Abstract
More and more publishers tend to create and upload their data as digital open data, and this is also the case for the Cultural Heritage (CH) domain. For facilitating their Data Interchange, Integration, Preservation and Management, publishers tend to create their data as Linked Open Data (LOD) and connect them with existing LOD datasets that belong to the popular LOD Cloud, which contains over 1,300 datasets (including more than 150 datasets of CH domain). Due to the high amount of available LOD datasets, it is not trivial to find all the datasets having commonalities (e.g., common entities) with a given dataset at real time. However, it can be of primary importance for several tasks to connect these datasets, for being able to answer more queries and in a more complete manner (e.g., for better understanding our history), for enriching the information of a given entity (e.g., for a book, a historical person, an event), for estimating the veracity of data, etc. For this reason, we present a research prototype, called ConnectionChecker, which receives as input a LOD Dataset, computes and shows the connections to hundreds of LOD Cloud datasets through LODsyndesis knowledge graph, and offers several measurements, visualizations and metadata for the given dataset. We describe how one can exploit ConnectionChecker for their own dataset, and we provide use cases for the CH domain, by using two real linked CH datasets: a) a dataset from the National Library of Netherlands, and b) a dataset for World War I from the Universities of Aalto and Helsinki.
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Acknowledgements
This work has received funding from the European Union’s Horizon 2020 coordination and support action 4CH (Grant agreement No 101004468).
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Mountantonakis, M., Tzitzikas, Y. (2022). How Your Cultural Dataset is Connected to the Rest Linked Open Data?. In: Moropoulou, A., Georgopoulos, A., Doulamis, A., Ioannides, M., Ronchi, A. (eds) Trandisciplinary Multispectral Modelling and Cooperation for the Preservation of Cultural Heritage. TMM_CH 2021. Communications in Computer and Information Science, vol 1574. Springer, Cham. https://doi.org/10.1007/978-3-031-20253-7_12
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