware reasoning and incremental updates.
README (SKILL.md)
Edge Knowledge Graph
边缘计算 × 知识图谱：在资源受限的边缘设备上构建和查询知识图谱。
何时使用
- IoT设备需要本地知识推理（断网场景）
- 移动端实时知识查询（低延迟需求）
- 边缘节点的轻量级知识管理
- 端侧个性化知识图谱
核心认知
1. 边缘知识图谱的核心矛盾
知识图谱的价值与规模成正比，但边缘设备的资源（内存、算力、存储）极其有限。解决方案不是"缩放"而是"投影"——将云端的完整知识图谱投影为边缘场景所需的子图。
投影原则：
- 场景驱动：只保留当前任务所需的三跳以内的实体和关系
- 频率加权：高频查询路径保留更多细节，低频路径只保留骨架
- 压缩存储：用整数ID替代URI，用位图索引替代B+树
2. 增量同步协议
边缘KG不能全量同步，需要增量更新：
- Change Vector：每个边缘节点维护一个变更向量（类似CRDT的version vector）
- Diff-Patch协议：云端计算出变更差异（Delta），只传输Delta
- 冲突解决：边缘离线期间的本地修改与云端更新冲突时，按时间戳+优先级合并
- 压缩传输：Delta用RDF diff格式 + gzip压缩，典型压缩比 > 90%
3. 端侧推理优化
在边缘设备上执行SPARQL查询太重。替代方案：
- 预计算路径：云端预计算高频查询路径，边缘只做路径匹配
- 规则缓存：OWL推理规则编译为简单的if-then规则，边缘执行
- 近似查询：允许一定误差的近似子图匹配，换取消耗降低
碰撞来源
edge-computing×knowledge-graph-builder×knowledge-graph-query-optimizercrdt-conflict-resolution（增量同步）×edge-ai-deployment（部署模式）
Usage Guidance
This skill is a high-level design/instruction document (no code, no installs, no credentials requested), and appears internally consistent with its stated goal. Still consider: (1) provenance — source and homepage are missing, so if you later install an implementation based on these instructions, audit that code and packages before running on devices; (2) testing — try implementations in a sandbox or isolated edge device before deploying to production; (3) least privilege — never supply cloud credentials or device admin credentials unless a concrete implementation legitimately requires them and you’ve reviewed that code. If you want a runnable implementation, ask the author for source (repo, releases) and a clear install plan so you can review packages and update mechanics.
Capability Analysis
Type: OpenClaw Skill
Name: edge-knowledge-graph
Version: 1.0.0
The skill bundle consists of metadata and conceptual documentation (SKILL.md) regarding the implementation of knowledge graphs on edge devices. It contains no executable code, scripts, or instructions that attempt to exfiltrate data, gain unauthorized access, or perform malicious actions. The content is purely educational and theoretical in nature.
Capability Assessment
Purpose & Capability
Name and description (edge knowledge-graph subgraphs for resource-limited devices) match the SKILL.md content: projection, incremental sync, lightweight inference. The doc does not request unrelated binaries, credentials, or config paths.
Instruction Scope
SKILL.md contains high-level design guidance (projection rules, diff/patch sync, conflict resolution, inference optimizations) and does not instruct the agent to read local files, access environment variables, execute binaries, or transmit data to external endpoints. There is no scope creep beyond the stated purpose.
Install Mechanism
There is no install spec and no code files — the skill is instruction-only, so nothing will be written to disk or downloaded during install.
Credentials
The skill declares no required environment variables, credentials, or config paths. That is proportional for a design/instruction document that does not perform runtime network or privileged operations.
Persistence & Privilege
The skill does not request always: true and does not indicate any mechanism for persistent or elevated privileges. Autonomous invocation remains possible (platform default) but the instructions themselves do not escalate privilege.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install edge-knowledge-graph - After installation, invoke the skill by name or use
/edge-knowledge-graph - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Edge Knowledge Graph 1.0.0
- Initial release with design principles for deploying knowledge graphs on resource-constrained edge devices.
- Introduces a "projection" strategy to generate scenario-specific subgraphs from cloud knowledge graphs.
- Details an incremental sync protocol using change vectors, diff/patches, and conflict resolution.
- Describes optimizations for query and inference on the edge, including path precomputation, rule caching, and approximate matching.
- Targets scenarios like IoT offline reasoning, mobile real-time queries, and edge-specific lightweight knowledge management.
Metadata
Frequently Asked Questions
What is Edge Knowledge Graph?
Build and query focused knowledge graph subgraphs locally on resource-limited edge devices for low-latency, offline-aware reasoning and incremental updates. It is an AI Agent Skill for Claude Code / OpenClaw, with 210 downloads so far.
How do I install Edge Knowledge Graph?
Run "/install edge-knowledge-graph" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Edge Knowledge Graph free?
Yes, Edge Knowledge Graph is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Edge Knowledge Graph support?
Edge Knowledge Graph is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Edge Knowledge Graph?
It is built and maintained by KingOfZhao (@kingofzhao); the current version is v1.0.0.
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