Making Sense of Text and Data
Provide consistent unified access to data across different systems by using the flexible and semantically precise structure of the knowledge graph model
Interlink your organization’s data and content by using knowledge graph powered natural language processing with our Content Management solutions.
Implement a Connected Inventory of enterprise data assets, based on a knowledge graph, to get business insights about the current status and trends, risk and opportunities, based on a holistic interrelated view of all enterprise assets.
Quick and easy discovery in clinical trials, medical coding of patients’ records, advanced drug safety analytics, knowledge graph powered drug discovery, regulatory intelligence and many more
Make better sense of enterprise data and assets for competitive investment market intelligence, efficient connected inventory management, enhanced regulatory compliance and more
Connect and model industry systems and processes for deeper data-driven insights in:
- Manufacturing
- Automotive Industry
- Energy
- Building Automation
- Infrastructure
- Aerospace & Defense
Improve engagement, discoverability and personalized recommendations for Financial and Business Media, Market Intelligence and Investment Information Agencies, Science, Technology and Medicine Publishers, etc.
Unlock the potential for new intelligent public services and applications for Government, Defence Intelligence, etc.
Connect and improve the insights from your customer, product, delivery, and location data. Gain a deeper understanding of the relationships between products and your consumers’ intent.
Link diverse data, index it for semantic search and enrich it via text analysis to build big knowledge graphs.
Organize your information and documents into enterprise knowledge graphs and make your data management and analytics work in synergy.
Integrate and evaluate any text analysis service on the market against your own ground truth data in a user friendly way.
Turn strings to things with Ontotext’s free application for automating the conversion of messy string data into a knowledge graph.
Clinical Text Classification to SNOMED CT Codes Using Transformers Trained on Linked Open Medical Ontologies
Anton Hristov, Petar Ivanov, Anna Aksenova, Tsvetan Asamov, Pavlin Gyurov, Todor Primov, and Svetla Boytcheva. 2023. Clinical Text Classification to SNOMED CT Codes Using Transformers Trained on Linked Open Medical Ontologies. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 519–526, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.