Disambiguation of Entity Association Statements

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-10-M-0437
Agency Tracking Number: N102-176-1050
Amount: $69,997.00
Phase: Phase I
Program: SBIR
Awards Year: 2011
Solicitation Year: 2010
Solicitation Topic Code: N102-176
Solicitation Number: 2010.2
Small Business Information
5 Independence Way, Suite 309, Princeton, NJ, -
DUNS: 087757766
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Dave Ihrie
 Vice President, Governmen
 (301) 233-4780
Business Contact
 Adriana Reininger
Title: Business&Contracts Mana
Phone: (609) 454-0657
Email: asr@semandex.net
Research Institution
Existing techniques for level 1 fusion of association statements in large RDF data stores have proven inadequate to the challenge of entity disambiguation from multiple intelligence sources. The resulting entity and association uncertainty causes problems of missed associations, redundant RDF statements that limit scalability, and significant limitations on higher level reasoning algorithms. Numerous researchers have established the need for higher level context in addressing this problem, and while necessary, context alone is not sufficient. We propose to leverage an existing software-based characteristic matcher operating in a context-aware framework by using a simulated annealing algorithm to support level 1 fusion of a large RDF data store. We will develop a capability whose goal is to generate a single connected graph from large RDF that contains no redundant entities and no missed connections. Specifically, the algorithm we propose will address (i) entity uncertainty, (ii) entity information from different knowledge bases that results in a contradiction, (iii) creation of statements regarding an entity in a knowledge base or common feature space that do not contradict existing statements on that entity, and (iv) allows for the deletion of an entity or entity statements without breaking other associations that may refer to that entity. The result will be an algorithm that integrates and connects new RDF-expressed sources while preserving the original data and semantics, allowing the expression of a large data corpus as a single connected graph. The Feasibility Criterion for Phase I will be to measure and show clear progress in RDF statement disambiguation against a data store containing tens of thousands of statements.

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

Agency Micro-sites

SBA logo
Department of Agriculture logo
Department of Commerce logo
Department of Defense logo
Department of Education logo
Department of Energy logo
Department of Health and Human Services logo
Department of Homeland Security logo
Department of Transportation logo
Environmental Protection Agency logo
National Aeronautics and Space Administration logo
National Science Foundation logo
US Flag An Official Website of the United States Government