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
    Country/TerritoryUnited 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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