Since first outlined by Tim Berners-Lee in 2001 the Semantic Web has been eagerly discussed and researched in academic circles and was widely expected to be the next generation of the Web. However, with the explosion of social networking and collaboration on the net the dream of the Semantic Web has taken a back seat.

So what is this ‘Semantic Web’? Currently the web is a collection of human readable documents, stored on various computers across the world. Web browsers can render the HTML content of these documents, but are unaware of the content of these documents. A browser knows a bit of HTML is representing a table, with 5 columns with 10 rows, but is unaware that it represents a league table for example. Once rendered, humans can infer the knowledge from this table – understand that it represents a football league table, with the team names, goal difference and points displayed in the columns and are able to infer that a certain team is top of the table, and that certain teams are in the lower half of the table. To the computer it is just a table with specified text within it. The dream of the semantic web is transfer this library of human readable documents into a web of information that machines can understand and then infer further knowledge from. In the example given, the machine may infer that Manchester United is a football team, and from this derive that they are from the City of Manchester, and from this derive that their rivals are Manchester City, and so on. If all the data on the web can be transferred into a format that computers can understand, then this data becomes far more useful.

It all seems a bit far-fetched I hear you say? I agree. The sheer amount of web pages and different data sources that exist make this an unattainable dream in my view. Ever since I first heard about it at University just over a year and a half ago I thought it was just another crazy academic project; a crazy project that still fascinates me. What if you could cut the problem down to a much smaller one, and focus a similar approach to a complex but known problem. The Semantic Web hasn’t taken off – but that doesn’t mean that the technology isn’t worthwhile. Take the medical research industry, where ontologies (the underpinning technology behind the Semantic Web – Google it if you’re interested) are successfully being used to find the underlying genetic causes of cardiovascular diseases. Semantic technology has allowed them to massively increase the efficiency of this research.

So what does all this have to do with law enforcement? Well, let me put forward a far-fetched idea. Imagine a police officer enters a new crime report into the system. This crime report contains details about the location of the crime, the victims, the date and time and whatever else is contained in the report. When entered into the system the police officer has no suspects in mind. However, shortly after the crime report has been entered into the system, it starts to work. It derives that the crime took place in Glasgow, on a certain street at a particular time. It derives from the report that a car was stolen. It looks at the registration of the vehicle and can find its currently registered owner, and from that it can find any known people that this person associates with. Immediately, the computer is building up knowledge from the crime report. Perhaps similar crimes in the area are automatically being searched for and information from these is being cross-referenced with this new crime.

What if the computer can derive that the circumstances behind the car theft are very similar to a previous car theft two years ago and that the perpetrator has only recently been released from prison? What if the computer can understand the links between data then draw up a list of suspects for a crime, almost immediately after a crime report has been entered? Sounds crazy – but it’s not too far off what the leading companies in medical research are doing to find new cures for diseases.

It may be some time off, but technology is always improving and maybe this is the next step. This leads to us wondering what the repercussions are if this sort of technology does emerge. Can you trust a computer to carry out this task? What are the legal implications? Will it stand up in court – how were these suspects identified? Surely this technology is only doing what humans would be doing – analysing data, just at a much quicker rate. What if the machine misses something that a human would have spotted – what could happen as a result of this over-sight? What if we don’t use this technology when it could be available and could stop a major terrorist attack? It all may sound far-fetched, and the fact that the Semantic Web is hardly known to anyone today 9 years since it was first advocated is a sign that it probably won’t happen. That doesn’t mean that a dream of the past can’t be the inspiration for the technology of the future.