Successful knowledge management, risk management and process automation initiatives use Knowledge Graphs and Text Analytics for data discovery to extract value from documents and transform them into actionable insights and data. Knowledge Graphs aka Semantic Networks are the bedrock of an organization’s Information Architecture - modeling an organization’s products, services and people. Such semantic approaches, leveraging Natural Language techniques, have been the backbone of Text Analytics. Recently, advances in Machine Learning (ML) are augmenting such traditional approaches to create Hybrid AI. Attend the next Earley Information Science webinar to understand the key steps to set up your next data discovery initiative for success using the latest methodology and technologies. We’ve partnered with Expert.AI, a recognized leader in document-oriented text analytics platforms to explain the technical and methodological advances that enable better data discovery.

The need for accurate data to support digital transformation and the emergence of new restrictions on the use of third-party data have prompted many companies to focus their attention on first party data.

Knowledge graphs are pretty hot these days. While this class of technology is getting a lot of market and vendor attention these days, it is not necessarily a new construct or approach. The core principles have been around for decades. Organizations are becoming more aware of the potential of knowledge graphs, but many digital leaders are puzzled as to how to take the next step and build business capabilities that leverage this technology.