🎬 IMDB4M

A Large-Scale Quad-Modal Knowledge Graph of Movies

A comprehensive RDF knowledge graph integrating Text, Images, Video, and Audio modalities for 376 fully-annotated movies

Overview • Features • Schema • Installation • Usage • Media Downloader • Evaluation • Citation

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📖 Overview

IMDB4M is a large-scale, quad-modal knowledge graph for the movie domain that overcomes the bimodal bottleneck of existing multimodal knowledge graphs.

IMDB4M comprehensively harmonises symbolic metadata of movies and actors and integrates them with four distinct modalities: text (plots, comments, reviews), images (posters, stills), video (trailers), and audio (soundtracks). Unlike prior resources often constructed with ad-hoc vocabularies, IMDB4M is engineered on schema.org to ensure semantic interoperability, discoverability, and structural quality.

The knowledge graph integrates:

• 🎥 Movie Metadata: Titles, plots, genres, ratings, release dates, budgets, revenues, production companies
• 🎭 Cast & Crew: 5,484 actors, directors, writers with complete filmographies using schema:PerformanceRole
• 🎵 Soundtracks: Music recordings and compositions with performers, composers, lyricists (avg. 11.2 per movie)
• 📹 Videos: Movie trailers with thumbnails, duration, and upload dates (99.2% coverage)
• 🖼️ Images: Movie stills and promotional images with captions and entity links (avg. 7.9 per movie)
• ⭐ Reviews & Ratings: User reviews, aggregate ratings, Metacritic scores, AI-generated summaries
• 🔗 External Links: Wikidata entity alignments via owl:sameAs mappings (78.1% artist coverage)

Key Design Principles:

• Linking over Hosting: Stores external URIs to legitimate platforms (IMDb, YouTube) rather than raw media to respect copyright
• Schema.org Vocabulary: Ensures semantic interoperability and Web-scale discoverability
• First-class Multimodal Objects: Modalities are typed semantic objects, not flat attributes

---

✨ Features

🗃️ Quad-Modal Data Integration

Modality	Description	Schema.org Types	Properties
Text	Plots, reviews, keywords, genre	schema:description, schema:Review	100% coverage, 48.6 avg/movie
Image	Stills, posters with captions & entity links	schema:ImageObject	100% coverage, 7.9 avg/movie
Video	Trailers with thumbnails, duration, upload dates	schema:VideoObject	99.2% coverage
Audio	Soundtracks with performers, composers, lyricists	schema:MusicRecording, schema:MusicComposition	94.15% coverage, 11.2 avg/movie

📊 Knowledge Graph Statistics

Metric	Value
RDF Triples	1,815,922
Unique Nodes	660,039
Unique Predicates	58
Seed Movies (fully annotated)	376
Total Movies (after expansion)	50,756
Artists (actors, directors, composers)	5,484
PerformanceRole instances	232,492
ImageObjects	36,844
Wikidata Alignments (Artists)	4,284 (78.1%)
Entity Types	17

📈 Modality Coverage (Seed Movies)

Modality	Coverage	Avg. per Movie
Text (plots, reviews, keywords)	100%	48.63 elements
Images (stills, posters)	100%	7.91 images
Video (trailers)	99.2%	0.99 videos
Audio (soundtracks)	94.15%	11.20 clips

93.62% of movies possess all four modalities simultaneously.

🔗 External Linkage

• Wikidata Integration: owl:sameAs mappings with 78.1% coverage for artists and 100% for seed movies
• YouTube Links: Soundtrack-to-video linking via neuro-symbolic RAG pipeline (87.16% accuracy)

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🏗️ Knowledge Graph Schema

IMDB4M uses Schema.org vocabulary as its primary ontology, chosen for:

1. Coverage: Provides primitives for movies, creative works, media objects, ratings, monetary values
2. Expressiveness: Rich typed representations via schema:ImageObject, schema:VideoObject, schema:MusicRecording
3. Interoperability: Widely adopted across the Web of Data, natively used by IMDb and YouTube

Schema Design Principles

• PerformanceRole Pattern: Actor participation uses schema:PerformanceRole to capture actor, movie, and schema:characterName together
• N-ary Structures: Typed blank nodes with xsd:date, xsd:dateTime, xsd:duration, xsd:integer, xsd:decimal
• Two-level Audio: schema:MusicRecording for performed audio, schema:MusicComposition for underlying work

