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.
The proper functioning of the European Energy Market requires exchanges of different data from numerous parties. Maintaining and improving the reliability of the European power system also requires significant exchange of data to perform necessary analysis. Common Information Model (CIM) is used at the core of the exchanges and it is an important ingredient for building Energy Knowledge Graphs.
In this session, Vladimir Alexiev, Chief Data Architect at Ontotext and Chavdar Ivanov, Managing Director at gridDigIt explain the role and benefits of using CIM and suggest directions towards Energy Knowledge Graphs. They also showcase how different products such as GraphDB and knowledge of semantic technologies can support the Energy industry.