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. To better organize this unstructured information and to better describe content such as products and documents, many organizations have implemented hierarchical metadata structures called taxonomies. Taxonomies are a common solution in knowledge management to capture the context, meaning, and business value to help organize both unstructured information and existing data.
Taxonomies present an excellent opportunity 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. In this presentation, Connor Vilenio from Enterprise Knowledge will share his experiences with a basic approach to enhancing a taxonomy’s underlying graph model to support a recommendation system that suggests relevant content from a semantic model of the organization’s understanding of semantic relevancy.
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Senior Data Management Consultant
Connor Vilenio is a senior Data Management Consultant at Enterprise Knowledge with experience designing and delivering data products, services, & integration pipelines, advanced metadata models including ontologies and knowledge graphs, information management strategies & roadmaps and Agile project management solutions to senior stakeholders in the public and private sector. He has special experience supporting NoSQL data structures and serverless architectures to manage enterprise-scale data flows and use cases. He applies this expertise to help clients better translate their pressing business challenges into innovative data solutions and architectures, driving process automation, business intelligence insights, and overall more efficient capitalization of business knowledge.