Abstract
In recent years, following FAIR and open data principles, the number of available big data including biomedical data has been increased exponentially. In order to extract knowledge, these data should be curated, integrated, and semantically described. Accordingly, several semantic integration techniques have been developed; albeit effective, they may suffer from scalability in terms of different properties of big data. Even scaled-up approaches may be highly costly due to performing tasks of semantification, curation, and integration independently. To overcome these issues, we devise ConMap, a semantic integration approach which exploits knowledge encoded in ontologies to describe mapping rules in a way that performs all these tasks at the same time. The empirical evaluation of ConMap performed on different data sets shows that ConMap can significantly reduce the time required for knowledge graph creation by up to 70% of the time that is consumed following a traditional approach. Accordingly, the experimental results suggest that ConMap can be a semantic data integration solution that embody FAIR principles specifically in terms of interoperability.
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Acknowledgement
This work has been supported by the European Union’s Horizon 2020 Research and Innovation Program for the project iASiS with grant agreement No 727658.
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Jozashoori, S., Novikova, T., Vidal, ME. (2019). Poster Paper Data Integration for Supporting Biomedical Knowledge Graph Creation at Large-Scale. In: Auer, S., Vidal, ME. (eds) Data Integration in the Life Sciences. DILS 2018. Lecture Notes in Computer Science(), vol 11371. Springer, Cham. https://doi.org/10.1007/978-3-030-06016-9_9
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DOI: https://doi.org/10.1007/978-3-030-06016-9_9
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