We aim at providing a complementary layer for the web semantics, catering for bottom-up phenomena that are empirically observable on the Semantic Web rather than being merely asserted by it. We focus on meaning that is not associated with particular semantic descriptions, but emerges from the multitude of explicit and implicit links on the web of data. We claim that the current approaches are mostly top-down and thus lack a proper mechanisms for capturing the emergent aspects of the web meaning. To fill this gap, we have proposed a framework based on distributional semantics (a successful bottom-up approach to meaning representation in computational linguistics) that is, however, still compatible with the top-down Semantic Web principles due to inherent support of rules. We evaluated our solution in a knowledge consolidation experiment, which confirmed the promising potential of our approach.
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