Context Relevance for Text Analysis and Enhancement for Soft Information Fusion

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Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Soft information fusion, fusing information from natural language messages with other soft information and with information from physical sensors is facilitated by representing the information in the messages as a formally defined propositional graph that abides by the uniqueness principle—the principle that every entity or event that is mentioned in the message is represented by a unique node in the graph, or, at worst, by several nodes connected by co-referentiality relations. To further facilitate information fusion, information from the message is enhanced with relevant information from background knowledge sources. What knowledge is relevant is determined by also representing the background knowledge as a propositional graph, embedding the knowledge graph from the messages into the background knowledge graph using the uniqueness principle to fuse a message graph node with a background knowledge graph node, and then using spreading activation to find subgraphs of the background knowledge graph. This combination of the message graph with the retrieved subgraphs is considered the “relevant information.” In this chapter, we discuss, evaluate, and compare two techniques for spreading activation.

Keywords

Context Relevance Information fusion Soft information fusion Spreading activation Graph knowledge representation Propositional graphs Tractor SNePS 
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Notes

Acknowledgements

This work was supported in part by the Office of Naval Research under contract N00173-08-C-4004, and by a Multidisciplinary University Research Initiative (MURI) grant (Number W911NF-09-1-0392) for “Unified Research on Network-based Hard/Soft Information Fusion ,” issued by the US Army Research Office (ARO) under the program management of Dr. John Lavery. The work describe here was done while both authors were in the Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY. Parts of this paper were taken from [39, 41, 42].

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Copyright information

© Springer International Publishing Switzerland (outside the USA) 2016

Authors and Affiliations

  1. 1.Applied Sciences Group, Inc.BuffaloUSA
  2. 2.University at BuffaloBuffaloUSA

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