Key Properties

Property	Domain	Range	Description
schema:actor	Movie	PerformanceRole	Cast member with character
schema:characterName	PerformanceRole	Text	Character played by actor
schema:director	Movie	Person	Film director
schema:creator	Movie	Person	Writer/creator
schema:trailer	Movie	VideoObject	Movie trailer
schema:audio	Movie	MusicRecording	Soundtrack entry
schema:image	Movie	ImageObject	Movie still/poster
schema:aggregateRating	Movie	AggregateRating	IMDb/Metacritic score
schema:review	Movie	Review	User review
schema:byArtist	MusicRecording	Person	Performer
schema:recordingOf	MusicRecording	MusicComposition	Underlying musical work
schema:composer	MusicComposition	Person	Music composer
schema:lyricist	MusicComposition	Person	Lyrics writer
schema:caption	ImageObject	Text	Image description
schema:mainEntity	ImageObject	Person	Cast members in image
schema:embedUrl	VideoObject	URL	Trailer embed URL
schema:thumbnailUrl	VideoObject	URL	Trailer thumbnail
schema:duration	VideoObject	Duration	Video length (xsd:duration)
schema:performerIn	Person	Movie	Actor filmography
owl:sameAs	Entity	WikidataURI	External link

---

📁 Repository Structure

imdb4m/
├── 📂 data/
│   ├── 📂 movies/                   # Movie data organized by IMDb ID
│   │   └── 📂 tt0120338/           # Example: Titanic
│   │       ├── 📂 movie_html/      # Parsed movie metadata (.ttl)
│   │       └── 📂 movie_soundtrack/ # Soundtrack metadata (.ttl, .json)
│   ├── 📂 kg/                       # Consolidated knowledge graph
│   │   ├── imdb_kg_cleaned.ttl     # Main KG file
│   │   └── sameas_mappings.ttl     # Wikidata alignments
│   └── 📂 sample/                   # Sample subset for testing
│
├── 📂 linker/                       # Music Linker module
│   ├── models.py                   # Pydantic data models
│   ├── youtube_client.py           # YouTube API integration
│   ├── gemini_matcher.py           # LLM-powered matching
│   └── music_linker.py             # Main orchestrator
│
├── 📂 media_downloader/             # Media Download module
│   ├── kg_parser.py                # KG parser for media URLs
│   ├── image_downloader.py         # Image download from Amazon CDN
│   ├── video_downloader.py         # Video download from IMDb
│   ├── audio_downloader.py         # Audio download from YouTube
│   ├── download_entity.py          # Single entity downloader
│   └── download_all.py             # Batch downloader with resume
│
├── 📂 extractor/                     # Data collection scripts
│   ├── download_imdb_movie.py      # Movie page extractor
│   ├── download_imdb_actor.py      # Actor page extractor
│   └── 📂 movie_seeds/             # Movie selection criteria
│
├── 📂 QA/                           # Quality assurance
│   ├── QA_gold.json                # Gold standard annotations
│   ├── qa_kg.json                  # KG-derived answers
│   └── evaluate_qa.py              # Evaluation metrics
│
├── 📜 parse_imdb_movie.py          # HTML → RDF parser (movies)
├── 📜 parse_imdb_actor.py          # HTML → RDF parser (actors)
├── 📜 parse_soundtrack_to_ttl.py   # Soundtrack → RDF parser
├── 📜 analyze_kg.py                # KG statistics & analysis
├── 📜 create_sameas_mappings.py    # Wikidata linking
└── 📜 requirements.txt             # Python dependencies

---

🔧 Construction Methodology

IMDB4M follows a four-stage pipeline:

1. Seeding Strategy

• Sampled N=100 movies per decade (1980-2020) for temporal diversity
• Resulted in 376 distinct seed movies after deduplication
• Each seed enriched with top 20 cast, trailers, images, reviews

2. Recursive Expansion

• Extracted 5,484 unique artists from seed movies
• Retrieved complete filmographies to extend neighbourhood structure
• Captured latent connections through shared collaborators

3. Pruning

• Removed ~69,000 leaf-node movies connected to only one artist
• Yielded refined core of 660,039 movies and 5,484 artists

4. External Linking

• Wikidata Alignment: Query SPARQL endpoint via IMDb ID property P345
• YouTube Linking: RAG pipeline with Gemini verification for soundtracks

Data Extraction

The extraction pipeline leverages:

• JSON-LD blocks from IMDb pages (primary source for schema.org metadata)
• Next.js data payloads for deeply nested structures (credits, filmographies, reviews)
• DOM traversal fallback for alternate titles, budgets, gallery references

