AI Skill Hub 推荐使用:本体知识图谱编辑器 是一款优质的AI工具。AI 综合评分 7.8 分,在同类工具中表现稳健。如果你正在寻找可靠的AI工具解决方案,这是一个值得深入了解的选择。
基于浏览器的RDF/本体知识图谱可视化编辑工具。支持从文件、URL加载RDF数据,提供图形化编辑界面。适合语义网、链接数据、知识图谱研究者和开发者使用。
本体知识图谱编辑器 是一款基于 TypeScript 开发的开源工具,专注于 知识图谱、RDF编辑、语义网 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
基于浏览器的RDF/本体知识图谱可视化编辑工具。支持从文件、URL加载RDF数据,提供图形化编辑界面。适合语义网、链接数据、知识图谱研究者和开发者使用。
本体知识图谱编辑器 是一款基于 TypeScript 开发的开源工具,专注于 知识图谱、RDF编辑、语义网 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
# 方式一:npm 全局安装 npm install -g ontosphere # 方式二:npx 直接运行(无需安装) npx ontosphere --help # 方式三:项目依赖安装 npm install ontosphere # 方式四:从源码运行 git clone https://github.com/ThHanke/ontosphere cd ontosphere npm install npm start
# 命令行使用
ontosphere --help
# 基本用法
ontosphere [options] <input>
# Node.js 代码中使用
const ontosphere = require('ontosphere');
const result = await ontosphere.run(options);
console.log(result);
# ontosphere 配置说明 # 查看配置选项 ontosphere --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量(覆盖配置文件) export ONTOSPHERE_CONFIG="/path/to/config.yml"
| I want to… | Start here |
|---|---|
| Try the live demo | [Open Ontosphere ↗](https://thhanke.github.io/ontosphere) |
| Connect an AI agent | [AI / MCP Integration](#ai--mcp-integration) |
| Run it locally | [Quick start (development)](#quick-start-development) |
| Load my own data | [Startup / URL parameters](#startup--url-parameters) |
| Contribute code | [Contributing](#contributing--development-notes) |
Ontosphere is a browser-based RDF/ontology knowledge graph editor. It loads RDF from local files, remote URLs, or SPARQL/Fuseki endpoints; lets users author nodes and edges directly on the canvas; runs OWL 2 DL reasoning (via Konclude) with visual differentiation of inferred triples; and applies multi-algorithm layout (Dagre, ELK) and automatic clustering for large graphs. Additional features include namespace management with live URI renaming, a drag-and-drop workflow template catalog, and a Model Context Protocol (MCP) server for AI-agent integration. All computation runs entirely client-side in the browser against an in-memory RDF store backed by Web Workers — no backend required.
navigator.modelContext API) for AI-agent integration. Tools: loadRdf, loadOntology, suggestOntologiesForTask, queryGraph, exportGraph, exportImage, addNode, removeNode, expandNode, getNodes, addLink, removeLink, getLinks, runLayout, clusterNodes, layoutNodes, focusNode, fitCanvas, runReasoning, clearInferred, getNeighbors, findPath, getNodeDetails, updateNode, getGraphState, setNamespace, removeNamespace, listNamespaces, loadShacl, validateGraph, getCapabilities, help. MCP manifest at /.well-known/mcp.json.navigator.modelContext does not exist in headless Chromium. Inject the polyfill before the page loads using page.addInitScript:
await page.addInitScript(() => {
const tools = {};
Object.defineProperty(navigator, 'modelContext', {
value: { registerTool: async (n, _d, _s, h) => { tools[n] = h; } },
configurable: true,
});
window.__mcpTools = tools;
});
// After page load:
await page.evaluate(async () => {
const mod = await import('/src/mcp/ontosphereMcpServer.ts');
await mod.registerMcpTools();
});
// Call a tool:
await page.evaluate(async ([name, params]) => window.__mcpTools[name](params),
['addNode', { iri: 'ex:alice', typeIri: 'foaf:Person', label: 'Alice' }]);
In a browser with native navigator.modelContext, tools register automatically on app load.
1. Install dependencies:
npm install
2. Start the Vite dev server: npm run dev
3. Open in your browser: http://localhost:8080/
http://docker-dev.iwm.fraunhofer.de:8080/
?rdfUrl=https://docker-dev.iwm.fraunhofer.de/dataset/<uuid>/fuseki/$/sparql
&apiKey=<ckan-api-jwt-token>
An AI agent built this from scratch in one session — full demo with tool calls →
The reasoning demo showcases OWL 2 DL / SROIQ(D) inference on a small employee ontology: Open demo ↗
The demo (public/reasoning-demo.ttl) defines a Person → Employee → Manager → Executive hierarchy with ABox assertions that drive inference patterns across all OWL 2 DL construct groups:
OWL 1 RL patterns: 1. rdfs:subPropertyOf — ex:hasFriend sub-property of ex:knows: alice hasFriend bob → alice knows bob. 2. owl:inverseOf — ex:isManagedBy inverse of ex:manages: alice manages carol → carol isManagedBy alice. 3. owl:SymmetricProperty — ex:isColleagueOf is symmetric: bob isColleagueOf carol → reverse direction. 4. owl:TransitiveProperty — ex:hasSupervisor is transitive: bob→alice, alice→dave → bob→dave. 5. rdfs:domain — ex:dave has no type; because he is subject of ex:manages (domain ex:Manager), the reasoner infers dave rdf:type ex:Manager.
