Bitext NAMER: Slashing Time and Costs in Automated Knowledge Graph Construction

The process of building Knowledge Graphs is essential for organizations seeking to organize, structure, and extract actionable insights from their data. However, traditional methods of constructing Knowledge Graphs are often slow, expensive, and complex, requiring significant expertise and manual effort. Bitext NAMER changes the game by automating key steps in the Knowledge Graph creation process, making it faster, more cost-effective, and accessible for businesses of all sizes. 

The Knowledge Graph Creation Workflow Simplified 

The process of constructing a knowledge graph involves multiple stages, including ontology or taxonomy creation, entity extraction, relationship mapping, and integration of structured and unstructured data. Traditionally, this process required extensive manual effort from domain experts and data engineers. Bitext NAMER automates key components of this workflow: 

1. Ontology and Taxonomy Development: While manual ontology creation can take weeks or months, Bitext NAMER simplifies this by providing pre-built dictionaries with over 100,000 entities per language and customizable annotated corpora. These resources serve as the foundation for creating domain-specific ontologies.
2. Entity Extraction: Bitext NAMER identifies 20 types of entities (e.g., people, organizations, locations) with over 95% accuracy across multiple languages. This eliminates the need for manual tagging or annotation while ensuring high-quality data for the KG.
3. Relationship Mapping: The tool detects semantic relationships between entities in real time, enabling the automatic creation of connections within the knowledge graph.
4. Data Integration: By processing both structured and unstructured data from diverse sources, Bitext NAMER ensures seamless integration into existing knowledge frameworks.

This automation reduces the time required to construct a knowledge graph from months to days or even hours, depending on the complexity of the data. 

Time and Cost Efficiency 

The use of Bitext NAMER significantly reduces the time and cost associated with knowledge graph construction: 

• Time Savings: Manual KG construction typically requires 200-300 hours for domain-specific projects. With Bitext NAMER, this can be reduced by up to 90%, allowing completion in as little as 15-25 hours.

• Cost Reduction: Automating entity extraction and relationship mapping eliminates the need for large teams of annotators or ontology engineers. This translates into cost savings of up to 70%, particularly for organizations processing large volumes of text across multiple languages.

For example, a financial services company using Bitext NAMER to build a KG for market intelligence could process thousands of documents daily without incurring the high costs associated with manual efforts. 

The Challenges of Multilingual NER and Its Importance for Global Knowledge Graphs 

Global enterprises often operate in multilingual environments, necessitating NER solutions that: 

• Handle linguistic diversity and nuances.

• Maintain consistency across languages.

• Address region-specific variations, such as named entity formats and cultural context.

Failure to address these complexities can lead to fragmented KGs, diminishing their utility and reliability. 

Technical Performance Highlights 

Bitext NAMER’s technical capabilities are optimized for enterprise-scale KG construction: 

• Processing Speed: Up to 100KB of raw text per second per CPU core.

• Multilingual Support: Covers over 20 languages natively (e.g., English, Spanish, French) with dictionaries available in 77 languages.

• Entity Coverage: Recognizes diverse entity types such as people, places, companies/brands, account numbers, and phone numbers.

• Deployment Flexibility: Available as an on-premise SDK or via SaaS API.

These features make it possible to handle complex datasets across industries such as finance, e-commerce, and cybersecurity. 

Applications in Knowledge Graph Automation 

The automation enabled by Bitext NAMER has transformative applications in various domains: 

1. Semantic Systems: Enhances search engines by creating semantic relationships between structured and unstructured data.
2. Financial Intelligence: Identifies key entities like accounts and transactions to build real-time market intelligence systems.
3. E-commerce: Recognizes brands and products to create recommendation systems based on customer behavior.
4. Cybersecurity: Detects suspicious patterns by connecting disparate datasets into unified graphs.