---

🚀 Installation

Prerequisites

• Python 3.10 or higher
• pip package manager

Setup

# Clone the repository
git clone https://github.com/onradio/imdb4m.git
cd imdb4m

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

API Keys (Optional - for Music Linker)

For soundtrack-to-YouTube linking functionality:

1. YouTube Data API v3: Google Cloud Console
2. Google Gemini API: Google AI Studio

# Create .env file with your keys
cp .env.template .env
# Edit .env with your API keys

---

📚 Usage

Loading the Knowledge Graph

from rdflib import Graph

# Load the main knowledge graph
g = Graph()
g.parse("data/kg/imdb_kg_cleaned.ttl", format="turtle")

print(f"Loaded {len(g)} triples")

SPARQL Queries

# Find all movies with their directors
query = """
PREFIX schema: <http://schema.org/>

SELECT ?movie ?title ?director ?directorName
WHERE {
    ?movie a schema:Movie ;
           schema:name ?title ;
           schema:director ?director .
    ?director schema:name ?directorName .
}
LIMIT 10
"""

for row in g.query(query):
    print(f"{row.title} - Directed by {row.directorName}")

Query Videos/Trailers

# Find all movies with trailers
query = """
PREFIX schema: <http://schema.org/>

SELECT ?movie ?title ?trailerName ?embedUrl ?duration
WHERE {
    ?movie a schema:Movie ;
           schema:name ?title ;
           schema:trailer ?trailer .
    ?trailer a schema:VideoObject ;
             schema:name ?trailerName ;
             schema:embedUrl ?embedUrl .
    OPTIONAL { ?trailer schema:duration ?duration }
}
LIMIT 10
"""

for row in g.query(query):
    print(f"{row.title}: {row.trailerName} - {row.embedUrl}")

Parsing New Movies

# Parse a movie HTML file
python parse_imdb_movie.py path/to/movie.html -o output.ttl

# Parse soundtrack data
python parse_soundtrack_to_ttl.py path/to/soundtrack.html

Neuro-Symbolic Audio Linker

The Music Linker uses a Retrieval-Augmented Generation (RAG) pipeline to link soundtrack entities to YouTube videos:

1. Stage 1 - Retrieval: Query YouTube Data API v3 using soundtrack metadata (title, artist, movie) with progressive relaxation
2. Stage 2 - Verification: Use Gemini 2.5 Flash as a neuro-symbolic reasoner to verify candidates and disambiguate between official releases vs covers

from linker import MusicLinker, SoundtrackParser, Config

# Initialize
config = Config()
linker = MusicLinker(
    youtube_api_key=config.youtube_api_key,
    gemini_api_key=config.gemini_api_key
)

# Parse soundtrack from TTL
soundtracks = SoundtrackParser.parse_soundtrack_ttl(
    subset_root="data/sample",
    imdb_id="tt0120338"  # Titanic
)

# Find YouTube matches (87.16% accuracy)
results = linker.find_matches_batch(soundtracks)

for result in results:
    if result.best_match:
        print(f"🎵 {result.soundtrack.title}: {result.best_match.url}")

Media Downloader

The Media Downloader module allows you to download actual media files (images, videos, audio) referenced in the Knowledge Graph. It respects rate limits and supports resumable batch downloads.

Download Media for a Single Entity

# Download all media for a movie (images, trailer, soundtrack audio)
python -m media_downloader.download_entity tt0120338

# Download all media for an actor
python -m media_downloader.download_entity nm0000138

# Download only images
python -m media_downloader.download_entity tt0120338 --images-only

# Download only videos (trailers)
python -m media_downloader.download_entity tt0120338 --videos-only

# Download only audio (from YouTube soundtracks)
python -m media_downloader.download_entity tt0120338 --audio-only

Batch Download All Entities

# Download media for all entities in the KG
python -m media_downloader.download_all

# Only movies, only images
python -m media_downloader.download_all --movies-only --images-only

# Only persons (actors, directors)
python -m media_downloader.download_all --persons-only

# Custom rate limiting (slower for sensitive servers)
python -m media_downloader.download_all --delay 5 --entity-delay 10 --batch-delay 120

# Test with limited entities
python -m media_downloader.download_all --max-entities 100

Output Structure

output/
├── tt0120338/              # Titanic
│   ├── images/             # Movie stills and posters
│   ├── videos/             # Trailers (MP4)
│   └── audio/              # Soundtrack tracks (from YouTube)
├── nm0000138/              # Leonardo DiCaprio
│   ├── images/             # Actor photos
│   └── videos/             # Actor-related videos
└── download_progress.json  # Resume tracking

