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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. UIMA is a framework and SDK for developing such applications. 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 such an application to be decomposed into components, for example "language identification" -> "language specific segmentation" -> "sentence boundary detection" -> "entity detection (person/place names etc.)". Each component must implement interfaces defined by the framework and must provide 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.

s Richard MacManus recently predicted, in 2008 we'll witness the rise of semantic web services. From the native support for Microformats in Firefox 3, to the New York Times' utilization of rich headers metadata, to this week's release of the Social Graph API by Google, semantics are starting to slip onto the web. The impact is being felt because large companies are really starting to focus on structured information. In the same vein, last week Reuters - an international business and financial news giant - launched an API called Open Calais. The API does a semantic markup on unstructured HTML documents - recognizing people, places, companies, and events. This technology is the next generation of the Clear Forest offering, which Reuters acquired last year. We have profiled Clear Forest on ReadWriteWeb and in this post we will look at what Reuters opened up and why.

understanding this content and making decisions based on it (especially in mission-critical situations) is not just a simple matter of consuming information. To effectively "make sense" of large, heterogeneous, and often unstructured content collections requires: - efficient, accurate, and context-based ways of extracting, filtering, and summarizing information; - better and more meaningful ways of organizing, visualizing, and interacting with the information; - faster, more objective methods for investigating hypotheses, detecting trends or patterns across multiple sources, and otherwise analyzing or interpreting information.

Making sense of the world is a ubiquitous activity, taking place around the margins of what we know. At work, your boss says, "Can you give a presentation next week on how wireless will affect our business?" Or perhaps, you join a new committee, and wonder "Who are these people? Who is in charge? What is our mission? What are we really going to do?" Maybe you move to a new neighborhood, and you try to make sense of the streets, schools, parks, shopping, and neighbors. Or you say to yourself, "I really need to get an updated cellphone-what has been happening with the current set of features, costs, plans and new gadgets?" Sensemaking can be a core professional task in itself, as for researchers, designers, or intelligence analysts. It arises when we change our place in the world or when the world changes around us. It arises when new problems, opportunities, or tasks present themselves, or when old ones resurface. It involves finding the important structure in a seemingly unstructured situation. It is an activity with cognitive and social dimensions, and has informational, communicational, and computational aspects.

SMILA is an extensible framework for building search solutions to access unstructured information in the enterprise. Besides providing essential infrastructure components and services, SMILA also delivers ready-to-use add-on components, like connectors to most relevant data sources. Using the framework as their basis will enable developers to concentrate on the creation of higher value solutions, like semantic driven applications etc.

KIM is a software platform for: * Semantic annotation of text At more length: automatic ontology population and open-domain dynamic semantic annotation of unstructured and semi-structured content for Semantic Web and KM applications * Indexing and retrieval (semantically-enabled and IE-enhanced search technology) * Query and exploration of formal knowledge * Co-occurrence tracking and ranking of entities * Entity popularity timelines analysis

Our goal in creating this site is to provide the basis for a thriving community of UIMA developers who can announce, discuss, design, share, and critique UIMA-compliant components, resources and solutions. The Unstructured Information Management Architecture (UIMA) is a software framework that supports rapid development and deployment of multimodal analytics - applications which provide value by processing human-readable text, audio and/or video in order to extract information, answer questions, summarize documents, etc.