|
|
| --- |
| license: mit |
| task_categories: |
| - graph-ml |
| language: |
| - en |
| tags: |
| - Graph |
| - GNN |
| - KG |
| pretty_name: AutoScenarioQA |
| size_categories: |
| - n<1K |
| --- |
| |
| # AutoScenarioQA Dataset |
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| `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. |
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| ## What the QA Is Used For |
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| 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. |
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| 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. |
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| 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. |
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| ## CSV Schema |
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| | 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. | |
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| ## Graph Schema and Arrows.app Example |
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| 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. |
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| The graph contains 79 nodes and 109 relationships. |
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| ### Schema Elements |
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| The graph has three top-level elements: |
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| | Element | Purpose | |
| | --- | --- | |
| | `nodes` | Scenario containers, regional splits, events, and effects. | |
| | `relationships` | Directed links between node IDs. | |
| | `style` | Default graph display settings. | |
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| ### Node Schema |
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| Node objects use the following fields: |
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| | 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`. | |
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| The node labels present in the published graph are: |
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|
| - `Scenario` |
| - `Region-Split` |
| - `Certain Event` |
| - `Uncertain Event` |
| - `Less Certain Event` |
| - `Relevant Event` |
| - `Less Relevant Event` |
| - `Effect` |
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| `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. |
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| ### Node Properties |
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| The published graph uses the following property keys where applicable: |
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| | 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. | |
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| ### Relationship Schema |
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| Each relationship uses `id`, `fromId`, `toId`, `type`, `properties`, and `style`. |
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| `fromId` is the source node and `toId` is the target node. The relationship types present in the published graph are: |
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| | 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. | |
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