brandburner/doctorwho-s06-narrative-kg
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--- license: cc-by-sa-4.0 task_categories: - graph-ml - text-generation tags: - narrative - knowledge-graph - screenplay - fabula - neo4j - graph-gravity size_categories: - 1K<n<10K --- # Doctor Who - Narrative Knowledge Graph A rich narrative knowledge graph extracted from *Doctor Who* screenplays using the [Fabula](https://fabula.productions) pipeline. Contains characters, locations, objects, organizations, events, themes, and conflict arcs with full participation semantics and Graph Gravity importance tiers. ## Dataset Overview | Metric | Value | |--------|-------| | Source database | `doctorwho.s06` | | Type | Season database | | Episodes | 44 | | Total nodes | 6,840 | | Total edges | 22,964 | | Schema version | 1.1.0 | | Exported | 2026-04-08 | ### Entity Breakdown | Type | Count | |------|-------| | Act | 130 | | Agent | 306 | | ConflictArc | 176 | | Episode | 44 | | Event | 1,212 | | Location | 422 | | Object | 1,208 | | Organization | 120 | | PlotBeat | 2,158 | | SceneBoundary | 865 | | Theme | 199 | ### Graph Gravity Tiers | Tier | Count | Description | |------|-------|-------------| | anchor | 59 | Main characters / key locations | | planet | 348 | Recurring entities | | asteroid | 1,649 | Minor / one-off entities | ### Relationship Types `AFFILIATED_WITH`, `BELONGS_TO_EPISODE`, `CALLBACK`, `CAUSAL`, `CHARACTER_CONTINUITY`, `CONTAINS_ACT`, `CONTAINS_BEAT`, `CONTAINS_SCENE`, `EMOTIONAL_ECHO`, `ESCALATION`, `EXEMPLIFIES_THEME`, `FORESHADOWING`, `INVOLVED_IN_ARC`, `INVOLVED_WITH`, `IN_EVENT`, `NARRATIVELY_FOLLOWS`, `OCCURS_IN`, `PARTICIPATED_AS`, `PART_OF`, `PART_OF_ACT` ... and 5 more ## Related Datasets This is a **single-season** dataset containing entities and events as extracted from Season 6 screenplays. - **Megagraph** (all seasons unified): [brandburner/doctorwho-mega-narrative-kg](https://huggingface.co/datasets/brandburner/doctorwho-mega-narrative-kg) > **Note:** The megagraph is *not* a simple union of season datasets. Cross-season entities are reconciled through a Global Entity Registry (GER), receiving new canonical UUIDs and distilled descriptions. Graph Gravity tiers are recalculated across all episodes. Use individual season datasets for single-season analysis; use the megagraph for cross-season analysis. ## Files | File | Description | |------|-------------| | `nodes.parquet` | All graph nodes with properties | | `edges.parquet` | All relationships with properties | | `positions.parquet` | 3D layout coordinates for visualization | | `meta.json` | Dataset metadata and entity counts | ## Schema ### Nodes (`nodes.parquet`) | Column | Type | Description | |--------|------|-------------| | `node_id` | string | Unique node identifier (UUID) | | `primary_label` | string | Node type (Agent, Location, Event, etc.) | | `name` | string | Display name | | `description` | string | Foundational description | | `tier` | string (nullable) | Graph Gravity tier: anchor / planet / asteroid | | `episode_count` | int (nullable) | Number of distinct episodes entity appears in | | `first_episode_seq` | int (nullable) | First appearance episode | | `last_episode_seq` | int (nullable) | Last appearance episode | | `properties_json` | string | Full node properties as JSON | ### Edges (`edges.parquet`) | Column | Type | Description | |--------|------|-------------| | `source_node_id` | string | Source node UUID | | `target_node_id` | string | Target node UUID | | `relationship_type` | string | Relationship type (e.g., PARTICIPATED_AS) | | `properties_json` | string | Edge properties as JSON | ### Positions (`positions.parquet`) | Column | Type | Description | |--------|------|-------------| | `node_id` | string | Node UUID | | `x`, `y`, `z` | float | 3D coordinates | | `size` | float | Node size (Graph Gravity weighted) | | `r`, `g`, `b` | int | RGB color by entity type | | `community` | int | Louvain community index | | `tier` | string (nullable) | Graph Gravity tier | ## Usage ```python from datasets import load_dataset import pandas as pd # Load from HuggingFace ds = load_dataset("brandburner/doctorwho-s06-narrative-kg") # Or load parquet directly nodes = pd.read_parquet("nodes.parquet") edges = pd.read_parquet("edges.parquet") # Filter to anchor characters anchors = nodes[(nodes['primary_label'] == 'Agent') & (nodes['tier'] == 'anchor')] # Build a NetworkX graph import networkx as nx G = nx.DiGraph() for _, n in nodes.iterrows(): G.add_node(n['node_id'], label=n['primary_label'], name=n['name']) for _, e in edges.iterrows(): G.add_edge(e['source_node_id'], e['target_node_id'], type=e['relationship_type']) ``` ## Citation ```bibtex @misc{fabula_doctorwho_s06, title = {Doctor Who Narrative Knowledge Graph}, author = {Fabula Pipeline}, year = {2026}, publisher = {HuggingFace}, howpublished = {\url{https://huggingface.co/datasets/brandburner/doctorwho-s06-narrative-kg}} } ``` ## License CC BY-SA 4.0
license: 知识共享署名-相同方式共享4.0（CC BY-SA 4.0） task_categories: - 图机器学习（graph-ml） - 文本生成 tags: - 叙事 - 知识图谱 - 剧本 - 叙事基干（fabula） - Neo4j - Graph Gravity size_categories: - 1K<n<10K --- # 《神秘博士》叙事知识图谱 本数据集为基于Fabula处理流水线（Fabula），从《神秘博士》剧本中提取得到的高质量叙事知识图谱。其包含角色、地点、物品、组织、事件、主题以及冲突弧线，并完整保留参与语义与Graph Gravity重要性层级（Graph Gravity）。 ## 数据集概览 | 指标 | 数值 | |--------|-------| | 源数据库 | `doctorwho.s06` | | 数据集类型 | 季级数据库 | | 剧集数量 | 44 | | 总节点数 | 6,840 | | 总边数 | 22,964 | | 模式版本 | 1.1.0 | | 导出时间 | 2026-04-08 | ### 实体分类统计 | 实体类型 | 数量 | |------|-------| | 幕（Act） | 130 | | 实体（Agent） | 306 | | 冲突弧（ConflictArc） | 176 | | 剧集（Episode） | 44 | | 事件（Event） | 1,212 | | 地点（Location） | 422 | | 物品（Object） | 1,208 | | 组织（Organization） | 120 | | 剧情节拍（PlotBeat） | 2,158 | | 场景边界（SceneBoundary） | 865 | | 主题（Theme） | 199 | ### Graph Gravity重要性层级 | 层级 | 数量 | 描述 | |------|-------|-------------| | 