Jennifer Zaino
SemanticWeb.com Contributor

A difficult business climate can be perfect time for companies to investigate semantic technologies. Many companies have had to tighten the purse-strings when it comes to hiring new talent, which means there’s not an expanding pool of resources available to help source information from multiple parties throughout ever more complex global marketplaces.

That’s true in the life sciences industry where semantic web startup Alitora Systems specializes, but no less so for many other sectors that are interested in increasing the effectiveness of their operations with emerging Web 3.0 technologies.

“Keeping track of the global village we live in means you can’t just go by what’s the word on the street from someone who talks to some of [an industry's] players. Industries are too big, and there are global implications for everything,” says Marc Hadfield, Alitora’s founder and CTO. About 80 percent of knowledge is contained within unstructured documents, where it is not easily searchable.

“The semantic web’s ability to take that data and provide a structured framework, enrich the data with meaning, and provide algorithms to connect relevant information with the people that are most likely to want to consume it is critical,” says Hadfield.

He’ll be speaking about how analytics, semantic reasoning, and data visualization aid in discovering critical business insights on a panel at the upcoming Web 3.0 Conference New York.

Alitora’s software is based on a natural language processing (NLP) engine that extracts knowledge statements from unstructured documents — that’s a particular issue for the life sciences space where high-value knowledge about things, such as the relationship between genetics and disease, lies hidden within journal articles, research papers, clinical trial data, FDA web sites, and the like. Its semantic database technology manages that information and calculate its relevancy to the network of individuals connected to the system to deliver to them the appropriate data.

Its understanding of each individual’s interests and expertise, accumulated through their expressed preferences and deductions based on their interactions with various pieces of knowledge, also makes it possible for the system to connect people with others in their organizations or extended business partnerships who can provide the expertise they need when they need it.

That’s been tried before, with the first generations of knowledge management systems, and even with wikis, but Hadfield says the difference now is that there’s no overhead that keeps individuals from participating.

“Essentially implementations such as ours just let people work as they would normally and by basic actions — they read certain documents or send certain information to co-workers — you build up this information about what is relevant to them and their expertise, and you can leverage that throughout the organization.”

The value of the automatic extraction and delivery of information both for business development and maintaining a competitive edge is becoming increasingly apparent. Within the life sciences market, for example, large pharmaceuticals firms have regularly sourced drug research from outside their companies, from smaller biotech firms who may have sourced information from universities, “so if a pharma is trying to make decisions as to how to further their portfolio of products they must source that IP from 10,000 biotechs around the world and 20,000 academic groups that are developing this type of information and patents,” he says. “The only way to make that effective is to use semantic technologies to be able to work through all that information.”

Given the current economy, there’s a labor efficiency argument to be made for the role semantic technologies have to play in increasing business effectiveness, too. On the one hand, companies can eliminate the manual knowledge aggregation tasks that involve people rekeying information into databases, but the real value is in making the knowledge worker’s job easier, Hadfield says. “They can filter more information, extract more information, and do better analysis with less effort.”

Alitora offers its technology, which includes its life-sciences specific NLP engine both as a service and via an enterprise license. But it also is seeing interest from companies in entirely separate fields, such as new media search, in licensing the rest of its platform as a service. Extracting information from things such as Twitter feeds is a very different knowledge extraction problem, Hadfield says, but at the core it’s still about unstructured data coming in, going through some degree of analysis, and then generating and distributing that knowledge across a collaborative network.

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