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SANTOS: Relationship-based Semantic Table Union Search (2209.13589v1)

Published 27 Sep 2022 in cs.DB

Abstract: Existing techniques for unionable table search define unionability using metadata (tables must have the same or similar schemas) or column-based metrics (for example, the values in a table should be drawn from the same domain). In this work, we introduce the use of semantic relationships between pairs of columns in a table to improve the accuracy of union search. Consequently, we introduce a new notion of unionability that considers relationships between columns, together with the semantics of columns, in a principled way. To do so, we present two new methods to discover semantic relationship between pairs of columns. The first uses an existing knowledge base (KB), the second (which we call a "synthesized KB") uses knowledge from the data lake itself. We adopt an existing Table Union Search benchmark and present new (open) benchmarks that represent small and large real data lakes. We show that our new unionability search algorithm, called SANTOS, outperforms a state-of-the-art union search that uses a wide variety of column-based semantics, including word embeddings and regular expressions. We show empirically that our synthesized KB improves the accuracy of union search by representing relationship semantics that may not be contained in an available KB. This result hints at a promising future of creating a synthesized KBs from data lakes with limited KB coverage and using them for union search.

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Authors (7)
  1. Aamod Khatiwada (6 papers)
  2. Grace Fan (3 papers)
  3. Roee Shraga (20 papers)
  4. Zixuan Chen (50 papers)
  5. Wolfgang Gatterbauer (45 papers)
  6. Renée J. Miller (15 papers)
  7. Mirek Riedewald (16 papers)
Citations (51)
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Summary

An Expert Overview of "SANTOS: Relationship-based Semantic Table Union Search"

The paper "SANTOS: Relationship-based Semantic Table Union Search," presents a novel approach to table union search by leveraging relationship semantics between columns in tables. Traditional methods define table unionability primarily based on column metadata and values, operating under the assumption that two tables are unionable if they share columns of attributes drawn from similar domains. This paper challenges this notion by proposing a more comprehensive definition of unionability that incorporates relationship semantics. It introduces SANTOS, a method that identifies unionable tables by examining semantic relationships between column pairs, thereby enhancing the accuracy and relevance of union search.

Key Contributions

  1. Semantic Relationship Definition: The authors redefine unionability by including semantic relationships between column pairs. They argue that similar column semantics are necessary but insufficient for unionability, as the semantic relationships between columns also play a critical role.
  2. Methods for Relationship Discovery:
    • Knowledge Base (KB) Method: SANTOS uses an existing KB to discover semantic relationships between columns, mapping column pairs to known relationships in the KB.
    • Synthesized KB Method: In response to limited KB coverage over real data lakes, SANTOS introduces a synthesized KB that captures co-occurrence information from data lakes themselves. This method does not rely solely on an external KB, making it robust in scenarios with sparse KB coverage.
  3. Empirical Evaluation and Benchmarks: The effectiveness of SANTOS is evaluated using three benchmarks: a repurposed TUS benchmark, and two newly developed benchmarks (SMALL and LARGE) using real open data lake tables. The results demonstrate that SANTOS significantly outperforms a state-of-the-art baseline (D3LD^3L), which does not consider relationship semantics.
  4. Impact of Synthesized KB: The synthesized KB improves the unionability search by providing relationship semantics not captured in the curated KB, suggesting potential for better data integration and search processes within data lakes.

Implications and Future Developments

The introduction of SANTOS has significant theoretical and practical implications. Theoretically, it advances the understanding of table unionability by highlighting the importance of relationship semantics. Practically, SANTOS offers a more accurate and holistic approach to discovering unionable tables, which is crucial for data scientists seeking to integrate datasets for analysis or machine learning tasks.

In terms of future developments, SANTOS opens avenues for further exploration of synthesized KBs. One potential area of research could involve optimizing synthesized KB creation, particularly focusing on performance improvements for large-scale data lakes. Additionally, future work could explore integrating SANTOS with domain-specific enterprise KBs to further enhance its applicability across diverse datasets.

Overall, "SANTOS: Relationship-based Semantic Table Union Search" provides a compelling framework that substantially improves upon existing methodologies by integrating semantic relationships into the table union search problem, thereby enhancing the accuracy and robustness of data integration processes in data lakes.

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