Inferring Meaning and Intent of Discovered Data Sources

There are many scenarios where there is a need for (semi) automated methods and tools to identify, characterize and exploit information resources, especially those that may have been discovered through obscure means. These information resources are retrieved from environments where there is little to no prior knowledge of the information sources, and from environments where there are unavailable models and uncooperative modelers. The key to this capability is developing techniques for crafting an understanding of the content and context of an information resource, and ultimately reconstructing the meaning and intent of the resource. Our approach to inferring meaning and intent is to gather the implicit semantics available in data source schemas, to build associations between the contents of the data source and the semantics described and defined in ontologies, and to glean additional semantic clues captured from an analysis of a set of queries submitted to the data source.