There is a growing interest in knowledge graphs to organize information and make it findable in organizations with large amounts of data and content. Unlike other data technologies, a knowledge graph has a structure that is typically based on a taxonomy and ontology, and thus should involve information architects. Knowledge graphs also have more benefits than information findability, including discovery, analysis, and recommendation. Knowledge graphs bring together content and data.
An enterprise knowledge graph involves a change in thinking about information and its access. Instead of designing information architecture in individual applications, an intranet, or website, a knowledge graph extracts data and links to content that exists in multiple different applications and repositories, linking them in a web or graph-like structure by means of customized, semantic relationships. This session explains what a knowledge graph is and how it is built, with a focus on ontologies. It also presents examples and standards.
About the speakers
Heather Hedden has been a taxonomist for over 26 years in various organizations and as an independent consultant. She currently works for professional services team of Semantic Web Company (vendor of PoolParty Semantic Suite software) and previously worked as a taxonomist at Cengage Learning, Viziant, First Wind, and Project Performance Corp. Heather has designed and developed, taxonomies, thesauri, ontologies, and metadata schema for internal and externally published content, including websites, intranets, and content management systems. She has given workshops on taxonomy creation at numerous conferences and as corporate training. Through Hedden Information Management she also teaches an online course in taxonomy creation. Heather is author of The Accidental Taxonomist, 2nd ed.