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Knowledge Graphs (KGs) represent heterogeneous domain knowledge on the Web and within organizations. There exist shapes constraint languages to define validating shapes to ensure the quality of the data in KGs. Existing techniques to extract validating shapes often fail to extract complete shapes, are not scalable, and are prone to produce spurious shapes. To address these shortcomings, we propose the Quality Shapes Extraction (QSE) approach to extract validating shapes in very large graphs, for which we devise both an exact and an approximate solution. QSE provides information about the reliability of shape constraints by computing their confidence and support within a KG and in doing so allows to identify shapes that are most informative and less likely to be affected by incomplete or incorrect data. To the best of our knowledge, QSE is the first approach to extract a complete set of validating shapes from WikiData. Moreover, QSE provides a 12x reduction in extraction time compared to existing approaches, while managing to filter out up to 93% of the invalid and spurious shapes, resulting in a reduction of up to 2 orders of magnitude in the number of constraints presented to the user, e.g., from 11,916 to 809 on DBpedia.
|Journal||Proceedings of the VLDB Endowment|
|Number of pages||10|
|Publication status||Published - 2023|
|Event||49th International Conference on Very Large Data Bases, VLDB 2023 - Vancouver, Canada|
Duration: 28 Aug 2023 → 1 Sept 2023
|Conference||49th International Conference on Very Large Data Bases, VLDB 2023|
|Period||28/08/2023 → 01/09/2023|
Bibliographical noteFunding Information:
This research was partially funded by the Danish Council for Independent Research (DFF) under grant agreement no. DFF-804800051B, the EU’s H2020 research and innovation programme under grant agreement No 838216, and the Poul Due Jensen Foundation.
© 2023, VLDB Endowment. All rights reserved.
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- 2 Active
Poul Due Jensen Professorate in Big Data and Artificial Intelligence
Hose, K., Jendal, T. E. & Hansen, E. R.
01/11/2019 → 31/10/2024
RelWeb: A Reliable Web of Data
01/09/2019 → 31/08/2023