Being Dialogic With A Knowledge Graph

To live up to the expectations of people  interacting with and in data-rich platforms, we need to serve the most relevant information and cater for a seamless knowledge discovery experience. In parallel, we have to deliver content in an immersive and efficient way. Both these needs are underpinned by the need to deal with large volumes of data and the complexity of the relationships between the data pieces.

To make content engaging and easily accessible for users on data-rich platforms, we are to rethink our content as a weave of messaging and data pieces. We also have to think about how the concepts our content is woven of, how they relate to each other and how they contribute to the knowledge we exchange and build with our communication and products.

To cite Apple’s Knowledge Navigator’s designer Hugh Dubberly:

“We used to think in terms of org charts (a tree showing who works for whom); now we think in terms of social networks (webs showing the many ways people are connected). We used to think of the “tree of knowledge” (a taxonomy classifying everything we know); now Google is building a “knowledge graph” (a web defining relationships between concepts). Increasingly, the data economy requires us to think in terms of networks and relationships.” ^[1]

What Dubberly is referring to is Google’s Knowledge Graph  – we already mentioned that this is Google’s database of billions of facts^[2] about people, places, and things, allowing their systems to “discover and surface publicly known, factual information when it’s determined to be useful”.

What I want us to pay attention to here is that while the term “knowledge graph” did gain popularity after Google’s introduction, their technical creation was only a blink in the long history of how computer science has been tackling the challenges of knowledge discovery, storage and navigation. Challenges that we as content people are now also seeking solutions for. And challenges that recently have been tackled with repositories of structured data, called knowledge graphs.

In this last part I want to show you around my world where web content challenges can and are met with a knowledge graph – a technological assemblage that supports a wide range of applications – from web search to personal assistants. It is a world where marketing communications are created, executed and governed in a knowledge management fashion, through a knowledge graph.

In Chapter 10: Wait, What The Heck Is A Knowledge Graph? we will see what a knowledge graph is, how it is related to the Semantic Web, what marketing communications needs it can potentially meet and how such a technological assemblage can make our content meaningful and dialogic.

In Chapter 11: It’s Time For Smarter Marketing: A Conceptual Model For A Marketing Communications Knowledge Graph I will introduce you to my model of a knowledge graph serving as an enabler of a communicative continuum.  We will see why and how a knowledge graph can serve marketing communications.

In Chapter 12 Knowledge Graphs Powering Content Experiences  we will look at living knowledge graphs to end on a positive note for the future of marketing communications on the Web. We will see examples of knowledge graphs powering content experiences to keep us motivated on the long road of approaching the Web and its contents as our own shared space where we need to cultivate relationships on both dialogic and data level.