meta string | num_nodes int64 | num_edges int64 | nodes string | edges string |
|---|---|---|---|---|
{"description": "Unified Knowledge Graph linking all Simson datasets", "datasets": {"racing-planet-simson-traces": {"records": 15, "type": "diagnosis_traces"}, "simson-forum-qa-pairs": {"records": 147, "type": "community_knowledge"}, "simson-repair-manual": {"records": 14, "type": "technical_reference"}, "racing-planet... | 173 | 348 | [{"id": "n0000", "type": "forum_qa", "data_id": "sb-faq-001", "label": "Piccolo Duo 1 – 4", "dataset": "simson-forum-qa-pairs", "topic": "Allgemein", "model": null}, {"id": "n0001", "type": "forum_qa", "data_id": "sb-faq-002", "label": "Welches Benzin muss meine Simson tanken?", "dataset": "simson-forum-qa-pairs", "top... | [{"id": "e00000", "source": "n0003", "target": "n0095", "relation": "references", "weight": 0.7}, {"id": "e00001", "source": "n0016", "target": "n0095", "relation": "references", "weight": 0.7}, {"id": "e00002", "source": "n0019", "target": "n0095", "relation": "references", "weight": 0.7}, {"id": "e00003", "source": "... |
🧠 Simson Unified Knowledge Graph
173 Nodes × 348 Edges – der Klebstoff zwischen allen Simson-Datasets.
Was das ist
Ein maschinenlesbarer Graph, der alle 6 Datasets miteinander verknüpft:
| Dataset | Status | Nodes |
|---|---|---|
| racing-planet-simson-traces | Diagnose-Traces | 15 |
| simson-forum-qa-pairs | Forum-Wissen | 30 |
| simson-repair-manual | Technische Daten | 14 |
| racing-planet-product-catalog | Teilekatalog | 37 |
| simson-youtube-tutorials | Video-Tutorials | 20 |
| simson-part-problem-matrix | Diagnose-Logik | 57 |
Edge-Typen
| Relation | Anzahl | Bedeutung |
|---|---|---|
similar_to |
228 | Forum-Threads mit gleichem Topic |
has_cause |
44 | Symptom → Ursache |
fixed_by_part |
38 | Ursache → Produkt |
fixes_with |
35 | Symptom → direktes Produkt |
references |
3 | Forum → Handbuch |
demonstrates |
(weitere) | YouTube → Handbuch |
Verwendung
from datasets import load_dataset
import json
ds = load_dataset("jmp1987/simson-unified-knowledge-graph")
graph = json.loads(ds["train"][0]["nodes"])
edges = json.loads(ds["train"][0]["edges"])
# Finde alle Teile für ein Symptom
symptom_node = next(n for n in graph if n["label"].startswith("Motor springt"))
related = [e for e in edges if e["source"] == symptom_node["id"]]
RAG-Integration
Der Knowledge Graph erlaubt:
- Hop-1 Retrieval: Frage → Symptom-Node → verlinkte Produkte/Manual/Forum
- Hop-2 Retrieval: Frage → Symptom → Ursache → nötige Teile → Assembly-Deps
- Cross-Dataset Retrieval: Automatisches Finden relevanter Chunks aus allen Datasets
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