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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© 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
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