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Neural Word Sense Disambiguation to Prune a Large Knowledge Graph of the Italian Cultural Heritage

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New Trends in Database and Information Systems (ADBIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1652))

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Abstract

In this paper, we describe our recent findings in interlinking the ArCo Italian cultural heritage entities to the well known Getty Art and Architecture (GVP) Thesaurus through the automated extraction of candidate entities from textual descriptions and the subsequent pruning of ambiguous out-of-domain entities using Neural Word Sense Disambiguation. The disambiguation task is particularly complex since, as detailed in this paper, we map Italian entities in the Arco cultural heritage onto lexical concepts in English (such as those in the GVP Thesaurus). To date, the majority of entity linking and word sense disambiguation systems are designed to work with English and to operate with general purpose sense inventories and knowledge bases, such as DBpedia, BabelNet and WordNet. To address this challenging entity linking and disambiguation task, we adapted a state-of-the-art Neural Word Sense Disambiguation to work in this multi-language setting. We here describe our adaptation process and discuss preliminary experimental results.

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Notes

  1. 1.

    https://sites.google.com/uniroma1.it/agdli/.

  2. 2.

    ArCo “indirectly reuses: DOLCE-Zero, DOLCE+DnS, CIDOC-CRM, EDM, BIBFRAME, FRBR, FaBiO, FEntry, OAEntry” ARCO - PRIMER GUIDE V1.0 http://wit.istc.cnr.it/arco/primer-guide-v1.0-en.html.

  3. 3.

    Further worsened, as previously mentioned, by the automated translation in English of the concepts extracted from the textual field in ArCo, in Italian.

  4. 4.

    “The idea of an AI-complete problem has been around since at least the late 1970s, and refers to the more formal idea of the technique used to confirm the computational complexity of NP-complete problems.” [10].

  5. 5.

    MDZ Digital Library team (dbmdz) at the Bavarian State Library https://huggingface.co/dbmdz/bert-base-italian-xxl-cased.

  6. 6.

    https://dati.cultura.gov.it/sparql.

  7. 7.

    Google Translation AI, https://cloud.google.com/translate.

  8. 8.

    floral decorative motifs (pedestal) - Neapolitan workshop (last quarter of the 19th century).

  9. 9.

    https://www.signll.org/conll/.

  10. 10.

    https://spacy.io/.

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Acknowledgements

This work was carried out within the research project “SMARTOUR: intelligent platform for tourism” funded by the Italian Ministry of University and Research with the Regional Development Fund of European Union (PON Research and Competitiveness 2007-2013).

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Authors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Erica Faggiani, Stefano Faralli & Paola Velardi

Authors
  1. Erica Faggiani
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  2. Stefano Faralli
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  3. Paola Velardi
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Corresponding author

Correspondence to Stefano Faralli .

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Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Silvia Chiusano

  2. Politecnico di Torino, Turin, Italy

    Tania Cerquitelli

  3. Poznań University of Technology, Poznań, Poland

    Robert Wrembel

  4. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  5. University of Genoa, Genoa, Italy

    Barbara Catania

  6. CNRS, Villeurbanne Cedex, France

    Genoveva Vargas-Solar

  7. University of Calabria, Rende, Italy

    Ester Zumpano

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Faggiani, E., Faralli, S., Velardi, P. (2022). Neural Word Sense Disambiguation to Prune a Large Knowledge Graph of the Italian Cultural Heritage. In: Chiusano, S., et al. New Trends in Database and Information Systems. ADBIS 2022. Communications in Computer and Information Science, vol 1652. Springer, Cham. https://doi.org/10.1007/978-3-031-15743-1_54

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