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The semantic web made simple more time that is spent in researching and confirming the data, the better the programme will be, but the bigger the budget required. Automating research What the semantic web offers is the ability to collect information from many, many sources automatically. It does this by moving away from the traditional, keyword-based Google-type searching to a new way of exploring links between data, so that the picture grows organically. by David Cole, IPV T here is a new term creeping in to discussions in the broadcasting business, and particularly around those involved in asset management and research projects. The term is “semantic web” and, I suspect, there are a lot of people who are neither completely sure they know what it means, nor trust its relevance in our business. I aim to provide some simple answers to those questions. As for the expression itself, it was coined by Tim Berners-Lee, who since the Olympic opening ceremony we all know was the inventor of the world wide web. He first talked about it in Scientific American as long ago as 2001, describing it as “a web of data that can be processed directly and indirectly by machines”. Putting it simply, it first assumes that you will want to look at multiple sources of information to put together the full picture, and second that some form of machine intelligence will do this information collection, evaluating the sources as it goes to present to the user a prioritised and ordered set of data. If that sounds like a complex and theoretical definition, perhaps a more practical application may help. Take the example of a typical broadcaster. While you may hear 52 | TV-BAY MAGAZINE consultants and asset management specialists talk about bringing all a broadcaster’s information into a single system, in practice that rarely happens. The news department will have its own archive, as will sport. Other departments will also have their own approach to information and asset management. What happens if a documentary-maker is commissioned to make a programme about, say, Usain Bolt? At the research stage they will want to look at what the sports department has to say about him, and see what can be gleaned from the news department. The programme- maker will probably already be engaged with the subject and have both a store of background information and an outline of the story to be told. There will also be an acknowledgement that valuable information may lie outside the broadcaster’s internal resources. Some will be hard and reliable sources, like the records of the Olympic’s and international athletics bodies, and some will be softer sources, ranging from www.usainbolt. com to Wikipedia tweet feeds and gossip sites. Under pressure, and using traditional search techniques, the documentary maker may go to the sources that provide the easiest route to verification. Potentially interesting information may never be discovered or be overlooked because it cannot be positively confirmed from multiple sources. The At IPV we have been in the business of enabling collaborative broadcast workflows for more than 15 years, and our products include asset management systems where they are appropriate. But since 2010 we have had growing success with a product we call Teragator, which is designed to be the platform for semantic web searches. It came about to meet just this application: the need to make sense of federated media asset management, aggregating data from disparate sources in a non-invasive way, and without changing the way that individual departments operate. It has a lot of very clever technology underpinning its data mining, but from a user’s point of view it presents a way to identify and manage complex relational links between assets and information in a simple and readily understandable fashion. You certainly do not have to understand the semantic web to be able to use it. Unlike conventional search engines that match keywords to a predefined schema, Teragator allows you to uncover seemingly unlinked data which is relevant to the story you are trying to tell. It allows data sources to be analysed in a single, uniform application, but modelled with different views to explore the way that parts of the story interlink. Through the analysis of the data and identification of entities, people, places, etc, new links are discovered that were not even on the agenda at the start of the process, enriching editorial  and offering new ideas and even new stories. Use of