Developing a core ontology to improve military intelligence analysis

Dragos, Valentina

doi:10.3233/kes-130253

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…As standardized metadata vocabularies for annotating and structuring data, ACOs are a complex and pervasive form of emerging scientific media (Smith, 2016) that stand to revolutionize industries and domains of social life (Staab and Studer, 2009). Computational ontologies assist with data integration and interoperability at the intersection of a variety of disciplines, from bioinformatics (Bodenreider and Stevens, 2006) to management (Allen and March, 2012), military intelligence (Dragos, 2013) to farming (Sivamani et al, 2013). Largely invisible to individuals who use digital technologies in everyday life, ACOs operate in the background.…”

Section: Methodsmentioning

confidence: 99%

The Tower of Babel problem: making data make sense with Basic Formal Ontology

Iliadis

2019

OIR

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PurposeApplied computational ontologies (ACOs) are increasingly used in data science domains to produce semantic enhancement and interoperability among divergent data. The purpose of this paper is to propose and implement a methodology for researching the sociotechnical dimensions of data-driven ontology work, and to show how applied ontologies are communicatively constituted with ethical implications.Design/methodology/approachThe underlying idea is to use a data assemblage approach for studying ACOs and the methods they use to add semantic complexity to digital data. The author uses a mixed methods approach, providing an analysis of the widely used Basic Formal Ontology (BFO) through digital methods and visualizations, and presents historical research alongside unstructured interview data with leading experts in BFO development.FindingsThe author found that ACOs are products of communal deliberation and decision making across institutions. While ACOs are beneficial for facilitating semantic data interoperability, ACOs may produce unintended effects when semantically enhancing data about social entities and relations. ACOs can have potentially negative consequences for data subjects. Further critical work is needed for understanding how ACOs are applied in contexts like the semantic web, digital platforms, and topic domains. ACOs do not merely reflect social reality through data but are active actors in the social shaping of data.Originality/valueThe paper presents a new approach for studying ACOs, the social impact of ACO work, and describes methods that may be used to produce further applied ontology studies.

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Section: Methodsmentioning

confidence: 99%

The Tower of Babel problem: making data make sense with Basic Formal Ontology

Iliadis

2019

OIR

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“…In [11], the author describes a core ontology based on NATO standards to improve military intelligence analysis. The main concepts of this core ontology have been selected as a basis of the MAGNETO ontology: Organization, Equipment, Person, Place, and Event as depicted in Fig.…”

Section: Fig 8 Magneto Ontology Conceptualisation For Criminal Invest...mentioning

confidence: 99%

Ontology Population Framework of MAGNETO for Instantiating Heterogeneous Forensic Data Modalities

Behmer

Chandramouli²,

Garrido

et al. 2019

IFIP Advances in Information and Communication Technology

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The growth in digital technologies has influenced three characteristics of information namely the volume, the modality and the frequency. As the amount of information generated by individuals increases, there is a critical need for the Law Enforcement Agencies to exploit all available resources to effectively carry out criminal investigation. Addressing the increasing challenges in handling the large amount of diversified media modalities generated at high-frequency, the paper outlines a systematic approach adopted for the processing and extraction of semantic concepts formalized to assist criminal investigations. The novelty of the proposed framework relies on the semantic processing of heterogeneous data sources including audio-visual footage, speech-to-text, text mining, suspect tracking and identification using distinctive region or pattern. Information extraction from textual data, machine-translated into English from various European languages, uses semantic role labeling. All extracted information is stored in one unifying system based on an ontology developed specifically for this task. The described technologies will be implemented in the Multimedia Analysis and correlation enGine for orgaNised crime prEvention and invesTiga-tiOn (MAGNETO).

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“…An ontology is a formal and explicit specification of a shared conceptualization [21]. There are already some works in the area of military applications based on ontologies as knowledge models, a short overview is given in [22]. Herein, their ONTO-CIF model uses the Intelligence Pentagram as the central hub (top-level) of their intelligence ontology.…”

Section: Military Intelligence Analysismentioning

confidence: 99%

Towards information extraction from ISR reports for decision support using a two-stage learning-based approach

Mühlenberg

Kuwertz

Schenkel

et al. 2019

Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2019

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The main challenge of computer linguistics is to represent the meaning of text in a computer model. Statistics based methods with manually created features have been used for more than 30 years with a divide and conquer approach to mark interesting features in free text. Around 2010, deep learning concepts found their way into the text-understanding research community. Deep learning is very attractive and easy to apply but needs massive pools of annotated and high quality data from every target domain, which is generally not available especially for the military domain. When changing the application domain one needs additional or new data to adopt the language models to the new domain. To overcome the everlasting "data problem" we chose a novel two-step approach by first using formal representations of the meaning and then applying a rule-based mapping to the target domain. As an intermediate language representation, we used abstract meaning representation (AMR) and trained a general base model. This base model was then trained with additional data from the intended domains (transfer learning) evaluating the quality of the parser with a stepwise approach in which we measured the parser performance against the amount of training data. This approach answered the question of how much data we need to get the required quality when changing an application domain. The mapping of the meaning representation to the target domain model gave us more control over specifics of the domain, which are not generally representable by a machine learning approach with self-learned feature vectors.

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Developing a core ontology to improve military intelligence analysis

Cited by 10 publications

References 8 publications

The Tower of Babel problem: making data make sense with Basic Formal Ontology

The Tower of Babel problem: making data make sense with Basic Formal Ontology

Ontology Population Framework of MAGNETO for Instantiating Heterogeneous Forensic Data Modalities

Towards information extraction from ISR reports for decision support using a two-stage learning-based approach

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