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Web-Scale IA using Linked Open Data

About the Talk

March 28, 2014 7:30 AM

San Diego, CA

San Diego, CA

The path ahead for information architecture is less about mapping closed site structures and more about creating findable, social objects of information distributed across the network and consumed on an array of devices.

We’ve heard about modeling adaptive and structured content, and how to markup with metadata to make our own material better exposed to the robots of the web. So what’s next? What do we do when our content model or user research uncovers an appetite for material we can’t hope to fulfill? Can we start to connect our content to that of other providers? Better still, can we use free third-party content and business data to bootstrap our own information architectures?

Let’s journey through the world of Linked Open Data, exposing, sharing, and connecting pieces of data, information, and knowledge. Here we connect real-world things and relationships using common vocabularies to create a single, extensible network of information stretching across an increasingly semantic web. When content providers like the New York Times, Wikipedia, and MusicBrainz each know different facts about a person, place, or thing, these sources can be fused to provide a richer knowledge graph which we can use to build content products more cheaply through crowdsourcing, make our products more findable, or make our content more reusable.

This isn’t the future; it’s current best practice–used by the BBC to build their Music pages, the New York Times to build topic pages, and by Google and Facebook to build their knowledge graphs. Through these examples we’ll offer practical guidance on how to get started using Linked Open Data in your own projects.

The Web has always allowed us to link related documents. Now the path ahead is to link up the underlying information. It might sound a little technical, but if designers are now asked to ‘code’–to work with the native fabric of their medium–shouldn’t IAs similarly have the power to wrangle data at its source? Working with Linked Data is mining and refining the information that flows across the web. Who better to do this than an information architect?

You’ll learn:

  • The advantages of making your content robot-readable and future-friendly

  • How to structure information using a standard framework, making it readable to people and robots

  • How to query data sources, treating the web as one big database

  • Best-practice methods for enhancing your existing content offering with third-party content and data

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