Advanced Temporal Reasoning for Precise Time-Stamping of Events (ARTEMIS)

Award Information
Agency:
Department of Defense
Branch:
Air Force
Amount:
$742,820.00
Award Year:
2008
Program:
SBIR
Phase:
Phase II
Contract:
FA8750-08-C-0106
Agency Tracking Number:
F071-085-2537
Solicitation Year:
2007
Solicitation Topic Code:
AF071-085
Solicitation Number:
2007.1
Small Business Information
LANGUAGE COMPUTER CORP.
1701 North Collins Blvd., Suite 2000, Richardson, TX, 75080
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
127802234
Principal Investigator
 Andrew Hickl
 Principal Investigator
 (972) 231-0052
 andy.hickl@languagecomputer.com
Business Contact
 Yolanda Guzman
Title: VP - Financial & Legal
Phone: (972) 231-0052
Email: yolanda@languagecomputer.com
Research Institution
N/A
Abstract
Phase II of ARTEMIS addresses three challenges faced by systems which seek to enhance analysts' awareness of event information. First, we demonstrate that our approach can acquire all of the essential temporal information associated with events. In addition to a state-of-the-art TERN system, we use a combination of approaches to acquire the knowledge needed to infer implicit forms of temporal information. Second, in order to take advantage of the explosion of temporal information that we will make available, we will provide temporal reasoning capabilities which will allow event visualization systems to "make sense" of complex situations automatically. Our Phase II work will continue the development of a robust graph-based temporal reasoning component which can track changes in reference time throughout a document, estimate the duration of specific events, identify the likely order of a sequence of events, and compute an exact time-stamp for events which are not associated with overt time or date information. Finally, we plan to expand the amount of domain-specific knowledge available to a state-of-the-art timestamping system by introducing new unsupervised techniques for modeling complex event pathways as they evolve over time as well as a semi-supervised approach to generating hierarchical event ontologies from unstructured texts.

* information listed above is at the time of submission.

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