Automatic Concept Maps :asA and :inA Dynamic Wiki

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-14-P-1082
Agency Tracking Number: N132-128-1243
Amount: $79,997.00
Phase: Phase I
Program: SBIR
Awards Year: 2014
Solicitation Year: 2013
Solicitation Topic Code: N132-128
Solicitation Number: 2013.2
Small Business Information
1422 Sachem Pl., Unit #1, Charlottesville, VA, 22901
DUNS: 809180151
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Kevin Corby
 Software Architect
 (434) 284-9406
 kevin@ccri.com
Business Contact
 James Conklin
Title: Director of Operations
Phone: (434) 214-4415
Email: conklin@ccri.com
Research Institution
N/A
Abstract
Representing knowledge in a triple store is trivial, yet querying and visualizing the resulting knowledge is difficult and inefficient when the number of triples is large. Needing to understand the data models from each of the contributing processes and how these data models overlap or interact further complicates this problem. Visualization tools for knowledge stored in the Resource Description Framework (RDF) tend to simply enable visualization of the data via a graph. While this does show the available data in a relatively intuitive manner, it simply does not scale. We will automatically identify intelligible, useful concepts that show how entities relate and expose undeclared relationships in the knowledge base. We will develop tools and techniques for concept generation to augment class/concept structures available from ontologies describing the knowledge store. We address the main problem in two steps: (1) feature selection, (2) analytics and visualization. This proposal describes our proposed methodology for extracting features of entities described in an RDF knowledge base, and the application of these features to automatic concept map generation. We propose to develop a scalable manifold learning algorithm for concept extraction that will also enable a broader application of machine learning algorithms to RDF data at scale.

* 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