OWL 2 DL extensions: 6. owl:someValuesFrom — alice and carol each worksOn projectAlpha (a Project) → inferred ProjectContributor. 7. owl:hasValue — carol isManagedBy alice (via inverseOf) → carol inferred DirectReport (hasValue restriction on alice). 8. owl:intersectionOf — dave manages bob (inferred Manager) and eve (Employee) → dave inferred TeamLead. 9. owl:disjointWith — Contractor disjointWith Employee; frank is a Contractor (structural TBox constraint). 10. owl:complementOf — NonEmployee ≡ ¬Employee (structural TBox only). 11. owl:propertyChainAxiom — hasGrandManager ← hasSupervisor ∘ hasSupervisor: carol→bob→alice → carol hasGrandManager alice. 12. owl:unionOf — LeadershipTeam ≡ Executive ∪ Manager: alice (Executive) and dave (inferred Manager) → inferred LeadershipTeam. 13. owl:sameAs — aliceCEO sameAs alice: aliceCEO inherits all of alice's inferred types including Executive.
A separate inconsistency demo (public/reasoning-demo-inconsistent.ttl) shows the consistency checker in action: Open inconsistency demo ↗
inc:frank is asserted as both inc:Employee and inc:Contractor, which are declared owl:disjointWith. Running reasoning produces isConsistent: false, reasoning is skipped, and the report's Errors tab shows the disjointness clash on frank.
| Demo | Final state |
|---|---|
| **[FOAF social network](docs/mcp-demo/foaf-social-network.md)**<br>Build a social network, extend FOAF with employment classes, run reasoning | [](docs/mcp-demo/foaf-social-network.md) |
| **[DL reasoning (Konclude)](docs/mcp-demo/reasoning-demo.md)**<br>Build TBox + ABox, infer types via domain/range and transitivity | [](docs/mcp-demo/reasoning-demo.md) |
| **[Scene ontology](docs/mcp-demo/scene-ontology.md)**<br>Load an external ontology, author individuals, export Turtle | [](docs/mcp-demo/scene-ontology.md) |
| **[Manchester Pizza Tutorial](docs/mcp-demo/pizza-tutorial.md)**<br>Full OWL pizza ontology — classes, disjointness, properties, DL reasoning | [](docs/mcp-demo/pizza-tutorial.md) |
Regenerate:
```sh npm run demo:all
See docs/demo-scripts/HOWTO.md for the full guide.
Two styles of demo video are supported:
Seed-driven — write a seed markdown file in docs/mcp-demo/seeds/ with JSON-RPC tool calls embedded in backtick blocks. The runner parses the seed and executes each turn directly against window.__mcpTools. Existing seeds: foaf-social-network, reasoning-demo, scene-ontology, pizza-tutorial.
Chat-style (side-by-side) — open demo-stage.html (mock chat left, app right), inject messages programmatically via addChatMessage(), and call tools on the app iframe via callToolOnStage(). No relay popup needed. Example: pizza-tutorial-chat.
| Video | Description |
|---|---|
| [advert-intro.mp4](docs/demo-videos/advert-intro.mp4) | Relay bookmarklet — mock chat + Ontosphere side by side |
| [foaf-social-network.mp4](docs/demo-videos/foaf-social-network.mp4) | AI builds a FOAF social graph with DL reasoning |
| [reasoning-demo.mp4](docs/demo-videos/reasoning-demo.mp4) | AI builds an OWL ontology and runs DL reasoning (Konclude) |
| [scene-ontology.mp4](docs/demo-videos/scene-ontology.mp4) | AI builds a film scene ontology on BFO/RO upper ontology |
| [pizza-tutorial.mp4](docs/demo-videos/pizza-tutorial.mp4) | Manchester Pizza Ontology — class hierarchy, disjointness, DL reasoning |
| [pizza-tutorial-chat.mp4](docs/demo-videos/pizza-tutorial-chat.mp4) | OWL pizza tutorial as AI tutor lesson, side-by-side chat |
To re-record all videos:
npm run demo:video # starts dev server, records, encodes, kills server
| Parameter | Default | Description |
|---|---|---|
apiKey | — | Value sent as an authentication header with the RDF fetch. |
apiKeyHeader | Authorization | Name of the HTTP header. |
?rdfUrl=https://private-endpoint.example.org/data.ttl
&apiKey=Bearer+my-token
&apiKeyHeader=Authorization
The API key is sent only with the RDF fetch request. CORS: the server must allow the Ontosphere origin with credentials (wildcard * origins are incompatible with authenticated requests).
Ontosphere exposes a full Model Context Protocol tool surface so AI agents can build and reason over knowledge graphs through natural-language chat.
loadOntology (TBox)
→ addNode ×N (ABox individuals, rdf:type set)
→ addLink ×N (object-property triples, edges appear on canvas)
→ runLayout (dagre-lr recommended)
→ expandNode (reveal annotation property cards — omit iri to expand all)
→ runReasoning (infer subClass / domain / range entailments; isConsistent=false signals contradiction)
→ fitCanvas + exportImage (SVG snapshot, token-efficient)
→ exportGraph(turtle) (final deliverable)
Access-Control-Allow-Origin).?rdfUrl=... to avoid conflicts.专业的RDF编辑工具,MCP集成设计合理。代码质量高,社区活跃度中等,适合语义网领域专业用户。
该工具使用 NOASSERTION 协议,商用场景请仔细阅读协议条款,必要时咨询法律意见。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
总体来看,本体知识图谱编辑器 是一款质量良好的AI工具,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。
| 原始名称 | ontosphere |
| Topics | 知识图谱RDF编辑语义网JSON-LD图编辑器 |
| GitHub | https://github.com/ThHanke/ontosphere |
| License | NOASSERTION |
| 语言 | TypeScript |
收录时间:2026-06-10 · 更新时间:2026-06-10 · License:NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。