锚点（anchor） | 59 | 主要角色 / 关键地点 | | 行星（planet） | 348 | 常驻实体 | | 小行星（asteroid） | 1,649 | 次要 / 一次性实体 | ### 关系类型 `AFFILIATED_WITH`、`BELONGS_TO_EPISODE`、`CALLBACK`、`CAUSAL`、`CHARACTER_CONTINUITY`、`CONTAINS_ACT`、`CONTAINS_BEAT`、`CONTAINS_SCENE`、`EMOTIONAL_ECHO`、`ESCALATION`、`EXEMPLIFIES_THEME`、`FORESHADOWING`、`INVOLVED_IN_ARC`、`INVOLVED_WITH`、`IN_EVENT`、`NARRATIVELY_FOLLOWS`、`OCCURS_IN`、`PARTICIPATED_AS`、`PART_OF`、`PART_OF_ACT` ……及另外5种关系类型。 ## 关联数据集 本数据集为单季数据集，提取自《神秘博士》第六季剧本中的实体与事件。 - **总图谱（Megagraph）**（全季合并数据集）：[brandburner/doctorwho-mega-narrative-kg](https://huggingface.co/datasets/brandburner/doctorwho-mega-narrative-kg) > **注意：** 总图谱并非各季数据集的简单合并。跨季实体通过全局实体注册系统（Global Entity Registry, GER）进行统一对齐，获得全新的标准UUID与精炼描述信息。Graph Gravity重要性层级将基于全剧集数据重新计算。若需开展单季分析，请使用单季数据集；若需进行跨季分析，则使用总图谱。 ## 文件说明 | 文件 | 描述 | |------|-------------| | `nodes.parquet` | 包含所有带属性的图节点 | | `edges.parquet` | 包含所有带属性的图关系 | | `positions.parquet` | 用于可视化的3D布局坐标文件 | | `meta.json` | 数据集元数据与实体统计信息文件 | ## 数据模式 ### 节点表（`nodes.parquet`） | 列名 | 数据类型 | 描述 | |--------|------|-------------| | `node_id` | 字符串 | 唯一节点标识符（UUID） | | `primary_label` | 字符串 | 节点类型（如Agent、Location、Event等） | | `name` | 字符串 | 显示名称 | | `description` | 字符串 | 基础描述信息 | | `tier` | 字符串（可为空） | Graph Gravity重要性层级：anchor / planet / asteroid | | `episode_count` | 整数（可为空） | 实体出现的不同剧集数 | | `first_episode_seq` | 整数（可为空） | 实体首次出现的剧集序号 | | `last_episode_seq` | 整数（可为空） | 实体末次出现的剧集序号 | | `properties_json` | 字符串 | 完整节点属性的JSON格式字符串 | ### 边表（`edges.parquet`） | 列名 | 数据类型 | 描述 | |--------|------|-------------| | `source_node_id` | 字符串 | 源节点UUID | | `target_node_id` | 字符串 | 目标节点UUID | | `relationship_type` | 字符串 | 关系类型（如PARTICIPATED_AS） | | `properties_json` | 字符串 | 边属性的JSON格式字符串 | ### 坐标表（`positions.parquet`） | 列名 | 数据类型 | 描述 | |--------|------|-------------| | `node_id` | 字符串 | 节点UUID | | `x`, `y`, `z` | 浮点数 | 3D空间坐标 | | `size` | 浮点数 | 节点大小（基于Graph Gravity权重计算） | | `r`, `g`, `b` | 整数 | 基于实体类型的RGB颜色值 | | `community` | 整数 | Louvain社区聚类索引 | | `tier` | 字符串（可为空） | Graph Gravity重要性层级 | ## 使用示例 python from datasets import load_dataset import pandas as pd # 从HuggingFace加载数据集 ds = load_dataset("brandburner/doctorwho-s06-narrative-kg") # 或直接加载parquet文件 nodes = pd.read_parquet("nodes.parquet") edges = pd.read_parquet("edges.parquet") # 筛选锚点层级角色 anchors = nodes[(nodes['primary_label'] == 'Agent') & (nodes['tier'] == 'anchor')] # 构建NetworkX图结构 import networkx as nx G = nx.DiGraph() for _, n in nodes.iterrows(): G.add_node(n['node_id'], label=n['primary_label'], name=n['name']) for _, e in edges.iterrows(): G.add_edge(e['source_node_id'], e['target_node_id'], type=e['relationship_type']) ## 引用格式 bibtex @misc{fabula_doctorwho_s06, title = {Doctor Who Narrative Knowledge Graph}, author = {Fabula Pipeline}, year = {2026}, publisher = {HuggingFace}, howpublished = {url{https://huggingface.co/datasets/brandburner/doctorwho-s06-narrative-kg}} } ## 授权协议 CC BY-SA 4.0（知识共享署名-相同方式共享4.0）
