ARF
Full name: Artificial Relationships in Fiction

# Artificial Relationships in Fiction

## Dataset Description
Artificial Relationships in Fiction (ARF) is a synthetically annotated dataset for Relation Extraction (RE) in fiction, created from a curated selection of literary texts sourced from Project Gutenberg. The dataset captures the rich, implicit relationships within fictional narratives using a novel ontology and GPT-4o for annotation. ARF is the first large-scale RE resource designed specifically for literary texts, advancing both NLP model training and computational literary analysis.

## Dataset Configurations and Features

### Configurations
- `fiction_books`: Metadata-rich corpus of 6,322 public domain fiction books (1850–1950) with inferred author gender and thematic categorization.
- `fiction_books_in_chunks`: Books segmented into 5-sentence chunks (5.96M total), preserving narrative coherence via 1-sentence overlap.
- `fiction_books_with_relations`: A subset of 95,475 text chunks annotated with 128,000+ relationships using GPT-4o and a fiction-specific ontology.


### 1. Configuration: `fiction_books`
- **Description**: Contains the full text and metadata of 6,322 English-language fiction books from Project Gutenberg.
- **Features**:
  - `book_id`: Unique Project Gutenberg ID.
  - `title`: Title of the book.
  - `author`: Author name.
  - `author_birth_year` / `author_death_year`: Author lifespan.
  - `release_date`: PG release date.
  - `subjects`: List of thematic topics (mapped to 51 standardized themes).
  - `gender`: Inferred author gender (via GPT-4o).
  - `text`: Cleaned full book text.
- **Use Case**: Supports thematic and demographic analysis of literary texts.

### 2. Configuration: `fiction_books_in_chunks`
- **Description**: Each book is segmented into overlapping five-sentence text chunks to enable granular NLP analysis.
- **Features**:
  - `book_id`, `chunk_index`: Book and chunk identifiers.
  - `text_chunk`: Five-sentence excerpt from the book.
- **Use Case**: Facilitates sequence-level tasks like coreference resolution or narrative progression modeling.

### 3. Configuration: `synthetic_relations_in_fiction_books` (ARF)
- **Description**: This subset corresponds to the **Artificial Relationships in Fiction (ARF)** dataset proposed in the LaTeCH-CLfL 2025 paper *"Artificial Relationships in Fiction: A Dataset for Advancing NLP in Literary Domains"*.
- **Features**:
  - `book_id`, `chunk_index`: Identifiers.
  - `text_chunk`: Five-sentence text segment.
  - `relations`: A list of structured relation annotations, each containing:
    - `entity1`, `entity2`: Text spans.
    - `entity1Type`, `entity2Type`: Entity types based on ontology.
    - `relation`: Relationship type.
- **Use Case**: Ideal for training and evaluating RE models in fictional narratives, studying character networks, and generating structured data from literary texts.



## ARF Dataset Structure (config 'synthetic_relations_in_fiction_books')
Each annotated relation is formatted as:
```json
{
  "entity1": "Head Entity text",
  "entity2": "Tail Entity text",
  "entity1Type": "Head entity type",
  "entity2Type": "Tail entity type",
  "relation": "Relation type"
}
```

Example:
```json
{
  "entity1": "Vortigern",
  "entity2": "castle",
  "entity1Type": "PER",
  "entity2Type": "FAC",
  "relation": "owns"
}
```

### Entity Types (11)
| Entity Type | Description |
|-------------|-------------|
| `PER` | Person or group of people |
| `FAC` | Facility – man-made structures for human use |
| `LOC` | Location – natural or loosely defined geographic regions |
| `WTHR` | Weather – atmospheric or celestial phenomena |
| `VEH` | Vehicle – transport devices (e.g., ship, carriage) |
| `ORG` | Organization – formal groups or institutions |
| `EVNT` | Event – significant occurrences in narrative |
| `TIME` | Time – chronological or historical expressions |
| `OBJ` | Object – tangible items in the text |
| `SENT` | Sentiment – emotional states or feelings |
| `CNCP` | Concept – abstract ideas or motifs |

