Abstract
An increasing number and size of datasets abiding by the Linked Data paradigm are published everyday. Discovering links between these datasets is thus central to achieve the vision behind the Data Web. Declarative Link Discovery (LD) frameworks rely on complex Link Specification (LS) to express the conditions under which two resources should be linked. Understanding such LS is not a trivial task for non-expert users, particularly when such users are interested in generating LS to match their needs. Even if the user applies a machine learning algorithm for the automatic generation of the required LS, the challenge of explaining the resultant LS persists. Hence, providing explainable LS is the key challenge to enable users who are unfamiliar with underlying LS technologies to use them effectively and efficiently. In this paper, we address this problem by proposing a generic approach that allows a LS to be verbalized, i.e., converted into understandable natural language. We propose a summarization approach to the verbalized LS based on the selectivity of the underlying LS. Our adequacy and fluency evaluations show that our approach can generate complete and easily understandable natural language descriptions even by lay users.
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For a complete description of the vocabulary, see http://nlp.stanford.edu/software/dependencies_manual.pdf.
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The survey interface can be accessed at https://umfragen.uni-paderborn.de/index.php/186916?lang=en.
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Acknowledgement
This work has been supported by the BMVI projects LIMBO (GA no. 19F2029C) and OPAL( no. 19F2028A), Eurostars Project SAGE (GA no. E!10882) as well as the H2020 projects SLIPO (GA no.731581).
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Ahmed, A.F., Sherif, M.A., Ngomo, AC.N. (2019). LSVS: Link Specification Verbalization and Summarization. In: Métais, E., Meziane, F., Vadera, S., Sugumaran, V., Saraee, M. (eds) Natural Language Processing and Information Systems. NLDB 2019. Lecture Notes in Computer Science(), vol 11608. Springer, Cham. https://doi.org/10.1007/978-3-030-23281-8_6
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