Automated information foraging for sensemaking

Phil Dibona, Shen Shyang Ho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In preparations for Multi-Domain Operations and Battles, All-Source and OSINT intelligence analysts gather, assess, and extract relevant information from operational databases as well as publicly available information. This data, often unstructured text documents, is noisy with relevant snippets buried within the document corpus. The costs of exploratory search and exploitive document analysis required to find these hidden snippets of information often drive searches toward a small subset of documents. Additionally, modern search tools may reinforce the confirmation bias of analysts by providing only those documents that closely match their search query. Due to the potentially high tempo of multi-domain battle, the end result is a decision or hypothesis that is ill-considered and substantiated by potentially biased information. An automated information foraging framework can mitigate these challenges by automatically identifying a wide breadth of topics for the user, extracted directly from a document corpus. A semantic network formed from the constituent entities within a document corpus contains inherently valuable topological structures that can be used to generate topics and also guide the analyst?s information exploration. Leveraging a suite of information retrieval and graph analysis algorithms that analyze the semantic network, a framework is defined for assisting analysts in both exploring and exploiting relevant information from a corpus to support the sensemaking process.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence and Machine Learning for Multi-Domain Operations Applications
EditorsTien Pham
PublisherSPIE
ISBN (Electronic)9781510626775
DOIs
StatePublished - Jan 1 2019
EventArtificial Intelligence and Machine Learning for Multi-Domain Operations Applications 2019 - Baltimore, United States
Duration: Apr 15 2019Apr 17 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11006
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceArtificial Intelligence and Machine Learning for Multi-Domain Operations Applications 2019
CountryUnited States
CityBaltimore
Period4/15/194/17/19

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Dibona, P., & Ho, S. S. (2019). Automated information foraging for sensemaking. In T. Pham (Ed.), Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications [110060D] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11006). SPIE. https://doi.org/10.1117/12.2518893