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Ontology Population from French Classified Ads

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Graph-Based Representation and Reasoning (ICCS 2023)

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

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Abstract

Understanding texts written in natural language is a challenging task. Semantic Web technologies, in particular ontologies, can be used to represent knowledge from a specific domain and reason like a human. Ontology population from texts aims to transform textual contents into ontological assertions. This paper deals with an approach of automatic ontology population from French textual descriptions. This approach has been designed to be domain-independent, as long as a domain ontology is provided. It relies on text-based and knowledge-based analyses, which are fully explained. Experiments performed on French classified advertisements are discussed and provide encouraging results.

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Notes

  1. 1.

    http://staffwww.dcs.shef.ac.uk/people/A.Aker/activityNLPProjects.html.

  2. 2.

    https://github.com/Galigator/openllet.

  3. 3.

    https://www.lecoindelimmo.com/.

  4. 4.

    Experimental files (inputs, outputs for all tested approaches, and GS) are at https://doi.org/10.5281/zenodo.5776752. A zip file with a runnable jar for KOnPote with Aker’s lemmatizer is at https://alec.users.greyc.fr/research/konpote/.

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Acknowledgements

We thank Quentin Leroy and Jean-Philippe Kotowicz for their participation in the ontology design, and Enor-Anaïs Carré and Morgan Gueret for the corpus.

Author information

Authors and Affiliations

  1. Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYC, 14000, Caen, France

    Céline Alec

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  1. Céline Alec
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Correspondence to Céline Alec .

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Editors and Affiliations

  1. Universidad de Málaga, Málaga, Spain

    Manuel Ojeda-Aciego

  2. FernUniversität in Hagen, Hagen, Germany

    Kai Sauerwald

  3. Humboldt-Universität zu Berlin, Berlin, Germany

    Robert Jäschke

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Alec, C. (2023). Ontology Population from French Classified Ads. In: Ojeda-Aciego, M., Sauerwald, K., Jäschke, R. (eds) Graph-Based Representation and Reasoning. ICCS 2023. Lecture Notes in Computer Science(). Springer, Cham. https://doi.org/10.1007/978-3-031-40960-8_13

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  • DOI: https://doi.org/10.1007/978-3-031-40960-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40959-2

  • Online ISBN: 978-3-031-40960-8

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