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Context-Aware Semantic Type Identification for Relational Attributes

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Abstract

Identifying semantic types for attributes in relations, known as attribute semantic type (AST) identification, plays an important role in many data analysis tasks, such as data cleaning, schema matching, and keyword search in databases. However, due to a lack of unified naming standards across prevalent information systems (a.k.a. information islands), AST identification still remains as an open problem. To tackle this problem, we propose a context-aware method to figure out the ASTs for relations in this paper. We transform the AST identification into a multi-class classification problem and propose a schema context aware (SCA) model to learn the representation from a collection of relations associated with attribute values and schema context. Based on the learned representation, we predict the AST for a given attribute from an underlying relation, wherein the predicted AST is mapped to one of the labeled ASTs. To improve the performance for AST identification, especially for the case that the predicted semantic types of attributes are not included in the labeled ASTs, we then introduce knowledge base embeddings (a.k.a. KBVec) to enhance the above representation and construct a schema context aware model with knowledge base enhanced (SCA-KB) to get a stable and robust model. Extensive experiments based on real datasets demonstrate that our context-aware method outperforms the state-of-the-art approaches by a large margin, up to 6.14% and 25.17% in terms of macro average F1 score, and up to 0.28% and 9.56% in terms of weighted F1 score over high-quality and low-quality datasets respectively.

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Authors and Affiliations

  1. Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education, Renmin University of China, Beijing, 100872, China

    Yue Ding, Yu-He Guo, Wei Lu & Xiao-Yong Du

  2. School of Information, Renmin University of China, Beijing, 100872, China

    Yue Ding, Yu-He Guo, Wei Lu & Xiao-Yong Du

  3. Tencent (Beijing) Technology Company Limited, Beijing, 100080, China

    Hai-Xiang Li

  4. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Mei-Hui Zhang

  5. College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China

    Hui Li

  6. Tencent (Shenzhen) Technology Company Limited, Shenzhen, 518057, China

    An-Qun Pan

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  1. Yue Ding
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Correspondence to Wei Lu.

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Ding, Y., Guo, YH., Lu, W. et al. Context-Aware Semantic Type Identification for Relational Attributes. J. Comput. Sci. Technol. 38, 927–946 (2023). https://doi.org/10.1007/s11390-021-1048-y

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  • DOI: https://doi.org/10.1007/s11390-021-1048-y

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