Abstract
Data lakes are repositories of data with potential for analysis. Data lakes aim to liberate data from silos, thereby enabling cross-cutting analyses that were hitherto out of reach. This gives rise to significant challenges for data scientists simply discovering what data sets may be relevant to a task-in-hand. Given a data set of interest, several proposals have been made for indexing schemes that can identify related data sets. However, such schemes tend to build on similarity metrics that stop short of providing a clear explanation as to how an identified data set relates to a provided target. We address this problem by applying Natural Language Inference (NLI) to providing explanations as to how the attributes of discovered data sets relate to those of the target, in terms of a collection of semantic relations. We provide two approaches to inferring semantic relations: (a) by performing unsupervised intensional and extensional analysis of the data sources using Natural Language Processing techniques; and (b) by performing supervised learning of semantic relations by applying BERT over source schema information. The contributions of this paper are: an NLI strategy for providing explicit characterisation of semantic relations between data sets; two approaches to inferring the semantic relations; and an empirical evaluation of the approaches using open government data.
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Notes
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In practice, one could drastically reduce the potentially prohibitive space of attribute pairs to process by initially performing general similarity discovery (e.g., using \(D^3L\)Â [2]) and apply RNLI only on the resulted similar pairs.
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These parameters lead to the best results during validation.
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Acknowledgements
Mario Ramirez is supported by the Mexican National Council for Science and Technology (CONACYT). Alex Bogatu is supported by Innovate UK.
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Ramirez, M., Bogatu, A., Paton, N.W., Freitas, A. (2021). Natural Language Inference over Tables: Enabling Explainable Data Exploration on Data Lakes. In: Verborgh, R., et al. The Semantic Web. ESWC 2021. Lecture Notes in Computer Science(), vol 12731. Springer, Cham. https://doi.org/10.1007/978-3-030-77385-4_18
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