Semantic Data Catalog: Ontology-Driven Data Discovery with OWL2Vec and Vector Search

The Data Discovery Challenge

Data is the fuel on which modern organizations depend for their operations. Data catalogs are central to managing and utilizing data assets, providing the ability to search for data based on asset names, metadata, and related business terms. However, most data catalogs rely on string matching using frameworks like Lucene or Elasticsearch.

This approach has a fundamental limitation: it lacks understanding of the meaning and relationships of concepts in the catalog. This leads to missing useful search hits on data assets, directly impacting organizational productivity and data governance.

With the rise of Data Mesh, where data products are organized in domains, this search challenge becomes even more pronounced. Organizations need a smarter approach to data discovery.

What is a Semantic Data Catalog?

A Semantic Data Catalog combines traditional data catalog capabilities with semantic search:

Semantic Data Catalog = Data Catalog + Semantic Search

It is an intelligent catalog of data assets that automates sharing common meanings across data silos and provides a means to define hierarchies and relationships featuring semantic reasoning. Think of it as an AI-enabled knowledge encyclopedia for your organization.

Why Semantic Search Matters

A semantic search engine operates on semantic context rather than just keywords. It understands:

• The meaning of search queries
• The relationships between entities
• The knowledge structure of your domain

This is achieved through Knowledge Graphs - semantic databases where information is structured to create knowledge from information. Entities (nodes) are related to each other via edges, provided with attributes, and placed in semantic context through ontologies.

Understanding Ontologies

At the heart of semantic data catalogs lies the ontology - a formal representation of knowledge as a set of concepts within a domain and the relationships between those concepts.

An ontology can be expressed as a 5-tuple:

Ontology = <C, R, F, I, A>

Where:

• C (Concepts/Classes): The main formalized elements of the domain with specific properties
• R (Relationships): Links between concepts representing the ontology structure (taxonomic or non-taxonomic)
• F (Functions): Elements that calculate information from other elements
• I (Instances/Individuals): Representations of main objects within the domain
• A (Axioms): Restrictions, rules, and logical definitions governing relationships between ontology elements

The Role of Semantic Reasoners

A semantic reasoner infers logical consequences from a set of asserted axioms in an ontology. It provides automated support for:

• Classification: Organizing data based on semantic relationships
• Querying: Finding relevant information based on meaning
• Validation: Detecting logical inconsistencies in concept models
• Knowledge Inference: Deriving new knowledge from existing facts

Popular reasoners include HermiT, ELK, Ontop, Pellet, and jcel.

Semantic Data Catalog Architecture

The architecture leverages ontologies, ontology embeddings, and vector search to improve data discovery:

Architecture Steps

1. Ontology Catalog: Store ontologies for each data asset, defining concepts, relationships, and semantic structures
2. Embedding Generation: Train an OWL2Vec model on the catalog to generate numerical vectors capturing knowledge and relationships
3. Vector Index: Load embeddings into a vector search engine (FAISS) for efficient similarity search
4. Semantic Search: Convert user queries to embeddings and find the most semantically relevant concepts

OWL2Vec: Ontology Embeddings

OWL2Vec* is a powerful embedding technique specifically designed for OWL ontologies. Based on research from Oxford University, it creates numerical representations that capture:

• Class hierarchies and inheritance relationships
• Property restrictions and domain constraints
• Logical axioms and semantic rules
• Instance relationships and data properties

How OWL2Vec Works

The embedding quality depends on:

• The language model configuration
• Ontology structure and richness
• Training hyperparameters

It's recommended to experiment with different configurations and evaluate using metrics like MRR (Mean Reciprocal Rank) and Hit Rate.

Vector Search with FAISS

Once embeddings are generated, they're loaded into a vector search engine. This project uses FAISS (Facebook AI Similarity Search) for efficient similarity-based search.

Understanding Vector Databases

Vector databases store data as high-dimensional vectors - mathematical representations of features or attributes. For text data:

# Similar concepts have similar vector representations
dog = [1.6, -0.3, 7.2, 19.6, 3.1, ..., 20.6]
puppy = [1.5, -0.4, 7.2, 19.5, 3.2, ..., 20.8]

The key advantage: fast and accurate similarity search based on semantic meaning rather than exact matches.

Similarity Metrics

Vector search uses distance metrics to find similar items:

Search Algorithms

For efficient retrieval at scale:

• k-Nearest Neighbors (kNN): Exact but slow for large datasets
• Approximate Nearest Neighbors (ANN): Fast with minor accuracy trade-off
  • ANNOY: Tree-based method
  • FAISS: Clustering-based (used in this project)
  • LSH: Locality-sensitive hashing
  • SCANN: Vector compression with quantization

Benefits of Semantic Data Catalogs

1. Increased Search Accuracy

By understanding the meaning of data concepts and their relationships, semantic catalogs provide more accurate and relevant search results.

Performance can be measured using:

2. Smarter Data Governance

With ontology-driven knowledge graphs providing clear understanding of data asset relationships, organizations gain a robust framework for managing and utilizing their data.

3. Increased Efficiency

Semantic search makes "searching for data" easier, enabling contextual knowledge-driven "searching in data" within your lakehouse. This directly improves productivity and operational efficiency.

Ontology Evaluation Criteria

For successful implementation, consider these evaluation dimensions:

Implementation Example

Using the Pizza Ontology from Protege Stanford as a sample, the system enables queries like:

# Sample semantic queries
query = 'margherita and onion'
query = 'mozzarella'
query = 'spicy pizza toppings'

The semantic search understands that:

• "Margherita" relates to specific toppings and pizza types
• "Mozzarella" is a cheese, which is a topping
• "Spicy" relates to certain ingredients with heat properties

This contextual understanding delivers results that string matching would miss.

Production Considerations

Before deploying a semantic data catalog, consider:

1. Model Training: Embedding quality depends on ontology model training and accuracy maintenance
2. Continuous Evaluation: Regularly assess embedding effectiveness
3. Index Updates: Keep vector indexes synchronized as the catalog evolves
4. Zipf's Law: Excessive metadata can negatively affect search - focus on meaningful concepts

Alternative Vector Databases

While FAISS provides excellent local performance, production deployments might consider:

• Pinecone: Fully managed vector database
• Weaviate: Open-source with hybrid search
• Milvus: Scalable similarity search
• Qdrant: High-performance with filtering
• Vespa: Combined search and recommendation

Conclusion

Semantic Data Catalogs represent the future of data discovery. By combining:

• Ontologies for knowledge representation
• OWL2Vec for semantic embeddings
• Vector Search for intelligent retrieval

Organizations can transform their data catalogs from simple keyword matchers into intelligent, context-aware knowledge systems that truly understand the meaning behind data assets.

The key insight: A Data Catalog is a social graph of data. Plain search doesn't help - knowledge graph-driven approaches based on ontology are essential for effective data discovery in the modern data mesh era.

Explore the complete implementation at semantic-data-catalog on GitHub.