Semantic Integration: Meeting the Challenge

Interoperability between data sources is the fundamental challenge of data integration, and NASA computer scientist Richard Keller says that although standards and organizational policies can help to some degree, "data standards can be difficult to legislate and are onerous and expensive to institute." Semantic integration hinges on exercising rigorousness in the capture of semantic metadata, he says. "If you describe the meaning of the data, then you can automate the process of recognizing connections across data sources and allow them to be married together properly," Keller says.

Ontology mapping as the next major challenge for semantic integration, Keller says. An ontology map supplies data to support the translation of the objects, properties, and relations from one ontology model into those of another, and the difficulty arises when the underlying data models differ from a conceptual point of view. "More broadly, I think the challenge for making semantic integration work in the marketplace is to make it quicker and easier to specify data semantics," he says. There are commercially available tools that can streamline the specification process, but Keller says the cost/benefit calculations are not favorable enough to facilitate widespread implementation.

The SemanticIntegrator project seeks to develop a framework to support semantic integration of NASA data assets through the integration of information sources using ontologies in combination with explicit integration rules.

From Dr. Dobb's Journal
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