Relevant content can be searched or browsed for by users, but it can also be delivered in the form of a personalized recommendation. A vast amount of the information businesses generate is unstructured, usually in the form of free-text documents, images, videos, or other human-understandable formats that are not simple for machines to process. Taxonomies, ontologies, and knowledge graphs allow for capturing the context, meaning, and business value to help organize both unstructured information and existing data and provide more accurate and richer results in all cases of search, browse, and recommendation.
Taxonomies present serve as a foundation as they are an ideal starting point for building knowledge graphs to support personalization services that improve the user experience by delivering relevant content to users.
This webinar will present a case study of a targeted content recommendation engine, which suggested course relevant course content based on users’ performance on online training material test completion. In addition to showing how the system was designed and created, this webinar presents the more fundamental principles of taxonomy and ontology design as the backbone to the knowledge graph.