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A hybrid quantum approach to leveraging data from HTML tables

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Abstract

The Web provides many data that are encoded using HTML tables. This facilitates rendering them, but obfuscates their structure and makes it difficult for automated business processes to leverage them. This has motivated many authors to work on proposals to extract them as automatically as possible. In this article, we present a new unsupervised proposal that uses a hybrid approach in which a standard computer is used to perform pre- and post-processing tasks and a quantum computer is used to perform the core task: guessing whether the cells have labels or values. The problem is addressed using a clustering approach that is known to be NP using standard computers, but our proposal can solve it in polynomial time, which implies a significant performance improvement. It is novel in that it relies on an entropy-preservation metaphor that has proven to work very well on two large collections of real-world tables from the Wikipedia and the Dresden Web Table Corpus. Our experiments prove that our proposal can beat the state-of-the-art proposal in terms of both effectiveness and efficiency; the key difference is that our proposal is totally unsupervised, whereas the state-of-the-art proposal is supervised.

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Acknowledgements

This work was supported by the Spanish and the Andalusian R&D programmes through Grants TIN2016-75394-R, PID2020-112540RB-C44 (MCIN/AEI/10.13039/501100011033), and P18-RT-1060 (FEDER funds from the EU). The standard computer used to perform the experimentation was partially supported by Dinamic Area, S.L. and the quantum computer was fully supported by D-Wave Systems, Inc.

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  1. ETSI Informática, University of Sevilla, Avda. Reina Mercedes, s/n, Sevilla, 41012, Spain

    Patricia Jiménez, Juan C. Roldán & Rafael Corchuelo

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  1. Patricia Jiménez
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  3. Rafael Corchuelo
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Correspondence to Rafael Corchuelo.

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Jiménez, P., Roldán, J.C. & Corchuelo, R. A hybrid quantum approach to leveraging data from HTML tables. Knowl Inf Syst 64, 441–474 (2022). https://doi.org/10.1007/s10115-021-01636-7

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