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Dependency-Aware Core Column Discovery for Table Understanding

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The Semantic Web – ISWC 2023 (ISWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14265))

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Abstract

In a relational table, core columns represent the primary subject entities that other columns in the table depend on. While discovering core columns is crucial for understanding a table’s semantic column types, column relations, and entities, it is often overlooked. Previous methods typically rely on heuristic rules or contextual information, which can fail to accurately capture the dependencies between columns and make it difficult to preserve their relationships. To address these challenges, we introduce Dependency-aware Core Column Discovery (DaCo), an iterative method that uses a novel rough matching strategy to identify both inter-column dependencies and core columns. Unlike other methods, DaCo does not require labeled data or contextual information, making it suitable for practical scenarios. Additionally, it can identify multiple core columns within a table, which is common in real-world tables. Our experimental results demonstrate that DaCo outperforms existing core column discovery methods, substantially improving the efficiency of table understanding tasks.

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Notes

  1. 1.

    By dependency, we are implying that column y is an attribute of column x if y depends on x [28].

  2. 2.

    Our solution can be applied to table understanding tasks since research on table understanding assumes an overlap between the table and the KG.

  3. 3.

    https://github.com/barrel-0314/daco.

  4. 4.

    It includes tough tables generated from SemTab for dealing with the tabular data to KG matching problem.

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Acknowledgements

We would like to thank Jiaoyan Chen for his useful comment on this paper. This work is supported by the State Grid Technology Project “research and application of key technologies for automatic graphic construction of power grid control system driven by model and data”, the National Natural Science Foundation of China under the grant numbers [62061146001, 62072099, 62232004], the “Zhishan” Scholars Programs of Southeast University, and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, China

    Jingyi Qiu, Aibo Song, Jiahui Jin, Tianbo Zhang, Jingyi Ding & Xiaolin Fang

  2. State Grid Zhejiang Electric Power Company, LTD, Hangzhou, China

    Jianguo Qian

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  1. Jingyi Qiu
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  2. Aibo Song
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Correspondence to Jiahui Jin .

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Editors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Terry R. Payne

  2. University of Bologna, Bologna, Italy

    Valentina Presutti

  3. Southeast University, Nanjing, China

    Guilin Qi

  4. Universidad Politécnica de Madrid, Madrid, Spain

    María Poveda-Villalón

  5. Huawei Technologies R&D UK, Edinburgh, UK

    Giorgos Stoilos

  6. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

    Laura Hollink

  7. IT University of Copenhagen, Copenhagen, Denmark

    Zoi Kaoudi

  8. Nanjing University, Nanjing, China

    Gong Cheng

  9. Tsinghua University, Beijing, Beijing, China

    Juanzi Li

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Qiu, J. et al. (2023). Dependency-Aware Core Column Discovery for Table Understanding. In: Payne, T.R., et al. The Semantic Web – ISWC 2023. ISWC 2023. Lecture Notes in Computer Science, vol 14265. Springer, Cham. https://doi.org/10.1007/978-3-031-47240-4_9

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