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.
Knowledge graphs have become a popular trend in the representation of complex data, metadata and content. Search and graph exploration are key tools for successfully utilizing knowledge graphs.
In this talk, Tomas Kovachev, Software engineer lead at Ontotext, explains why graph path search is a computationally expensive task and presents our graph path search implementation. He compares how the different RDF and property graph databases implement it and dives into how GraphDB extends the SPARQL 1.1 standard to fully support all significant graph path search use cases.