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Please share the difference between homonyms and synonyms in data science with examples.

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When we discover that two concepts are synonyms (for example, sofa and couch, class expression owl:equivalentClass is used. 

So, any occurrence that was a member of the class sofa is now also a part of the class couch and vice-versa. Advantage of this approach is that the "context" of this identity is automatically scoped to the philosophy in which the equivalent statement is made. If there is a very small mapping ontology between a furniture ontology and an internal decorating ontology, we can say that these two are equivalent. 

In another condition, if you want to preserve the difference between a couch and a sofa, you do that by simply not adding the mapping ontology that represented them equivalent.


In a particular language, a set of characters may express more than one concept. Example the word "mole," for which WordNet has 6 word meanings. The Semantic Web way is to provide each its namespace; for example, I might mention to the counterspy mole as cia:mole and the burrowing rodent as the mammal:mole. The nice thing about this is, anyone ever needed to refer to the rodent they have to refer to mammal:mole.

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