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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2017: Advances in Artificial Intelligence: From Theory to Practice pp 495–501Cite as

ADnOTO: A Self-adaptive System for Automatic Ontology-Based Annotation of Unstructured Documents

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10350))

Abstract

In this paper we describe ADnOTO, a self-adaptive system for automatic ontology-based document annotation. The main goal of ADnOTO is the automatization of the document annotation process, particularly in the context of ontology-based digital libraries.

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Notes

  1. 1.

    We refer the reader to [9] for a detailed description of the stole ontology.

References

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Author information

Authors and Affiliations

  1. POLCOMING, Università di Sassari, Viale Mancini N. 5, 07100, Sassari, Italy

    Laura Pandolfo & Luca Pulina

Authors
  1. Laura Pandolfo
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  2. Luca Pulina
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Corresponding author

Correspondence to Laura Pandolfo .

Editor information

Editors and Affiliations

  1. Artois University, Lens, France

    Salem Benferhat

  2. Artois University, Lens, France

    Karim Tabia

  3. Texas State University, San Marcos, Texas, USA

    Moonis Ali

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Pandolfo, L., Pulina, L. (2017). ADnOTO: A Self-adaptive System for Automatic Ontology-Based Annotation of Unstructured Documents. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_54

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  • DOI: https://doi.org/10.1007/978-3-319-60042-0_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60041-3

  • Online ISBN: 978-3-319-60042-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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