Semantic Web helps protect public health

Computerworld - After 9/11, health officials and the U.S. government became very concerned about the possibility of bioterror and interested in ways of identifying possible bioterror incidents early to react and contain them, says Parsa Mirhaji. "People were talking about all kinds of wild things like tracking fruit juice and handkerchief sales to identify a flu-like outbreak that can indicate a potential anthrax attack," says the assistant professor of medicine and the director of the Center for Biosecurity and Public Health Information Research at the University of Texas in Houston.

The problem was not a lack of data but almost too much. "We have information from hospital emergency departments, community clinics, pharmacy prescription drug sales, clinical laboratories, environmental safety commissions, pollution exposure in air and water." This is all complex data, and to do any good it needs to be analyzed quickly, first for normal patterns to provide a basis for comparison and then for deviations from those patterns that might indicate a natural disease outbreak or a bioterror incident.

This data comes from multiple sources and systems that use different, often incompatible schema. It comes in fast, in near real time, and to be of use, it needs to be analyzed very quickly. Conventional analysis methods simply are not as effective -- in an outbreak, officials depending on them would constantly be behind the spread of an infectious disease.

To solve that problem, Mirhaji's team turned to Semantic Web technology, a term that is beginning to appear in conversations among those working with very large amounts of data involved in scientific and medical research.

Web of meaning and connectivity

Semantic Web refers to the web of meaning and connectivity in large and complex data sets accessible from a distributed network such as World Wide Web, a network of collaborators and trustees or within boundaries of an organization. It's a way to organize complex data in meaningful ways by assigning a formal meaning to each element of data. This makes all data explicit, unambiguous and its interpretation identical for both machines and humans.

A simple example might be cell phone numbers. Presume, for instance, that a company issues cell phones with a consecutive set of numbers to all its salespeople. If you know this, then whenever you see one of the numbers in that set, you know that the person holding it is a sales professional who works for a specific company.

Suppose you are at a conference in Japan and someone gives you a business card written in Japanese, which you cannot read. But you see that the cell number is part of the series issued to this company's salespeople. Immediately, you know something about the person who just gave you his card, even if you cannot talk to him.