Turn unstructured text into structured, actionable intelligence
Extract, enrich, and connect entities from complex scientific and technical documents using advanced, ontology-driven Named Entity Recognition (NER) capabilities powered by Dimensions.
Unlock deeper insights across publications, patents, clinical data, and internal documents with precision, scale, and context.
Unstructured data is holding your organization backAcross enterprise environments, critical knowledge is buried in: – Scientific publications and patents – Internal reports and documents – Clinical and regulatory data – PDFs, tables, and even images Traditional Natural Language Processing approaches often fall short: – Generic models lack domain specificity – Entity recognition lacks context and precision – Manual curation is slow, costly, and unscalable As a result, valuable insights remain inaccessible, disconnected, and underutilized. |
Go beyond basic NER with ontology-driven semantic enrichment
Dimensions delivers advanced Named Entity Recognition capabilities designed for complex, domain-specific data.
Built on proven OntoChem technology and integrated within the Dimensions ecosystem, this approach combines:
- Ontology-based entity recognition
- Machine learning and semantic methods
- Context-aware enrichment and relationship extraction
Transform raw text into structured, connected knowledge—ready for downstream analytics, AI, and decision-making.
Key capabilities
Domain-specific entity recognition at scale
Identify and extract entities with high precision across specialized domains, including:
- Chemicals and compounds
- Genes and proteins
- Diseases and biomarkers
- Drugs and clinical concepts
- Organizations, companies, and more
Context-aware understanding (not just keyword matching)
Move beyond surface-level extraction:
- Disambiguate terms and resolve homonyms
- Detect abbreviations and hierarchical relationships
- Recognize entities within their scientific context
Delivering significantly higher precision than generic NLP approaches.
Semantic enrichment with structured outputs
Each extracted entity is enriched with:
- Classifications and attributes
- Links to external data sources
- Ontological relationships
Output structured, machine-readable data ready for integration into knowledge graphs and analytics pipelines.
Relationship extraction for deeper insights
Go beyond identifying entities—understand how they connect:
- Enable high-precision discovery and analysis
- Extract relationships (e.g., drug–disease, protein–interaction)
- Generate structured triplets and graph-ready data
Built for real-world enterprise data
Process complex, heterogeneous data sources, including:
- Scientific publications and patents
- Internal documents (Word, PDF, XML)
- Tables and structured data
- Images and scanned documents (via OCR and image analysis)
Unlock knowledge wherever it exists.o knowledge graphs and analytics pipelines.
Hybrid AI approach: precision + scalability
Combine:
- Machine learning for adaptability and scale
- Semantic and rule-based methods for precision and control
Achieve high-quality extraction while reducing cost and time to deployment.
Use cases
Accelerate research and discovery
Extract and connect insights across global research and internal data to support hypothesis generation and validation.
Automate document processing and compliance
Reduce manual review time by structuring large volumes of regulatory, legal, or technical documents.
Enhance search, knowledge management, and AI applications
Power:
- Semantic search
- Knowledge graphs
- AI and LLM-based applications
With enriched, structured data.
Enable advanced analytics and decision-making
Transform unstructured text into data that supports:
- Trend analysis
- Risk identification
- Strategic planning
Ready to unlock the value of your unstructured data?
Whether you’re building AI models, improving search, or accelerating research workflows, our team can help you apply advanced entity recognition and semantic enrichment to your specific use case.
These capabilities are delivered as part of the Dimensions ecosystem and may be accessed via the Dimensions Knowledge Graph or through tailored solutions based on your requirements.
