Session type: Main Conference Talk

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.

Can a company that has ignored previous knowledge management systems and chooses to store key information on sticky notes, peoples' heads or on personal computers ever be motivated to change? Can people learn to trust digital content?

Our small team built a holistic and collaborative solution for knowledge management. We created a 10-step process to ensure that every pain point and possible stumbling block could be addressed. Here we share this process, which includes our information architecture, content and communication strategies.

While our journey has had challenges (like highly change-resistant departments), the overwhelming sentiment has been one of gratitude. One employee, who in a pre-survey said that she felt ‘lost in a jungle', declared that she had been ‘found after being lost in the jungle' in a post-solution survey. Our team have been buoyed by the meaning and purpose of doing this work. In this talk, I will take you through our process and lessons learned along the way.

Using clear language is an ethical imperative in today's world. Learn why, and see how you can ensure your communications are clear by following the eight guidelines for writing with plain language.

Whether we're talking about design, content, search, or engineering, we all need an information model that captures what the user needs and cares about. But we don't all need the same parts, or even start in the same place. I'll look at information modeling approaches I've encountered across programming, design, content architecture, and SEO teams, and the priorities they serve. Then I'll share how we can integrate these approaches to align cross-discipline work, reduce conflicts, and ensure a consistent end-to-end customer experience. An integrated information model ensures that our user experience, search experience, and content architecture stays aligned as our users needs and business problems change and evolve.