Provide consistent unified access to data across different systems by using the flexible and semantically precise structure of the knowledge graph model
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:
Improve engagement, discoverability and personalized recommendations for Financial and Business Media, Market Intelligence and Investment Information Agencies, Science, Technology and Medicine Publishers, etc.
Tagarev, A., Tulechki, N., Boytcheva, S.. Comparison of Machine Learning Approaches for Industry Classification Based on Textual Descriptions of Companies. Proceedings of Recent Advances in Natural Language Processing (RANLP) 2019, INCOMA Ltd., 2019, ISSN:2603-2813, http://dx.doi.org/10.26615/978-954-452-056-4_134, pp. 1169-1175. SJR (Scopus):0.143 Q3 (Scopus)