Software to bring order to information chaos

A new software system that enables faster and more comprehensive analysis of vast quantities of information is so effective that it not only creates order out of chaos and allows computers to perform tasks that before only people could perform, it is also creating new information from old data.

“Our greatest contribution was to create a framework for integrating structured and unstructured information,” says Dr Babis Theodoulidis, Senior Lecturer at the University of Manchester’s Institute of Science and Technology and coordinator of the IST-funded PARMENIDES project behind these tools.

Currently, the vast majority of information is unstructured text, like reports, newspaper articles, letters, memos, essentially any information that is not part of a database.

“Analysing text requires human intervention and, when you are trying to analyse perhaps thousands of documents in many different languages, really large scale text analyses becomes very expensive, or even impossible,” says Theodoulidis.

Structured information is found only in databases, like customer management software, personnel files, library catalogues, and any information that is organised by specific fields of data, such as name, address and so on.

“Analysing structured data is not new. Analysing unstructured information using computers is only a recent development, but integrating and analysing the combined data has never been done before. Our framework makes that possible,” says Theodoulidis.

Practical applications

It means that, once the appropriate priming and tuning is completed, a computer can analyse a given text and put it into context. “For example, a company might get a letter of complaint and then an employee needs to read and forward it to the right person,” says Theodoulidis. “But in our system the letter is ’read’ by a computer, which then links the letter to the company’s personnel database and forwards the letter to the right person.”

The Greek Ministry of Defence (MoD) used the PARMENIDES system to analyse large quantities of unstructured data, like newspaper reports about terrorist attacks, and then combine that with military intelligence. This type of analysis could reveal that one group is changing its methods from car bombs to suicide bombs or chemical attacks. Or that one group is beginning to work with another.

“We got our greatest result with the MoD. Before PARMENIDES, they analysed all their unstructured data manually, essentially people reading articles. Now that’s almost entirely automatic,” says Theodoulidis.

But PARMENIDES’ framework does not just provide a snapshot analysis, it can analyse data over time, too, enabling the system to spot new trends or developments that would remain hidden otherwise. Healthcare consultant BioVista, for example, combined recruitment and business information to track the shifting research priorities in biotech companies over time.

Furthermore, its method of analysis creates new, hidden information from old data. The work was so successful that BioVista hired two software developers and created its own IT department to develop the technology. “Before that they simply outsourced their IT, but they see a value in this type of system and want to pursue it,” says Dr Theodoulidis.

Helping computers understand

The key to the framework is the use of ontologies. They are simply a vocabulary detailing all the significant words for a particular domain, like healthcare or tourism or military intelligence, and the relationship between each word.

PARMENIDES used one ontology to analyse unstructured text, another to analyse databases and a third to unify the two by data sets. So while a newspaper might talk of a ’terrorist’ or ’bomber’, a military database might use the terms ’hostile’ or ’enemy agent’ or specific names. Each data type has its own ontology for the context.

The group also developed tools to enable the semi-automatic creation of those ontologies. “For example, if you give the system many, many samples of the type of information you want to analyse it will produce a provisional ontology, which users can adjust to create a definitive ontology,” says Theodoulidis.

For the future the group is pursuing a joint venture with BioVista to develop aspects of the framework further. Separately it is working with IBM, BioVista and the Greek MoD to make the system more robust and refined.

“I’d also like to develop this technology to work on a Grid-based architecture,” says Theodoulidis. “That would, in many ways, be its ideal environment.” And it would create the opportunity to develop even more novel tools for analysing data to bring order and clarity to chaos and confusion.