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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
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
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)
Guo, X., Qian, H., Wu, F., Liu, J.: A method for constructing geographical knowledge graph from multisource data. Sustainability 13(19), 10602 (2021)
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)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2015)
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)
Pellegrino, M.A., Scarano, V., Spagnuolo, C.: Move cultural heritage knowledge Graphsin everyone’s pocket (2020)
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)
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)
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)
Robusto, C.C.: The cosine-haversine formula. Am. Math. Mon. 64(1), 38–40 (1957)
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
Tietz, T., et al.: Linked stage graph. In: SEMANTICS Posters &Demos (2019)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)
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)
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv abs/1810.00826 (2019)
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)
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)
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
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this paper
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)