Enhancing the Usability of European Digital Cultural Library Using Web Architectures and Deep Learning
- 11 Downloads
Europeana provides APIs (Application Programming Interfaces) for both end users and content providers, in an effort to enable stakeholders (institutions and private developers) to build their own applications, leading to an increasing number of projects that are built around the Europeana API and are run by various cultural/touristic institutions and companies. However, due to the large volume of digitized cultural artifacts there is not enough qualified human resources available to provide manual indexing This problem affects Europeana, where the search results following a user query are often mixed with partially or totally irrelevant items which are linked in some way with the search input keywords due to incomplete/incorrect or ambiguous metadata. In order to properly address the challenges described above, we propose the use of automated, intelligent techniques that allow the interpretation and classification of digital cultural artifacts and the refinement/ranking of search results. We apply a mixed approach using Web architectures for implementing a user-friendly search engine and a Deep Learning model that performs image classification in order to achieve an improvement in the relevance of the search results from Europeana.
KeywordsDigital cultural library Semantic web Deep learning
- Belhi, A., Bouras, A., & Foufou, S. (2017). Digitization and preservation of cultural heritage: The EPROQHA approach. In 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Malabe.Google Scholar
- Dervakos, E., Korkofigas, A., & Sofou, N., & Stamou, G. (2018). Active human-in-the-loop deep learning for cultural metadata enrichment. In EuropeanaTech Conference. Google Scholar
- Llamas, J., Lerones, P. M., Zalama, E., & Bermejo, J. G. G. (2008). Applying deep learning techniques to cultural heritage images within the INCEPTION project. In Euro-Mediterranean conference (pp. 25–32). Cham: Springer.Google Scholar
- Yasser, A. M., Clawson, K., & Bowerman, C. (2017). Saving cultural heritage with digital make-believe: Machine learning and digital techniques to the rescue. In Proceedings of the 31st British Computer Society Human Computer Interaction Conference.Google Scholar