document: Using Google distance to weight approximate ontology matches Discovering mappings between concept hierarchies is widely regarded as one of the hardest and most urgent problems facing the Semantic Web . The problem is even harder in domains where concepts are inherently vague and ill-defined , and can not be given a crisp definition . A notion of approximate concept mapping is required in such domains , but until now , no such notion is vailable . The first contribution of this paper is a definition for approximate mappings between concepts . Roughly , a mapping between two concepts is decomposed into a number of submappings , and a sloppiness value determines the fraction of these submappings that can be ignored when establishing the mapping . A potential problem of such a definition is that with an increasing sloppiness value , it will gradually allow mappings between any two arbitrary concepts . To improve on this trivial behavior , we need to design a heuristic weighting which minimises the sloppiness required to conclude desirable matches , but at the same time maximises the sloppiness required to conclude undesirable matches . The second contribution of this paper is to show that a Google based similarity measure has exactly these desirable properties . We establish these results by experimental validation in the domain of musical genres . We show that this domain does suffer from ill-defined concepts . We take two real-life genre hierarchies from the Web , we compute approximate mappings between them at varying levels of sloppiness , and we validate our results against a handcrafted Gold Standard . Our method makes use of the huge amount of knowledge that is implicit in the current Web , and exploits this knowledge as a heuristic for establishing approximate mappings between ill-defined concepts .
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