From the website:
Unstructured Information Management applications are software systems that analyze large volumes of unstructured information in order to discover knowledge that is relevant to an end user. An example UIM application might ingest plain text and identify entities, such as persons, places, organizations; or relations, such as works-for or located-at.
UIMA enables applications to be decomposed into components, for example “language identification” => “language specific segmentation” => “sentence boundary detection” => “entity detection (person/place names etc.)”. Each component implements interfaces defined by the framework and provides self-describing metadata via XML descriptor files. The framework manages these components and the data flow between them. Components are written in Java or C++; the data that flows between components is designed for efficient mapping between these languages.
UIMA additionally provides capabilities to wrap components as network services, and can scale to very large volumes by replicating processing pipelines over a cluster of networked nodes.
The UIMA project offers a number of annotators that produce structured information from unstructured texts.
If you are using UIMA as a framework for development of topic maps, please post concerning your experiences with UIMA. What works, what doesn’t, etc.