At Airbnb, making sense of mountains of data isn’t just a nice-to-have—it’s mission-critical. Whether you’re a guest searching for the perfect getaway or an employee launching a new feature, finding the right information quickly makes all the difference. That’s where Airbnb’s contextual knowledge graphs come in—a powerful, evolving web of connected data that’s transforming how we understand and retrieve knowledge.
Why Knowledge Graphs?
Traditional databases are great for storing facts, but not so great at connecting the dots. As Airbnb has grown, so has the complexity of our data. Knowledge graphs bridge these gaps by modeling relationships—so we can link “listing has amenity,” “host provides experience,” and more, into a single, searchable network.
What Does This Mean in Practice?
- Richer Search & Recommendations: Surfacing smarter, more relevant results (for guests and employees alike).
- Business Agility: New data sources and concepts can be integrated on the fly as Airbnb evolves.
- Context at Scale: Not just answering “what,” but “why” and “how” different data points relate.
Behind the Scenes: Engineering Highlights
- Graph Construction: Automated pipelines extract and connect data from across the company.
- Contextualization: The graph doesn’t just store facts—it understands context, weaving together the full story behind each node.
- Lightning-Fast Retrieval: Efficient algorithms make knowledge accessible in real-time, powering both internal tools and guest features.
Why Taxonomy is the Secret Sauce
Taxonomies give us a shared language. Imagine consistent labels for everything from “wifi” and “hot tub” to “villa” and “treehouse.” Airbnb’s taxonomy is baked directly into the knowledge graph, creating:
- Standardized Data: Consistency across listings, search, and analytics.
- Discovery Superpowers: Advanced filters so guests can find exactly what they’re dreaming of.
- Deeper Insights: A rock-solid foundation for analytics and trend-spotting.
In the Graph:
- Nodes = taxonomy concepts (“Hot Tub,” “Pet-Friendly”)
- Edges = relationships (“has_amenity”, “is_a_type_of”)
- Rich Metadata = multilingual labels, definitions, and context for every concept
This supports powerful, hierarchical queries like “Show me all listings with any wellness-related amenity”—making Airbnb’s data more useful than ever.
Keeping the Taxonomy Clean: Fighting Fragmentation
But here’s the twist: as teams and features multiply, so do the chances of duplication and confusion. (Is “Eco Lodging” the same as “Sustainable Stays”?)
A healthy taxonomy is non-negotiable. Airbnb tracks:
- Semantic Duplication Rate
- Orphaned Node Rate
- Legacy Rate
- Cross-Team Overlap
AI to the Rescue: Smarter Semantic Consolidation
To keep things tidy, Airbnb uses an AI-powered cleanup crew:
- Semantic Embeddings: Every node gets mapped in semantic space (using OpenAI Ada, Sentence-BERT, etc.).
- Clustering: Algorithms group together concepts that mean the same thing, even if named differently.
- LLM Validation: Large Language Models confirm duplicates and suggest canonical names.
Real Example:
- “Pet-Friendly Stays” & “Dog-Friendly Accommodations” → 0.85 similarity, 92% data overlap → likely merge.
- “Remote Work Retreats” vs “Work from Anywhere” → Only 17% overlap → probably not duplicates.
Even better, Airbnb cross-checks the real-world data beneath each node: if 90% of listings overlap, merge away. If overlap is low, flag it for human review.