Posted on: August 21, 2015, by: Ann Kelly
In today’s business environment, unstructured information has become one of the most valuable resources in an organization and its rate of growth is staggering. The need to extract information from internal and external sources and integrate it into existing systems for use in key business decisions is critical to maintaining a competitive advantage.
The Semantic Web was supposed to help us solve this problem. All information would be available, required facts would be a click away and intelligent agents would locate things. The reality; the world is disillusioned. The volume, velocity, and variety of data flowing into organizations is out of control, the quality of public information is suspect, and the context in which that information was created is seldom obvious.
The real problem is described. It’s difficult to extract value from information unless it is well described; the context, the topics it pertains to and the relationships between facts must be clearly identified in a way that machines can interpret.
Traditionally, machines have had a difficult time processing information mainly because it lacks a predefined structure where the context and format are well understood. Unfortunately, most of the valuable information within organizations is found in unstructured information which lacks structure and doesn’t conform to any standard.
So, where does this bring us?
Semantic technologies have the capacity to extract meaning from unstructured information found within an enterprise and make them available for processing. Our new Semaphore 4 platform combines the power of semantic technologies with our ontology management, auto-classification, and semantic enhancement server to help organizations identify, classify and tag their content in order to use the intelligence within it to manage their business.
Semaphore begins with a model that defines the concepts, topics, products, market segments, and organizational structures associated with a business domain. Rulesets are created from the model and combined with sophisticated Natural Language processing and entity and fact extraction to result in a set of precise and consistent metadata tags associated with each information asset.
With metadata, content-intensive processes can be handled by machines instead of humans. The identified facts and relationships can be used in business intelligence applications, to provide prescriptive and predictive analytics and to provide a means for the visualization of the content to drive business decisions.
So while Semantic Web doubters believe that it won’t live up to the hype, Smartlogic is working with companies worldwide to use semantic technologies to solve real business problems.
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