By Rachel Ehrenfeld
May 14, 2011
Talking on the CBS show “60 Minutes,” President Obama noted: “It’s going to take some time for us to exploit the intelligence that we were able to gather on site” during the raid in which Osama bin Laden was killed. This information, according to Mr. Obama, gives the U.S. a chance “to…really deliver a fatal blow [to Al Qaeda], if we follow through aggressively in the months to come.”
The current Document Exploitation (DOCEX) method, including automated translation and categorization technologies, suffers from several major deficiencies: focus on an inefficient key word spotting techniques, long processing times, and a high rate of false negatives and positives. Moreover, the automated translation tools are still unsuitable for the sophisticated language of neo-classical Arabic used by Islamist terrorists such as al-Qaeda. Similarly, categorization technologies do not meet the challenge of analyzing sophisticated, culturally sensitive nuances. Poorly translated and categorized documents of course cause the loss of crucial actionable and preventative intelligence.
To address such problems, IntuView, an Israeli company, has developed revolutionary software that can process large volumes of digital documents related to Islamic terrorism very quickly. The software instantly assesses any digital document in a supported language (Arabic, Urdu, Farsi, etc.), determines its relevance and risk rating, and provides an intelligence analysis report based, for example, on the content of the document, including classification, date of writing, type of document, author, region, ideological, ethnical and religious affiliation, and a summary of the content, based on a sophisticated “artificial intuition” program. The summary integrates the implicit meaning of religious concepts, verses from religious and ideological texts, and historical allusions. The software integrates the information about entities (individuals, organizations, etc.) mentioned in the documents to create a virtual “identity card” of the person behind that name: possible affiliations, name variants, ethnic origin, gender, family/tribal links, etc., aggregating and matching information found in multiple entries and identifying links (family, tribe) between the entities.