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Understanding Semantic Web Technologies
By Ericka Chickowski

  Table of Contents:
  1. Understanding Semantic Web Technologies
  2. Semantic Web Technology: Providing Context
  3. Semantic Web Technology: Leaning on Obstacles
  4. Semantic Web Technology: Making Data Usable
  5. Challenges


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Understanding Semantic Web Technologies - Semantic Web Technology: Making Data Usable
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Making Data More Usable

The flexible, more robust connections between data created by ontologies is especially appealing to specialized verticals that must rely on making connections between disparate collections of data to make breakthroughs in their work. For example, life sciences and drug research company employees could do wonders if they had easier access and knowledge about little-known studies and information hidden in archives scattered around the organization.

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“In life sciences, there is great need to integrate data sources. They have so much information about biological data, drug data, chemical data and so on,” Polikoff says. “Our customers there use our product to integrate their data and allow researchers and various scientists to search connections in data in a free-form way. So it is not determined how things could be connected; they are discovering connections as they browse and search things. [That’s] because lots of connections in science are discovered by chance. You collect so much data you have to bring it together and let people look at it critically.”

Other early adopters of semantics include law firms and other companies seeking to sift through mountains of court documents, government and intelligence agencies in need of a way to find a needle in the haystack of public and top-secret information, and even the banking industry. Some fraud-prevention companies have been using semantics to take transaction information collected by their systems to get a clearer picture of when and where fraud may be occurring.

“Semantic technology can give you the agility to take a lot of siloed data points and give you a holistic view across an enterprise,” says Ken Harris, vice president of product development for ACI Worldwide, a fraud-detection software developer that recently partnered with a semantics company called Metatomix to better integrate information collected by its antifraud software. “The real power in it is being able to take beyond just a taxonomy approach of just standard data or business-process flow and actually being able to take that to the next level of conceptual or theory-type models and applying those to the data that you actually have.”

Semantics helps ACI Worldwide create a “view of fraud and interact with the ever-changing environment of fraud in a way that is very powerful,” Harris says. The best way to solve this problem is to take fraud and apply logic to fraud and understand what the meaning of that is, he says. “This is the technology that is going to change that overnight”



 
 
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