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SNePS Information Fusion Projects

Information fusion is a process for associating, correlating, and combining data and information from single and multiple sources to achieve refined estimates of characteristics, events, and behaviors for observed entities in an observed field of view. Several joint projects have been undertaken by SNePS researchers and information fusion researchers at the Center for Multisource Information Fusion. These projects have typically focused on introducing reasoning techniques to information fusion processes in order to draw conclusions about entities observed from an information fuion process. Recent, projects have started to focus on aiding soft information fusion by automatically creating SNePS propositiona graphs from natural language data.
Contents
Cyber Security Project
Counterinsurgency Projects

Cyber Security Project
The Cyber Security Project used information fusion techniques on network analysis sensors in order to produce refined data estimates about network traffic. Databases about network attack types and identifiers were used to create knowledge bases that could be used to reason about similarities between attack types in order to perform connonicalization. SNePS research focused on utilizing Topbraid as knowledge base and Pellet as a reasoner over SNePS. An efficiency report for SNePS was also conducted in order to address working with large-scale knowledge repositories.

Relevant Publications:

  1. Michael Kandefer, Stuart Shapiro, Adam Stotz, and Moises Sudit, Symbolic Reasoning in the Cyber Security Domain, Proceedings of MSS 2007 National Symposium on Sensor and Data Fusion McLean, VA, June 2007.

  2. Michael Kandefer and Stuart C. Shapiro, Comparing SNePS with Topbraid/Pellet, SNeRG Technical Note 42, State University of New York at Buffalo, Buffalo, NY, July 18, 2008.

  3. A. Patrice Seyed, Michael Kandefer, and Stuart C. Shapiro, SNePS Efficiency Report, SNeRG Technical Note 43, State University of New York at Buffalo, Buffalo, NY, July 18, 2008.

  4. Michael Kandefer, A. Patrice Seyed, and Stuart C. Shapiro, The Use of SNePS for Cyber Security Reasoning, SNeRG Technical Note 44, State University of New York at Buffalo, Buffalo, NY, July 18, 2008.

Counterinsurgency Projects
The counter insurgency (COIN) projects have focused on taking sensor data, using information techniques on that data, and then using the SNePS reasoner to conclude information about the domain using the "fused" information. The first COIN project involved reasoning about anomalous behavior in shipping lanes using sonar data. The goal was to conclude whether swarms of small boats may be engaged in an attack against the shipping lanes. To aid in this process large-scale ontologies were employed as background information and a method for managing large scale knowledge sources using context was developed, called context-based information retrieval (CBIR).

The second, ongoing project, called MURI, has focused on processing natural language untterances in a COIN domain in order to produce SNePS propositional graph representations of those messages. The messages are taken from field operatives and generally indicate the activities of suspected insurgents. In order to aid in the production of the propositional graphs we are currently employing the GATE syntactic processing suite and the FrameNet caseframe repository. The propositional graphs created are the input for the information fusion processes, as well as input for a CBIR process. The CBIR process uses the propositional graphs to retrieve relevant background knowledge from the NGA: GNS and Research Cyc about entities indentified in the messsages. The goal is to use this additional information to reason about insurgent activity and aid reference resolution.

Relevant Publications:

  1. Michael Kandefer and Stuart C. Shapiro, A Categorization of Contextual Constraints. In Alexei Samsonovich, Ed., Biologically Inspired Cognitive Architectures: Papers from the AAAI Fall Symposium, Technical Report FS-08-04, AAAI Press, Menlo Park, CA, 2008, 88-93.

  2. Michael Kandefer and Stuart C. Shapiro, An F-Measure for Context-Based Information Retrieval. In Gerhard Lakemeyer, Leora Morgenstern, and Mary-Anne Williams, Eds., Commonsense 2009: Proceedings of the Ninth International Symposium on Logical Formalizations of Commonsense Reasoning, The Fields Institute, Toronto, CA, 2009, 79-84.

  3. Juan Gómez-Romero, Jesús Garcia, Michael Kandefer, James Llinas, Jose Manuel Molina, Miguel Angel Patricio, Michael Prentice, & Stuart C. Shapiro, Strategies and Techniques for Use and Exploitation of Contextual Information in High-Level Fusion Architectures, Proceedings of the 13th International Conference on Information Fusion, 2010, TH1.7.3, 8 pages, unpaginated.

  4. Michael Prentice, Michael Kandefer, & Stuart C. Shapiro, Tractor: A Framework for Soft Information Fusion, Proceedings of the 13th International Conference on Information Fusion, 2010, Th3.2.2, 8 pages, unpaginated.
Last modified: Thu Mar 10 14:57:12 EST 2011
Michael W. Kandefer <mwk3@buffalo.edu>
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