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In this paper we propose an ontology matching paradigm based on the idea of harvesting the Semantic Web, i.e., automatically finding and exploring multiple and heterogeneous online knowledge sources to derive mappings. We adopt an experimental approach in the context of matching two real life, large-scale ontologies to investigate the potential of this paradigm, its limitations, and its relation to other techniques. Our experiments yielded a promising baseline precision of 70% and identified a set of critical issues that need to be considered to achieve the full potential of the paradigm. Besides providing a good performance as a stand-alone matcher, our paradigm is complementary to existing techniques and therefore could be used in hybrid tools that would further advance the state of the art in the ontology matching field.