Uncovering connections within and across datasets in the financial markets
The sheer volume and variety of data are prompting enterprises to identify automated solutions for deriving insights and making decisions. One way would be to apply natural language processing (NLP) and knowledge graph technology to datasets. According to Thomson Reuters, this will help label, tag, and present even difficult to manage textual datasets, and uncover previously hidden connections and insights.
Thomson Reuters is committed to enabling their customers to stay competitive by understanding and adopting new technology such as graph databases. To further this goal, the company invited senior data scientists from some of its biggest customers to Imperial College, London. The aim was to discuss the practical application of knowledge graphs and NLP for financial markets. In addition, discover how to encourage wider adoption of the knowledge graph technology.
Click here to explore use cases for knowledge graph technology, learn from the experiences of other enterprises, and gather insights on managing the enterprise data warehouse provided by Thomson Reuters.