Abstract
The success of linked data has resulted in a large amount of data being generated in a standard RDF format. Various techniques have been explored to generate a compressed version of RDF datasets for archival and transmission purpose. However, these compression techniques are designed to compress a given dataset without using any external knowledge, either through a compact representation or removal of semantic redundancies present in the dataset. In this paper, we introduce a novel approach to compress RDF datasets by exploiting alignments present across various datasets at both instance and schema level. Our system generates lossy compression based on the confidence value of relation between the terms. We also present a comprehensive evaluation of the approach by using reference alignment from OAEI.
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Joshi, A.K., Hitzler, P., Dong, G. (2016). Alignment Aware Linked Data Compression. In: Qi, G., Kozaki, K., Pan, J., Yu, S. (eds) Semantic Technology. JIST 2015. Lecture Notes in Computer Science(), vol 9544. Springer, Cham. https://doi.org/10.1007/978-3-319-31676-5_5
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