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.
Nikola Tulechki, Knowledge Engineer at Ontotext, demonstrates a data cleaning, reconciliation and RDF-ization workflow with OntoRefine aiming to enhance an existing knowledge graph with new information contained in tabular data.
You can watch him clean up a tabular dataset with OntoRefine, set up a project-specific reconciliation service on top of an existing knowledge graph and use it from the OntoRefine project to match strings in the source data to entities in the knowledge graph. The enriched data will then be converted to RDF, using the Visual RDF Mapper, and imported into the existing knowledge graph in the GraphDB repository.