Abstract: Co-clustering based on generalized conceptual relationships can automatically detect story forms incorporating archetypes/targets and actions. Co-clustering can help in identifying similarities that exist in low-dimensional sub-spaces of sparse data such as textual paragraphs. Through co-clustering, the clusters themselves and their characteristic features are identifiable which can be useful in describing and summarizing their contents. The residual error of factorization with concept-based features is significantly lower than the error with prior keyword-based features. Qualitative evaluations also suggest that concept-based features yield more coherent, distinctive and interesting story forms compared to those produced by using prior keyword-based features.

Abstract: Co-clustering based on generalized conceptual relationships can automatically detect story forms incorporating archetypes/targets and actions. Co-clustering can help in identifying similarities that exist in low-dimensional sub-spaces of sparse data such as textual paragraphs. Through co-clustering, the clusters themselves and their characteristic features are identifiable which can be useful in describing and summarizing their contents. The residual error of factorization with concept-based features is significantly lower than the error with prior keyword-based features. Qualitative evaluations also suggest that concept-based features yield more coherent, distinctive and interesting story forms compared to those produced by using prior keyword-based features.

Abstract: Computerized contextual analysis systems and methods suitable for monitoring situations, regions, and groups characterized by volatility and uncertainty are provided. Via use of exemplary systems and methods, decision makers, for example politicians, warfighters, and analysts can gain insight into the cultures, attitudes, events, and relationships that may impact their missions.

Abstract: Computerized contextual analysis systems and methods suitable for monitoring situations, regions, and groups characterized by volatility and uncertainty are provided. Via use of exemplary systems and methods, decision makers, for example politicians, warfighters, and analysts can gain insight into the cultures, attitudes, events, and relationships that may impact their missions.

Abstract: The invention includes methods and software tools for acquiring data from diverse sources, and structuring the data in a form that may be used to determine object equivalence. Practice of the invention includes one or more of the following tools: a data acquisition web agent creator, a web agent created by the web agent creator, an agent manager for deploying said web agent, and ontology-directed classifier, an ontology-directed extractor, and an ontology-directed matcher. The tools are example driven through a graphical user interface.

Abstract: A web site for user suggestions of products, services or other information. The Suggestor also submits tags with those suggestions. To the extent subsequent users use the same tags to access or purchase the user suggestion, the suggesting user will be rewarded. The present invention also provides mechanisms for disbursing rewards for “finding-and-buying-thru-tags”, ranking suggestions, enabling various privacy preserving communications and deal validation mechanisms among shoppers, Suggestors and their social networks.

Abstract: A method for extracting an attribute occurrence from template generated semi-structured document comprising multi-attribute data records comprises identifying a first set of attribute occurrences in the template generated semi-structured document using an ontology. The method further comprises determining a boundary of each multi-attribute data record in the template generated semi-structured document, learning a pattern for an attribute corresponding to an identified attribute occurrence of the first set in the template generated semi-structured document, and applying the pattern within the boundary of each multi-attribute data record in the template generated semi-structured document to extract a second set of attribute occurrences.