Features

Feature	Description
Resume Support	Progress saved to JSON; interrupted downloads resume automatically
Rate Limiting	Configurable delays between downloads to avoid anti-bot measures
Batch Processing	Process all 50,000+ entities with automatic breaks
Selective Download	Filter by entity type (movie/person) or media type (image/video/audio)

Rate Limiting Defaults

Setting	Default	Description
--delay	2.0s	Delay between individual media downloads
--entity-delay	5.0s	Delay between entities
--batch-size	10	Entities per batch before longer break
--batch-delay	30s	Break duration between batches

---

📥 Accessing Modality Data

IMDB4M follows a linking-over-hosting principle: the knowledge graph stores URIs pointing to media hosted on their original platforms (IMDb, YouTube, Amazon CDN) rather than redistributing raw files. This ensures copyright compliance and keeps the resource lightweight.

There are two ways to obtain the actual media files for research purposes:

Option A: Download from Source (Self-Service)

Use the Media Downloader module included in this repository to retrieve media files directly from their original sources by following the URIs in the knowledge graph:

# Download all media for all entities
python -m media_downloader.download_all

# Download media for a specific movie
python -m media_downloader.download_entity tt0120338

# Download only specific modalities
python -m media_downloader.download_entity tt0120338 --images-only
python -m media_downloader.download_entity tt0120338 --videos-only
python -m media_downloader.download_entity tt0120338 --audio-only

The downloader includes adaptive rate limiting and resume support for multi-day runs. See the Media Downloader section for full usage details.

Note: Source availability depends on the original platforms. Some media may become unavailable over time due to content changes on IMDb or YouTube.

Option B: Request a Confidential Dump

For researchers who require a complete, pre-downloaded copy of the multimodal resource (including images, video frames, and audio files), we can provide a confidential dump for non-commercial academic use only.

To request access, please contact us at: ioannis.reklos [at] kcl [dot] ac [dot] uk

Please include in your request:

• Your name and institutional affiliation
• A brief description of your intended use case
• Confirmation that the data will be used for non-commercial research purposes only

---

📊 Validation & Evaluation

IMDB4M includes a comprehensive validation framework combining SPARQL-based question answering and link verification.

Validation Results

Metric	Value
Overall F1 Score	98.7%
Precision	99.4%
Recall	98.1%
Avg. Levenshtein Similarity	0.993
Query Success Rate	99.3%
YouTube Link Accuracy	87.16%

Running Evaluation

cd QA
python evaluate_qa.py

18 Competency Questions (SPARQL Queries)

ID	Query	Coverage
Q1	Who directed the movie?	100%
Q2	Who wrote the script?	100%
Q3	Who are the actors?	100%
Q4	What is the rating?	100%
Q5	How many ratings?	100%
Q6	What is the plot?	100%
Q7	When was it released?	100%
Q8	What is the runtime?	100%
Q9	What is the Metacritic score?	95.2%
Q10	What are the keywords?	100%
Q11	What is the budget?	95.5%
Q12	What is the trailer?	99.2%
Q13	What is the genre?	100%
Q14	What is the poster?	100%
Q15	Production companies?	100%
Q16	Alternate names?	98.9%
Q17	Content rating?	98.4%
Q18	Images and captions?	100%

---

📹 Video Representation

IMDB4M captures movie trailers as schema:VideoObject entities:

<https://www.imdb.com/title/tt0120338> schema:trailer <https://www.imdb.com/video/vi1740686617> .

<https://www.imdb.com/video/vi1740686617> a schema:VideoObject ;
    schema:name "Official Trailer" ;
    schema:description "A seventeen-year-old aristocrat falls in love..." ;
    schema:duration "PT1M37S"^^xsd:duration ;
    schema:embedUrl <https://www.imdb.com/video/vi1740686617/> ;
    schema:thumbnailUrl <https://m.media-amazon.com/images/M/...jpg> ;
    schema:uploadDate "2023-01-10T18:08:38.447000+00:00"^^xsd:dateTime .

---

🎵 Soundtrack Representation

Detailed soundtrack modeling with performers, composers, and compositions:

<https://www.imdb.com/title/tt0120338/> schema:audio [
    a schema:MusicRecording ;
    schema:name "My Heart Will Go On" ;
    schema:byArtist <https://www.imdb.com/name/nm0001144/> ;  # Céline Dion
    schema:producer <https://www.imdb.com/name/nm0000035/> ;  # James Horner
    schema:recordingOf [
        a schema:MusicComposition ;
        schema:name "My Heart Will Go On" ;
        schema:composer <https://www.imdb.com/name/nm0000035/> ;  # James Horner
        schema:lyricist <https://www.imdb.com/name/nm0421263/>   # Will Jennings
    ]
] .

