Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, F-59313 Valenciennes, France"/> INSA Hauts-de-France, F-59313 Valenciennes, France"/> ReDCAD Laboratory, University of Sfax, B.P. 1173, 3029 Sfax, Tunisia"/>
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2021 Volume 36
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REVIEW   Open Access    

Agent mining approaches: an ontological view

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  • Abstract: Introduced as an interdisciplinary area that combines multi-agent systems, data mining and knowledge discovery, agent mining is currently in practice. To develop agent mining applications involves a combination of different approaches (model, architecture, technique and so on) from software agent and data mining (DM) areas. This paper presents an investigation of the approaches used in the agent mining systems by deeply analyzing 121 papers resulting from a systematic literature review. An ontology was defined to capitalize the knowledge collected from this study. The ontology is organized according to seven main facets: the problem addressed, the application domain, the agent-related and the mining-related elements, the models, processes and algorithms. This ontology is aimed at providing support to decisions about agent mining application design.
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  • Cite this article

    Emmanuelle Grislin-Le Strugeon, Kathia Marcal de Oliveira, Dorsaf Zekri, Marie Thilliez. 2021. Agent mining approaches: an ontological view. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000114
    Emmanuelle Grislin-Le Strugeon, Kathia Marcal de Oliveira, Dorsaf Zekri, Marie Thilliez. 2021. Agent mining approaches: an ontological view. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000114

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Agent mining approaches: an ontological view

Abstract: Abstract: Introduced as an interdisciplinary area that combines multi-agent systems, data mining and knowledge discovery, agent mining is currently in practice. To develop agent mining applications involves a combination of different approaches (model, architecture, technique and so on) from software agent and data mining (DM) areas. This paper presents an investigation of the approaches used in the agent mining systems by deeply analyzing 121 papers resulting from a systematic literature review. An ontology was defined to capitalize the knowledge collected from this study. The ontology is organized according to seven main facets: the problem addressed, the application domain, the agent-related and the mining-related elements, the models, processes and algorithms. This ontology is aimed at providing support to decisions about agent mining application design.

    • The authors declare none.

    • This work was conducted using the Protégé resource, which is supported by grant GM10331601 from the National Institute of General Medical Sciences of the United States National Institutes of Health.

    • https://protege.stanford.edu/

    • In this paper, OntoGraf was used for all figures of the ontology.

    • This paper uses the same format found in Anand et al. (2012), Oliveira et al. (2013) to present the axioms.

    • Each object property has its inverse object property defined, even if not shown in the figures. For instance, partOf_directly has its inverse named hasPart_direcly; refersTo has its inverse isReferedBy

    • Note, in all figures about the ontology ‘has subclass’ relationship corresponds to the inverse of the ‘is-a’ relationship for taxonomies in ontology. For instance, in this figure the relationship means ‘MiningProcess is-a MiningRelatedElement

    • Note that Disjoint With is equivalent to the $\oplus$ operator in formal logic.

    • Note that the word exactly represents a cardinality restriction. It specifies the exact number that an individual must participate in for a given property. For example, an instance of the class KQML_Model participates in one relationship along the specified object property describesCProtocol to exactly one instance of the class CommunicationProtocol. In other words, ‘a KQML_Model describes exactly one CommunicationProtocol’.

    • © The Author(s), 2021. Published by Cambridge University Press2021Cambridge University Press
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    Emmanuelle Grislin-Le Strugeon, Kathia Marcal de Oliveira, Dorsaf Zekri, Marie Thilliez. 2021. Agent mining approaches: an ontological view. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000114
    Emmanuelle Grislin-Le Strugeon, Kathia Marcal de Oliveira, Dorsaf Zekri, Marie Thilliez. 2021. Agent mining approaches: an ontological view. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000114
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