Skip to main content

MusicBO, an Application of Text2AMR2FRED to the Musical Heritage Domain

  • Conference paper
  • First Online:
The Semantic Web: ESWC 2024 Satellite Events (ESWC 2024)

Abstract

Converting textual data into Knowledge Graphs (KGs) poses a significant challenge, particularly when dealing with multilingual and historical documents. In this paper, we describe the application of Text2AMR2FRED to MusicBO corpus, the former being a tool for transforming text into RDF/OWL KGs via Abstract Meaning Representation (AMR), the latter being a diachronic collection of Musical Heritage (MH) texts.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://polifonia.disi.unibo.it/musicbo/sparql.

  2. 2.

    https://projects.dharc.unibo.it/melody/musicbo/music_in_bologna_knowledge_graph_overview.

  3. 3.

    MELODY (Make mE a Linked Open Data storY) is a web portal that allows users to query Linked Open Data and create web-ready interactive data stories.

  4. 4.

    MusicBO corpus is part of the wider Polifonia Textual CorpusFootnote 5, a large-scale, multilingual and multigenre diachronic textual corpus.

  5. 5.

    https://github.com/polifonia-project/Polifonia-Corpus.

  6. 6.

    Due to copyright reasons, the documents of MusicBO corpus cannot be entirely disclosed. Still, we released metadata that allows the reproduction of the corpus https://doi.org/10.5281/zenodo.6672165.

  7. 7.

    https://arco.istc.cnr.it/txt-amr-fred/.

  8. 8.

    Statistics have been calculated using SpaCy (https://spacy.io) NLP library, employing the models en_core_web_trf for documents in English language and it_core_news_lg for documents in Italian language.

  9. 9.

    https://github.com/polifonia-project/textual-corpus-population.

  10. 10.

    For English language documents, we implemented a co-reference resolution pipeline based on Spacy’s neuralcoref (https://spacy.io/universe/project/neuralcoref). We are currently evaluating tools for Italian.

  11. 11.

    https://github.com/polifonia-project/rulebased-postocr-corrector.

  12. 12.

    PropBank Frames are the core lexicon of the PropBank paradigm and consist of predicate-argument structures named “rolesets".

  13. 13.

    https://github.com/polifonia-project/amr2Fred.

  14. 14.

    https://github.com/polifonia-project/machine-reading.

  15. 15.

    https://www.dbpedia.org/, https://www.wikidata.org/, https://verbatlas.org/.

  16. 16.

    Extrapolated from the KG originating from the sentence "In the year 1814, Barbaja went to Bologna and offered Rossini a better engagement than before.", taken from the MusicBO corpus document The Life of Rossini (Edwards, 1869), available at: https://freeditorial.com/en/books/filter-author/henry-sutherland-edwards.

  17. 17.

    The named entities are automatically linked to their entry in Wikipedia by BLINK [9], the entity linker used by SPRING, and aligned to Wikidata and DBPedia in the AMR2RDF step of our pipeline.

  18. 18.

    https://melody-data.github.io/stories/published_stories/story_1687714706.423208.html.

  19. 19.

    https://github.com/UKPLab/m-AMR2Text.

  20. 20.

    https://github.com/google-research/bleurt.

  21. 21.

    https://github.com/polifonia-project/musicbo-knowledge-graph/tree/main.

References

  1. Bevilacqua, M., Blloshmi, R., Navigli, R.: One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, no. 14, pp. 12564–12573 (2021). https://ojs.aaai.org/index.php/AAAI/article/view/17489

  2. Gangemi, A., Alam, M., Asprino, L., Presutti, V., Recupero, D.R.: Framester: a wide coverage linguistic linked data hub. In: EKAW 2016, pp. 239–254. Springer International Publishing, Bologna, Italy (2016)

    Google Scholar 

  3. Gangemi, A., et al.: Text2AMR2FRED, a tool for transforming text into RDF/OWL Knowledge Graphs via Abstract Meaning Representation. In: 22nd ISWC, CEUR Workshop Proc., Athens, Greece (2023)

    Google Scholar 

  4. Gangemi, A., Hassan, E., Presutti, V., Recupero, D.R.: FRED as an event extraction tool. In: van Erp, M., Hollink, L., Troncy, R., van Hage, W.R., van de Laar, P., Shamma, D.A., Gao, L. (eds.) Proceedings of DeRiVE 2013, co-located with the 12th ISWC 2013, Sydney, Australia, October 21, 2013. CEUR Workshop Proceedings, vol. 1123, pp. 14–17. CEUR-WS.org (2013)

    Google Scholar 

  5. Gangemi, A., Presutti, V., Recupero, D.R., Nuzzolese, A.G., Draicchio, F., Mongiovì, M.: Semantic web machine reading with FRED. Semant. Web 8(6), 873–893 (2017). https://doi.org/10.3233/SW-160240, https://doi.org/10.3233/SW-160240

  6. Meloni, A., Reforgiato Recupero, D., Gangemi, A.: AMR2FRED, a tool for translating abstract meaning representation to motif-based linguistic knowledge graphs. In: The Semantic Web: ESWC 2017 Satellite Events, pp. 43–47. Springer International Publishing, Portorož, Slovenia (2017)

    Google Scholar 

  7. Orlando, R., Conia, S., Faralli, S., Navigli, R.: Universal semantic annotator: the first unified API for WSD, SRL and semantic parsing. In: Proceedings of the 13th LREC 2022, pp. 2634–2641. European Language Resources Association, Marseille, France (2022). https://aclanthology.org/2022.lrec-1.282

  8. Sennrich, R., Haddow, B., Birch, A.: Improving neural machine translation models with monolingual data. In: Proceedings of the 54th Annual Meeting of the ACL (Volume 1: Long Papers), pp. 86–96. ACL, Berlin, Germany (2016). https://doi.org/10.18653/v1/P16-1009, https://aclanthology.org/P16-1009

  9. Wu, L., Petroni, F., Josifoski, M., Riedel, S., Zettlemoyer, L.: Scalable zero-shot entity linking with dense entity retrieval. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6397–6407. ACL, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.519, https://aclanthology.org/2020.emnlp-main.519

Download references

Acknowledgements

The authors acknowledge the support of the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101004746.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arianna Graciotti .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gangemi, A. et al. (2025). MusicBO, an Application of Text2AMR2FRED to the Musical Heritage Domain. In: Meroño Peñuela, A., et al. The Semantic Web: ESWC 2024 Satellite Events. ESWC 2024. Lecture Notes in Computer Science, vol 15344. Springer, Cham. https://doi.org/10.1007/978-3-031-78952-6_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-78952-6_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-78951-9

  • Online ISBN: 978-3-031-78952-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics