---
license: mit
task_categories:
- graph-ml
language:
- en
tags:
- Graph
- GNN
- KG
pretty_name: AutoScenarioQA
size_categories:
- n<1K
---

# AutoScenarioQA Dataset

`t_reference_questions.csv` is a question-answer dataset for reasoning over the automotive-market scenario graph `t_scenario_graph`. It contains 680 question records across three difficulty levels that target information-extraction and reasoning tasks on the scenario graph.

## What the QA Is Used For

The QA records model questions that members of a strategic foresight team may ask while developing, reviewing, or challenging automotive-industry scenarios. They can be used to evaluate a graph-query system, knowledge-graph assistant, or graph-reasoning model against expected answers and supporting evidence.

The questions cover both scenario content and graph structure. Scenario-focused questions address possible policy, market, technology, energy, and supply-chain developments and their consequences. Structure-focused questions address direct relationships between events, node labels and properties, chronological paths, branches, cycles, isolated nodes, predecessor and successor nodes, and filtered node sets.

This makes the dataset useful for testing whether graph-based QA systems distinguish plausible narrative answers from answers grounded in graph topology and node attributes.

## CSV Schema

| Column | Description |
| --- | --- |
| `user_group` | Intended user role, such as `Scenario Team Member`. |
| `reference_question` | Natural-language question about the scenario graph. |
| `category` | Question category: `Attribute-Based Filtering`, `Graph Navigation`, `Node Properties`, `Node and Label Identification`, `Path and Sequence Analysis`, `Relationships and Connections`, `Scenario-Specific Questions`, `Temporal Reasoning`, or `Topological Queries`. |
| `level` | Difficulty level: `1`, `2`, or `3`. |
| `reference_answer` | Expected answer to the question. |
| `reference_evidence` | Supporting graph evidence, such as node IDs, edge direction, or property names. |
| `all_node_ids` | Relevant node IDs where provided. |

## Graph Schema and Arrows.app Example

The scenario graph used by the questions is provided in [t_scenario_graph.json](t_scenario_graph.json). The accompanying Python scripts can generate the AutoScenarioQA dataset from the supplied graph, making the dataset adaptable to other scenario-graph realizations.

The graph contains 79 nodes and 109 relationships.

### Schema Elements

The graph has three top-level elements:

| Element | Purpose |
| --- | --- |
| `nodes` | Scenario containers, regional splits, events, and effects. |
| `relationships` | Directed links between node IDs. |
| `style` | Default graph display settings. |

### Node Schema

Node objects use the following fields:

| Field | Meaning |
| --- | --- |
| `id` | Unique `n<number>` identifier used by relationships. |
| `position` | Graph canvas coordinates: `x` and `y`. |
| `caption` | Human-readable name shown in the graph. |
| `labels` | One or more semantic categories for the node. |
| `properties` | Scenario attributes and vehicle sales volume where applicable. |
| `style` | Optional display overrides, including `node-color`, `border-color`, and `caption-color`. |

The node labels present in the published graph are:

- `Scenario`
- `Region-Split`
- `Certain Event`
- `Uncertain Event`
- `Less Certain Event`
- `Relevant Event`
- `Less Relevant Event`
- `Effect`

`Scenario` labels a scenario branch, and `Region-Split` assigns it to a regional branch. Event nodes can combine one certainty label (`Certain Event`, `Uncertain Event`, or `Less Certain Event`) with one relevance label (`Relevant Event` or `Less Relevant Event`). `Effect` identifies an outcome node.

### Node Properties

The published graph uses the following property keys where applicable:

| Property | Meaning |
| --- | --- |
| `START` | Start date or start period. |
| `END` | End date or end period. |
| `t` | Temporal value or period. |
| `BEV-SHARE` | Battery-electric-vehicle share. |
| `PremiumSegmentWithChinesePlayers` | Premium-segment vehicle sales volume involving Chinese competitors. |
| `TraditionalPremiumSegment` | Traditional premium-segment vehicle sales volume. |
| `TotalIndustry` | Total-industry vehicle sales volume. |

### Relationship Schema

Each relationship uses `id`, `fromId`, `toId`, `type`, `properties`, and `style`.

`fromId` is the source node and `toId` is the target node. The relationship types present in the published graph are:

| Type | Meaning |
| --- | --- |
| `isIn` | Places a scenario in a regional split. |
| `hasEvent` | Connects a scenario to an event in that scenario. |
| `isPriorTo` | States that the source event occurs before the target event. |
| `hasEffect` | Connects an event to an effect node. |