---

🖼️ Image Representation

Movie stills with captions, dimensions, and entity links:

<https://www.imdb.com/title/tt0120338/mediaviewer/rm4035688192/> a schema:ImageObject ;
    schema:caption "Leonardo DiCaprio and Kate Winslet in Titanic (1997)" ;
    schema:width 2048 ;
    schema:height 1385 ;
    schema:url <https://m.media-amazon.com/images/M/...jpg> ;
    schema:mainEntity <https://www.imdb.com/name/nm0000138/>,  # Leonardo DiCaprio
                      <https://www.imdb.com/name/nm0000701/> . # Kate Winslet

---

🔗 Wikidata Integration

IMDB4M includes owl:sameAs mappings to Wikidata for enhanced interoperability:

<https://www.imdb.com/title/tt0120338> owl:sameAs <http://www.wikidata.org/entity/Q44578> .
<https://www.imdb.com/name/nm0000138> owl:sameAs <http://www.wikidata.org/entity/Q38111> .

Generate mappings:

python create_sameas_mappings.py

---

📈 Knowledge Graph Analysis

# Run comprehensive KG analysis
python analyze_kg.py

Graph Structure Statistics

Metric	Value
Connected Components	1 (fully connected)
Graph Density	5.05 × 10⁻⁵
Average Degree	5.5
Leaf Nodes	38.6% (primarily literals)
Sink Nodes	45%
Hub Nodes (top 1%)	6,656
Max Out-Degree	787 (prolific actor)

Entity Type Distribution

Type	Count
schema:PerformanceRole	232,492
schema:Movie	50,756
schema:ImageObject	36,844
schema:Person	16,994
Blank Nodes	253,369 (38.4%)

Top Predicates (72% of all triples): schema:actor, rdf:type, schema:performerIn, schema:characterName

---

🎯 Applications

IMDB4M enables research across multiple domains:

🎥 Movie Recommendation

• Content-based recommendation using visual style of posters and acoustic features of soundtracks
• Extract audio embeddings from linked YouTube videos
• Temporal visual features from trailers for queries like "find movies with high-paced action sequences and electronic scores"

🔍 Multimodal Question Answering

• Knowledge Graph Question Answering (KGQA) with perceptual grounding
• Multimodal RAG systems answering "Who is the actor shown in this scene, and what other movies have they directed?"
• Complex queries involving reified relations ("Who played character X in movie Y?")

🧩 Multimodal Knowledge Graph Completion

• Link Prediction: Infer schema:genre from poster and plot
• Entity Alignment: Cross-platform alignment via visual similarity of actor portraits
• KG Embeddings: Train multimodal embedding models (e.g., TransE with visual features)

---

🤝 Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

---

📜 License

This project is released under a Creative Commons Attribution-NonCommercial (CC-BY-NC) license to ensure alignment with IMDb's terms of use, which restrict data utilisation to academic and non-commercial settings.

IMDB4M functions strictly as a structural indexing layer rather than a media hosting platform. The resource does not store or redistribute any raw multimedia files—only external URIs that reference the original content hosted on IMDb and YouTube.

---

📄 Citation

If you use IMDB4M in your research, please cite:

@inproceedings{imdb4m2026,
  title={{IMDB4M}: A Large-Scale Multi-Modal Knowledge Graph of Movies},
  author={Reklos, Ioannis and de Berardinis, Jacopo and Simperl, Elena and Mero{\~n}o-Pe{\~n}uela, Albert},
  year={2026},
  note={Under review}
}

Comparison with Related Work

Dataset	Text	Image	Video	Audio	#Entity	#Relation
MKG-W	14,123	14,463	–	–	15,000	169
MKG-Y	12,305	14,244	–	–	15,000	28
TIVA-KG	11,858	11,636	10,269	2,441	11,858	16
KVC16K	14,822	14,822	14,822	14,822	16,015	4
IMDB4M	385,595	37,220	3,983	4,211	660,039	58

---

🙏 Acknowledgments

• IMDb for movie metadata
• Wikidata for entity linking
• Schema.org for vocabulary standards
• YouTube Data API for video linking
• Google Gemini for intelligent matching

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