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A Framework for Relational Link Discovery

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AI 2005: Advances in Artificial Intelligence (AI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3809))

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Abstract

Link discovery is an emerging research direction for extracting evidences and links from multiple data sources. This paper proposes a self-organizing framework for discovering links from multi-relational databases. It includes main functional modules for developing adaptive data transformers and representation specification, multi-relational feature construction, and self-organizing multi-relational correlation and link discovery algorithms.

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, University of Technology Sydney, Australia

    Dan Luo

  2. Faculty of Software, Liaoning Technical University, China

    Chao Luo

  3. Beijing Vocational & Technical Institute of Industry, China

    Chunzhi Zhang

Authors
  1. Dan Luo
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  2. Chao Luo
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  3. Chunzhi Zhang
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Editor information

Editors and Affiliations

  1. Guangxi Normal University, College of CS and IT, Guilin, China, and University of Technology, Faculty of Engineering and Information Technology, Sydney, Australia

    Shichao Zhang

  2. Department of Electrical and Computer Systems Engineering, Monash University, 3800, Melbourne, Victoria, Australia

    Ray Jarvis

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© 2005 Springer-Verlag Berlin Heidelberg

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Luo, D., Luo, C., Zhang, C. (2005). A Framework for Relational Link Discovery. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_193

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  • DOI: https://doi.org/10.1007/11589990_193

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30462-3

  • Online ISBN: 978-3-540-31652-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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