Ontology Driven Integration Framework (ODIF)

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: FA8750-04-C-0198
Agency Tracking Number: B041-043-1208
Amount: $98,207.00
Phase: Phase I
Program: SBIR
Awards Year: 2004
Solicitation Year: 2004
Solicitation Topic Code: MDA04-043
Solicitation Number: 2004.1
Small Business Information
1408 University Drive East, College Station, TX, 77840
DUNS: 555403328
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Perakath Benjamin
 Senior Research Scientist
 (979) 260-5274
Business Contact
 Donielle Mayer
Title: Business Operations Manag
Phone: (979) 260-5274
Email: dmayer@kbsi.com
Research Institution
This project will research, design, and demonstrate an innovative Ontology Driven Integration Framework (ODIF). We propose a hybrid approach that combines knowledge extraction techniques with ontology analysis methods to extract semantic information from distributed, unstructured text sources and that rapidly deploys this knowledge for knowledge sharing and integration for space launch and range operations applications. The Phase I effort will (i) establish ODIF requirements, (ii) formulate knowledge extraction and integration methods, (iii) design the ODIF architecture, and (iii) build and demonstrate prototype ODIF. The Phase II project will harden the software and demonstrate its benefits on a focused space launch and range operations military application leading to rapid technology transition and commercialization. Key innovations include (i) novel application of ontology-assisted text mining methods for knowledge extraction from unstructured text sources; (ii) advanced ontology conflict analysis and mapping methods to facilitate semantic information integration and information sharing; (iii) novel, scalable agent-based software design strategy that will facilitate rapid and cost effective integration and deployment of the solution into military space transportation applications; (iv) novel machine learning and self adaptation mechanisms that will enable the automatic revisions to the extracted knowledge in uncertain and dynamic environments.

* Information listed above is at the time of submission. *

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