I’m at the Semantic Technology & Business conference in NYC. Matthew Degel, Senior Vice President and Chief Architect at Viacom Media Networks is talking about “Modeling Media and the Content Supply Chain Using Semantic Technologies.” [NOTE: Liveblogging. Getting things wrong. Mangling words. Missing points. Over- and under-emphasizing the wrong things. Not running a spellpchecker. You are warned!]

Matthew says that the problem is that we’re “drowning in data but starved for information” Tere is a “thirst for asset-centric views.” And of course, Viacom needs to “more deeply integrate how property rights attach to assets.” And everything has to be natively local, all around the world.

Viacom has to model the content supply chain in a holistic way. So, how to structure the data? To answer, they need to know what the questions are. Data always has some structure. The question is how volatile those structures are. [I missed about 5 mins m– had to duck out.]

He shows an asset tree, “relating things that are different yet the same,” with SpongeBob as his example: TV series, characters, the talent, the movie, consumer products, etc. Stations are not allowed to air a commercial with the voice actor behind Spoongey, Tom Kenney, during the showing of the SpongeBob show, so they need to intersect those datasets. Likewise, the video clip you see on your setup box’s guide is separate from, but related to, the original. For doing all this, Viacom is relying on inferences: A prime time version of a Jersey Shore episode, which has had the bad language censored out of it, is a version of the full episode, which is part of the series which has licensing contracts within various geographies, etc. From this Viacom can infer that the censored episode is shown in some geography under some licensing agreements, etc.

“We’ve tried to take a realistic approach to this.” As excited as they are about the promise, “we haven’t dived in with a huge amount of resources.” They’re solving immediate problems. They began by making diagrams of all of the apps and technologies. It was a mess. So, they extracted and encoded into a triplestore all the info in the diagram. Then they overlaid the DR data. [I don’t know what DR stands for. I’m guessing the D stands for Digital, and the R might be Resource]] Further mapping showed that some apps that they weren’t paying much attention to were actually critical to multiple systems. They did an ontology graph as a London Underground map. [By the way, Gombrich has a wonderful history and appreciation of those maps in Art and Representation, I believe.]

What’s worked? They’re focusing on where they’re going, not where they’ve been. This has let them “jettison a lot of intellectual baggage” so that they can model business processes “in a much cleaner and effective way.” Also, OWL has provided a rich modeling language for expressing their Enterprise Information Model.

What hasn’t worked?

• “The toolsets really aren’t quite there yet.” He says that based on the conversations he’s had to today, he doesn’t think anyone disagrees with him.

• Also, the modeling tools presume you already know the technology and the approach. Also, the query tools presume you have a user at a keyboard rather than as a backend of a Web service capable of handling sufficient volume. For example, he’d like “Crystal Reports for SPARQL,” as an example of a usable tool.

• Visualization tools are focused on interactive use. You pick a class and see the relationships, etc. But if you want to see a traditional ERD diagram, you can’t.

• Also, the modeling tools present a “forward-bias.” E.g., there are tools for turning schemas into ontologies, but not for turning ontologies into a reference model for schema.

Matthew makes some predictions:

• They will develop into robust tools

• Semantic tech will enable queries such as “Show me all Madonna interviews where she sings, where the footage has not been previously shown, and where we have the license to distribute it on the Web in Australia in Dec.”