Abstract
Since the beginning of the Linked Open Data initiative, the number of published Linked Data datasets has gradually increased. However, the reuse of datasets is hindered by a lack of descriptive and reliable metadata about the nature of the data, such as their topic coverage. Manual curation of metadata is however costly and hard to maintain, because of which we advocate a Linked Data profiling approach that will be able to automatically extract topics from datasets as metadata. One of the main challenges in developing this is the lack of evaluation data, i.e. manually curated metadata (topics) for datasets. In this paper we describe such an evaluation dataset and the framework that enabled its creation.
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Acknowledgements
This work was supported by Science Foundation Ireland under grant number SFI/12/RC/2289 (Insight) and by the European Union under grant number H2020-644632 (MixedEmotions).
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Abele, A., McCrae, J.P., Buitelaar, P. (2017). An Evaluation Dataset for Linked Data Profiling. In: Gracia, J., Bond, F., McCrae, J., Buitelaar, P., Chiarcos, C., Hellmann, S. (eds) Language, Data, and Knowledge. LDK 2017. Lecture Notes in Computer Science(), vol 10318. Springer, Cham. https://doi.org/10.1007/978-3-319-59888-8_1
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DOI: https://doi.org/10.1007/978-3-319-59888-8_1
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