Skip to main content

Geolocation of Cultural Heritage Using Multi-view Knowledge Graph Embedding

  • Conference paper
  • First Online:
Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges (ICPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13645))

Included in the following conference series:

  • 352 Accesses

Abstract

Knowledge Graphs (KGs) have proven to be a reliable way of structuring data. They can provide a rich source of contextual information about cultural heritage collections. However, cultural heritage KGs are far from being complete. They are often missing important attributes such as geographical location, especially for sculptures and mobile or indoor entities such as paintings. In this paper, we first present a framework for ingesting knowledge about tangible cultural heritage entities from various data sources and their connected multi-hop knowledge into a geolocalized KG. Secondly, we propose a multi-view learning model for estimating the relative distance between a given pair of cultural heritage entities, based on the geographical as well as the knowledge connections of the entities.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://neo4j.com/.

  2. 2.

    https://github.com/MEMEXProject/MEMEX-KG.

  3. 3.

    https://www.wikipedia.org/.

  4. 4.

    http://www.opengis.net/ont/geosparql.

  5. 5.

    https://www.wikidata.org/.

  6. 6.

    https://www.europeana.eu/.

  7. 7.

    https://www.openstreetmap.org/.

  8. 8.

    https://www.spacy.io/.

References

  1. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_52

    Chapter  Google Scholar 

  2. Carriero, V.A., et al.: ArCo: the Italian cultural heritage knowledge graph. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11779, pp. 36–52. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30796-7_3

    Chapter  Google Scholar 

  3. Freire, N., Voorburg, R., Cornelissen, R., de Valk, S., Meijers, E., Isaac, A.: Aggregation of linked data in the cultural heritage domain: a case study in the Europeana network. Information 10(8), 252 (2019)

    Article  Google Scholar 

  4. Guo, X., Qian, H., Wu, F., Liu, J.: A method for constructing geographical knowledge graph from multisource data. Sustainability 13(19), 10602 (2021)

    Article  Google Scholar 

  5. Haslhofer, B., Isaac, A.: data. europeana. eu: the Europeana linked open data pilot. In: International Conference on Dublin Core and Metadata Applications, pp. 94–104 (2011)

    Google Scholar 

  6. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2015)

    Google Scholar 

  7. Maietti, F., Di Giulio, R., Piaia, E., Medici, M., Ferrari, F.: Enhancing heritage fruition through 3D semantic modelling and digital tools: the inception project. In: IOP Conference Series: Materials Science and Engineering, vol. 364, p. 012089. IOP Publishing (2018)

    Google Scholar 

  8. Pellegrino, M.A., Scarano, V., Spagnuolo, C.: Move cultural heritage knowledge Graphsin everyone’s pocket (2020)

    Google Scholar 

  9. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  10. Qassimi, S., Abdelwahed, E.H.: Towards a semantic graph-based recommender system. A case study of cultural heritage. J. Univers. Comput. Sci. 27, 714–733 (2021)

    Google Scholar 

  11. Qiu, P., Gao, J., Yu, L., Lu, F.: Knowledge embedding with geospatial distance restriction for geographic knowledge graph completion. ISPRS Int. J. Geo Inf. 8(6), 254 (2019)

    Article  Google Scholar 

  12. Robusto, C.C.: The cosine-haversine formula. Am. Math. Mon. 64(1), 38–40 (1957)

    Article  MathSciNet  Google Scholar 

  13. Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38

    Chapter  Google Scholar 

  14. Tietz, T., et al.: Linked stage graph. In: SEMANTICS Posters &Demos (2019)

    Google Scholar 

  15. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  16. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)

  17. Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)

    Article  Google Scholar 

  18. Vsesviatska, O., et al.: Ardo: an ontology to describe the dynamics of multimedia archival records. In: Proceedings of the 36th Annual ACM Symposium on Applied Computing, pp. 1855–1863 (2021)

    Google Scholar 

  19. Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv abs/1810.00826 (2019)

    Google Scholar 

  20. Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc. (2018)

    Google Scholar 

  21. Zhu, X., Li, T., De Melo, G.: Exploring semantic properties of sentence embeddings. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 632–637 (2018)

    Google Scholar 

Download references

Acknowledgments

This work was supported by MEMEX project funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No 870743.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Hebatallah A. Mohamed or Sebastiano Vascon .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mohamed, H.A. et al. (2023). Geolocation of Cultural Heritage Using Multi-view Knowledge Graph Embedding. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13645. Springer, Cham. https://doi.org/10.1007/978-3-031-37731-0_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-37731-0_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37730-3

  • Online ISBN: 978-3-031-37731-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics