Abstract
Making sensible queries on databases collected from different organizations presents a challenging task for linking semantic equivalent data facts. Current techniques primarily focused on performing pair-wise attribute matching and paid little attention towards discovering probabilistic structural dependencies by exploiting the ontological domain knowledge of tables, attributes and tuples to construct hierarchical cluster mapping trees. In this paper, we present Ontology Guided Data Linkage (OGDL) framework for self-organizing heterogeneous data sources into homogeneous ontological clusters through multi-faceted classification. Through the evaluation on real-world data, we demonstrate the robustness and accuracy of our system.
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Gollapalli, M., Li, X., Wood, I., Governatori, G. (2011). Ontology Guided Data Linkage Framework for Discovering Meaningful Data Facts. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25856-5_19
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DOI: https://doi.org/10.1007/978-3-642-25856-5_19
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