Jennifer Zaino
SemanticWeb.com Contributor

Mainstream businesses increasingly are taking the semantic web seriously.

Consider the U.K. Governments Technology Program’s Market Blended Insight (MBI) project. While funded under that program as a three-year applied research project, it includes as part of its consortium the marketing departments of ParcelForce Worldwide, British Gas Business, AXA, Clydesdale and Yorkshire Bank (NAGE), 3M and pH Group. Having these partners on board helps ensure that the work it is doing to help companies improve their marketing activities has application in real-world scenarios.

The challenge the project is seeking to solve relates to a better and lower-cost way of creating market intelligence out of the innumerable data sets that characterize most enterprise environments – and utilizing other data sets, as well. The project, according to a paper presented last year at the 7th International Semantic Web Conference, is building an industrial scale prototype that aims to provide UK business users with dynamic, relevant insight into their target markets and allows them to make informed decisions on a potential client’s propensity to buy. MBI’s third prototype, and the most advanced proof-of-concept version, is expected to be delivered in January.

“Companies look at corporate customer data sets, databases, profiles of users and consumers of products and try to come up with new segmentations of markets and how to meet needs,” says Nigel R. Shadbolt, professor of Artificial Intelligence (AI) and Deputy Head (Research) of the School of Electronics and Computer Science at the University of Southampton and the CTO of identity-protection company Garlik, whose solution is based on semantic web technology.

Shadbolt is one of the main investigators on the MBI project. Generally, that work involves a lot of bespoke analysis using proprietary methods, and the help of internal or outsourced market analysis professionals to refactor, re-analyze and re-represent large amounts of data, which all adds up to higher costs.

“The notion of MBI was that it would seek to take lots of information sets that are increasingly becoming available, not just the ones companies could use off their business networks, but the data starting to appear on the open web and elsewhere and blend them together to give a more powerful set of tools to identify new business opportunities,” Shadbolt says.

The MBI project has no plans to do away with the need for skilled market analyzers who are good at sniffing out interesting correlations or new ways of factoring products or anticipating requirements, he says, but using semantic web technologies and linked data could help close many of the gaps that exist across information sets and lower costs, in part by making it easier and more flexible to reuse data.

In that respect, it’s a power tool for the analysts. “What is going on is building tools to do things like multi-integration and refactoring of information according to ontologies you can redescribe in a variety of ways to explore how information falls out,” he says.

Re-factoring data around new needs or users

Often business opportunities are lost because of the inability to factor in new and unanticipated or unknown sets of data. Take the 2012 London Olympics – it’s not every day that a country takes on the work of building facilities for such an event from the ground up, which creates challenges for businesses around anticipating and predicting and mapping data around requirements (such as construction materials) to help the UK complete the task.

“New sources of opportunity like the London Olympics can generate new business opportunities in the areas of supply chain, but they don’t fit in with traditional ways of organizing or categorizing material,” he says. Ontology-driven data extraction and user-view visualization based on semantic web technology provides powerful ways of breaking out of fixed subsets to re-factor data around new types of needs or classes of users.

This summer the MBI project also began using the recently released open source version of Garlik’s RDF triple store to represent a whole range of integrated information sets and perform dynamic analysis. It had previously been using a licensed version of Garlik’s technology. The open source version, available for download, is highly scalable (60 billion triples), can run locally on a laptop or desktop, and has a fully compliant SPARQL interface, Shadbolt says.

He sees opportunities in the open source triple store for a range of start-ups to take advantage of the data it aggregates from across the open web and various proprietary databases to drive their own highly scalable semantic web-enabled solutions.

“If you had to go to venture capital money build another triple store, that’s where you hit real problems,” he says. “There are a group of people interested in extending it, and it brings the cost of entry down to companies who want to do something where Jena or Sesame aren’t big enough or scalable enough for their needs. It lets them make a choice as to what suits them and that generally helps the semantic web community. I think Garlik’s view is that where there is an emerging development community, it will be happy to offer advice and guidance in the style of MySQL.”

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