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Volume 38 Issue 4
July  2023
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Ding Y, Guo YH, Lu W et al. Context-aware semantic type identification for relational attributes. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38(4): 927−946 July 2023. DOI: 10.1007/s11390-021-1048-y.
Citation: Ding Y, Guo YH, Lu W et al. Context-aware semantic type identification for relational attributes. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38(4): 927−946 July 2023. DOI: 10.1007/s11390-021-1048-y.
shu

Context-Aware Semantic Type Identification for Relational Attributes

  • 1.

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

  • 2.

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

  • 3.

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

  • 4.

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

  • 5.

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

  • 6.

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

Funds: The work was supported by the National Key Research and Development Program of China under Grant No. 2020YFB2104100, the National Natural Science Foundation of China under Grant Nos. 61972403 and U1711261, the Fundamental Research Funds for the Central Universities of China, the Research Funds of Renmin University of China, and Tencent Rhino-Bird Joint Research Program.
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  • Author Bio:

    Yue Ding received her B.S. degree in Internet of Things from Nanjing University of Posts and Telecommunications, Nanjing, in 2018. She is currently pursuing her M.S. degree in Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education and School of Information at Renmin University of China, Beijing. Her research interests include data integration and machine learning

    Yu-He Guo received her B.S. degree in computer science from Renmin University of China, Beijing, in 2018. She is currently pursuing her M.S. degree in Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education and School of Information in Renmin University of China, Beijing. Her research interests lie in natural language processing and data integration

    Wei Lu is currently an associate professor in Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education and School of Information at Renmin University of China, Beijing. He received his Ph.D. degree in computer applied technology from Renmin University of China, Beijing, in 2011. His research interests include query processing in the context of spatiotemporal, cloud database systems and applications. He is a member of CCF

    Hai-Xiang Li is currently a senior expert at Tencent (Beijing) Technology Company Limited, Beijing. His research interests include transaction processing, query optimization, distributed consistency, high availability, database system architecture, cloud database and distributed database systems. He is a member of CCF

    Mei-Hui Zhang received her Ph.D. degree in computer science from National University of Singapore, Singapore, in 2013. She is currently a professor with Beijing Institute of Technology, Beijing, and was an assistant professor with Singapore University of Technology and Design, Singapore, from 2014 to 2017. Her research interests include big data management and analytics, large-scale data integration, modern database systems, block chain and AI. She has served as PC Vice-Chair of ICDE 2018 and associate editor of VLDB 2018, VLDB 2019, VLDB 2020 and SIGMOD 2021. She is a winner of VLDB 2020 Early Career Research Contribution Award. She is a member of CCF, ACM and IEEE

    Hui Li is currently a professor in College of Computer Science and Technology at Guizhou University, Guiyang. He received his Ph.D. degree in computer software and theory from Renmin University of China, Beijing, in 2012. His research interests include large-scale data analytics, high-performance database systems and data-driven intelligent applications. He is a member of CCF, ACM and IEEE

    An-Qun Pan is a technical director at Tencent (Shenzhen) Technology Company Limited, Shenzhen, with more than 15 years of experience in the research and development of distributed computing and storage systems. He is currently responsible for the research and development of distributed database system (TDSQL). He is a member of CCF

    Xiao-Yong Du is a professor in Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education and School of Information at Renmin University of China, Beijing. He received his Ph.D. degree in computer science from Nagoya Institute of Technology, Nagoya, in 1997. His research focuses on intelligent information retrieval, high performance database and unstructured data management. He is a fellow of CCF

  • Corresponding author:

    lu-wei@ruc.edu.cn

  • Received Date: October 04, 2020
  • Accepted Date: June 08, 2021
    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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