Central to any collaboration problem is the need that the information being considered is as complete and up-to-date as possible. However, information sources tend to be disparate in their location and in both their structure and semantics. Additionally, many information sources such as web pages, blogs, etc were developed for interaction between humans and not in a form that can be shared easily between humans and machines. For example, a query of a web site can identify whether it contains a set of terms but cannot infer that “if two individuals have a working relationship, and one of these individuals works for a particular organization, then the second individual likely knows the first person and is a member of the same organization”. The ability to extract structured information from unstructured sources such as web pages and to merge that with information from more structured sources including databases and ontologies is the core focus of the paper. Extracted information is mapped to an ontology, allowing both machines and humans to understand and reason with the information and allowing information to be provided that is compatible with the data needs of an application. The extraction capability is generic and allows the extraction and mapping of individuals (and their roles), groups and organizations to the ontology. Future developments of the extraction capability will provide the ability to extract, edit and merge new classes and associate appropriate instances of individuals, groups, etc. The paper provides details of the Information Bridging System (IBS) and its three key support technologies: int-SEARCH, int-MAPPER and int-EXTRACTOR, which provide the capability to access, search, fuse and merge information from structured and unstructured sources. This allows users and systems to perform semantic queries to identify “connections” between information in different sources, ensuring information is kept up-to-date as sources and their “connections” change, ensuring an efficient and productive process.