brandburner/doctorwho-s14-narrative-kg

--- 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.s14` | | Type | Season database | | Episodes | 26 | | Total nodes | 3,997 | | Total edges | 11,967 | | Schema version | 1.1.0 | | Exported | 2026-04-08 | ### Entity Breakdown | Type | Count | |------|-------| | Act | 72 | | Agent | 193 | | ConflictArc | 137 | | Episode | 26 | | Event | 710 | | Location | 225 | | Object | 669 | | Organization | 28 | | PlotBeat | 1,337 | | SceneBoundary | 475 | | Theme | 125 | ### Graph Gravity Tiers | Tier | Count | Description | |------|-------|-------------| | anchor | 18 | Main characters / key locations | | planet | 244 | Recurring entities | | asteroid | 853 | 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 6 more ## Related Datasets This is a **single-season** dataset containing entities and events as extracted from Season 14 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-s14-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_s14, title = {Doctor Who Narrative Knowledge Graph}, author = {Fabula Pipeline}, year = {2026}, publisher = {HuggingFace}, howpublished = {\url{https://huggingface.co/datasets/brandburner/doctorwho-s14-narrative-kg}} } ``` ## License CC BY-SA 4.0许可证：CC BY-SA 4.0 任务类别： - 图机器学习（Graph ML） - 文本生成 标签： - 叙事（Narrative） - 知识图谱（Knowledge Graph） - 剧本（Screenplay） - Fabula - Neo4j - Graph Gravity 数据规模类别：1K<n<10K # 《神秘博士》叙事知识图谱（Doctor Who - Narrative Knowledge Graph） 本数据集通过Fabula流水线从《神秘博士》官方剧本中提取构建，是一套完备的叙事知识图谱，涵盖角色、地点、物品、组织、事件、主题及冲突弧结构，包含完整的实体参与语义与Graph Gravity重要性分层体系。 ## 数据集概览 | 指标 | 数值 | |--------|-------| | 源数据库 | `doctorwho.s14` | | 数据集类型 | 季级数据库 | | 剧集总数 | 26 | | 总节点数 | 3,997 | | 总边数 | 11,967 | | 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） | 72 | | 角色（Agent） | 193 | | 冲突弧（ConflictArc） | 137 | | 剧集（Episode） | 26 | | 事件（Event） | 710 | | 地点（Location） | 225 | | 物品（Object） | 669 | | 组织（Organization） | 28 | | 剧情节拍（PlotBeat） | 1,337 | | 场景边界（SceneBoundary） | 475 | | 主题（Theme） | 125 | ### Graph Gravity重要性层级 | 层级 | 数量 | 描述 | |------|-------|-------------| | 锚点（anchor） | 18 | 主要角色 / 关键地点 | | 行星（planet） | 244 | 常驻实体 | | 小行星（asteroid） | 853 | 次要 / 一次性实体 | ### 关系类型 包含`AFFILIATED_WITH`（隶属关联）、`BELONGS_TO_EPISODE`（归属剧集）、`CALLBACK`（回调呼应）、`CAUSAL`（因果关联）、`CHARACTER_CONTINUITY`（角色连贯性）、`CONTAINS_ACT`（包含幕）、`CONTAINS_BEAT`（包含剧情节拍）、`CONTAINS_SCENE`（包含场景）、`EMOTIONAL_ECHO`（情感共鸣）、`ESCALATION`（冲突升级）、`EXEMPLIFIES_THEME`（诠释主题）、`FORESHADOWING`（伏笔铺垫）、`INVOLVED_IN_ARC`（参与冲突弧）、`INVOLVED_WITH`（关联互动）、`IN_EVENT`（参与事件）、`NARRATIVELY_FOLLOWS`（叙事时序跟随）、`OCCURS_IN`（发生于）、`PARTICIPATED_AS`（以身份参与）、`PART_OF`（属于）、`PART_OF_ACT`（属于幕）等，另有6种未完全列出的关系类型。 ## 关联数据集 本数据集为**单季数据集**，包含从《神秘博士》第14季剧本中提取的实体与事件。 - **全季整合巨图（Megagraph）**（覆盖所有季数据）：[brandburner/doctorwho-mega-narrative-kg](https://huggingface.co/datasets/brandburner/doctorwho-mega-narrative-kg) > **注意：** 全季整合巨图并非单季数据集的简单合集，跨季实体将通过全局实体注册表（Global Entity Registry, GER）完成对齐，获取全新的规范UUID与精炼描述。Graph Gravity重要性层级将在全剧集范围内重新计算。如需开展单季分析，请使用单季数据集；如需开展跨季分析，请使用全季整合巨图。 ## 数据文件说明 | 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边 | | `positions.parquet` | 用于可视化的3D布局坐标数据 | | `meta.json` | 数据集元数据与实体计数统计 | ## 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（Agent、Location、Event等） | | `name` | 字符串 | 实体显示名称 | | `description` | 字符串 | 实体基础描述信息 | | `tier` | 可空字符串 | Graph Gravity层级：anchor / planet / asteroid | | `episode_count` | 可空整数 | 实体出现的不同剧集数量 | | `first_episode_seq` | 可空整数 | 实体首次出现的剧集序号 | | `last_episode_seq` | 可空整数 | 实体最后一次出现的剧集序号 | | `properties_json` | 字符串 | 以JSON格式存储的完整节点属性 | ### 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（例如PARTICIPATED_AS） | | `properties_json` | 字符串 | 以JSON格式存储的边属性 | ### 位置表（`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可视化坐标 | | `size` | 浮点数 | 节点大小（由Graph Gravity加权计算得出） | | `r`, `g`, `b` | 整数 | 按实体类型区分的RGB颜色值 | | `community` | 整数 | Louvain社区聚类索引 | | `tier` | 可空字符串 | Graph Gravity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-s14-narrative-kg") # 或直接读取Parquet文件 nodes = pd.read_parquet("nodes.parquet") edges = pd.read_parquet("edges.parquet") # 筛选锚点角色 anchors = nodes[(nodes['primary_label'] == 'Agent') & (nodes['tier'] == 'anchor')] # 构建NetworkX有向图 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_s14, title = {Doctor Who Narrative Knowledge Graph}, author = {Fabula团队}, year = {2026}, publisher = {HuggingFace}, howpublished = {url{https://huggingface.co/datasets/brandburner/doctorwho-s14-narrative-kg}} } ## 许可证 CC BY-SA 4.0