Today’s organizations expect the best of what the web has to offer. Savvy knowledge workers want the findability and discoverability of Amazon’s recommendation engines and the intuitive usability of Google’s search rankings and knowledge graphs. Technology has finally caught up to allow organizations to obtain the benefit of such solutions.

As organizations implement the next generation of knowledge management, traditional search efforts have merged into a fusion of recommendation, analytics, and question-answering machines. The holy grail of cognitive solutions is a combination of robust, customizable IT architectures, and information models shaped in an agile way.

Our presentation will:  

• Explain how ontologies serve as a framework for enterprise knowledge graphs;
• Show how a semantic layer complements traditional information models, while making the semantics of metadata available and accessible;
• Demonstrate how semantic knowledge models can be used as a basis for text mining and identifying context-based recommendations;
• Show how Enterprise Knowledge Graphs play a fundamental role in this architecture, while bridging the gap between unstructured and structured data.

We will use a real-world production example of a robust, relevant recommendation engine for a large international development bank.