### Relation Types (48)
| Relation Type        | Entity 1 Type     | Entity 2 Type     | Description                               |
|----------------------|------------------|-------------------|-------------------------------------------|
| `parent_father_of` | `PER` | `PER` | Father relationship |
| `parent_mother_of` | `PER` | `PER` | Mother relationship |
| `child_of` | `PER` | `PER` | Child to parent |
| `sibling_of` | `PER` | `PER` | Sibling relationship |
| `spouse_of` | `PER` | `PER` | Spousal relationship |
| `relative_of` | `PER` | `PER` | Extended family relationship |
| `adopted_by` | `PER` | `PER` | Adopted by another person |
| `companion_of` | `PER` | `PER` | Companionship or ally |
| `friend_of` | `PER` | `PER` | Friendship |
| `lover_of` | `PER` | `PER` | Romantic relationship |
| `rival_of` | `PER` | `PER` | Rivalry |
| `enemy_of` | `PER/ORG` | `PER/ORG` | Hostile or antagonistic relationship |
| `inspires` | `PER` | `PER` | Inspires or motivates |
| `sacrifices_for` | `PER` | `PER` | Makes a sacrifice for |
| `mentor_of` | `PER` | `PER` | Mentorship or guidance |
| `teacher_of` | `PER` | `PER` | Formal teaching relationship |
| `protector_of` | `PER` | `PER` | Provides protection to |
| `employer_of` | `PER` | `PER` | Employment relationship |
| `leader_of` | `PER` | `ORG` | Leader of an organization |
| `member_of` | `PER` | `ORG` | Membership in an organization |
| `lives_in` | `PER` | `FAC/LOC` | Lives in a location |
| `lived_in` | `PER` | `TIME` | Historically lived in |
| `visits` | `PER` | `FAC` | Visits a facility |
| `travel_to` | `PER` | `LOC` | Travels to a location |
| `born_in` | `PER` | `LOC` | Birthplace |
| `travels_by` | `PER` | `VEH` | Travels by a vehicle |
| `participates_in` | `PER` | `EVNT` | Participates in an event |
| `causes` | `PER` | `EVNT` | Causes an event |
| `owns` | `PER` | `OBJ` | Owns an object |
| `believes_in` | `PER` | `CNCP` | Believes in a concept |
| `embodies` | `PER` | `CNCP` | Embodies a concept |
| `located_in` | `FAC` | `LOC` | Located in a place |
| `part_of` | `FAC/LOC/ORG` | `FAC/LOC/ORG` | Part of a larger entity |
| `owned_by` | `FAC/VEH` | `PER` | Owned by someone |
| `occupied_by` | `FAC` | `PER` | Occupied by someone |
| `used_by` | `FAC` | `ORG` | Used by an organization |
| `affects` | `WTHR` | `LOC/EVNT` | Weather affects location or event |
| `experienced_by` | `WTHR` | `PER` | Weather experienced by someone |
| `travels_in` | `VEH` | `LOC` | Vehicle travels in a location |
| `based_in` | `ORG` | `LOC` | Organization based in a location |
| `attended_by` | `EVNT` | `PER` | Event attended by person |
| `ends_in` | `EVNT` | `TIME` | Event ends at a time |
| `occurs_in` | `EVNT` | `LOC/TIME` | Event occurs in a place or time |
| `features` | `EVNT` | `OBJ` | Event features an object |
| `stored_in` | `OBJ` | `LOC/FAC` | Object stored in a place |
| `expressed_by` | `SENT` | `PER` | Sentiment expressed by person |
| `used_by` | `OBJ` | `PER` | Object used by person |
| `associated_with` | `CNCP` | `EVNT` | Concept associated with event |

## Dataset Statistics
| Metric                     | Value      |
|----------------------------|------------|
| Books                     | 96         |
| Authors                   | 91         |
| Gender Ratio (M/F)        | 55% / 45%  |
| Subgenres                 | 51         |
| Annotated Chunks          | 95,475     |
| Relations per Chunk       | 1.34 avg   |
| Chunks with No Relations  | 35,230     |
| Total Relations           | ~128,000   |

## Methodology
- **Source Texts**: English-language fiction from PG bookshelves: Fiction, Children & YA, Crime/Mystery.
- **Annotation Model**: GPT-4o via custom prompt integrating strict ontologies.
- **Sampling**: Balanced author gender and thematic distributions.
- **Ontology Adherence**: <0.05% deviation for entities; 2.95% for relations.
- **Format**: Structured JSON, optimized for NLP pipelines.

## Applications
- **Fine-tuning RE Models**: Adapt models to literary domains with implicit, evolving relationships.
- **Computational Literary Studies**: Analyze character networks, thematic evolution, and genre patterns.
- **Creative AI**: Enhance AI-driven storytelling, character consistency, and world-building tools.

## Citation
If you use this dataset in your research, please cite:

```bibtex
@inproceedings{christou-tsoumakas-2025-artificial,
    title = "Artificial Relationships in Fiction: A Dataset for Advancing {NLP} in Literary Domains",
    author = "Christou, Despina  and Tsoumakas, Grigorios",
    editor = "Kazantseva, Anna and Szpakowicz, Stan and Degaetano-Ortlieb, Stefania and Bizzoni, Yuri and Pagel, Janis",
    booktitle = "Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.latechclfl-1.13/",
    pages = "130--147",
    ISBN = "979-8-89176-241-1"
}
```