Automated PrOduct GEneration and Enrichment (APOGEE)

Creating information products to answer"Tell Me About"questions requires the ability to identify key pieces of information relevant to a complex set of content requirements. Complicating matters, these key pieces of information are scattered across data stores and buried in huge volumes of data. This results in the current predicament analysts find themselves; information retrieval and management consumes huge amounts of time that could be better spent performing analysis. The persistent growth in data accumulation rates will only increase the amount of time spent on these tasks without a significant advance in automated solutions for information product generation. We propose a system called Automated PrOduct GEneration and Enrichment (APOGEE). APOGEE automates the creation of information products; learning the creation process by example. There are three stages to APGOEE"s workflow; first, using clustering and other machine learning techniques APOGEE learns the content models for a range of information products; next, using a search-and-align based methodology, APOGEE maps the content models to the semantic structure underlying unstructured text; finally, APOGEE uses the learned content model and semantic mapping to automatically generate new information products. All this can be done on the fly without requiring predefined information product templates or ontologies.