Rule-based machine translation (RBMT; "Classical Approach" of MT) is machine translation systems based on linguistic information about source and target languages basically retrieved from (unilingual, bilingual or multilingual) dictionaries and grammars covering the main semantic, morphological, and syntactic regularities of each language respectively.
The following example can illustrate the general frame of RBMT: Minimally, to get a German translation of this English sentence one needs: And finally, we need rules according to which one can relate these two structures together.
With access to a large knowledge base, rule-based systems can be enabled to resolve many (especially lexical) ambiguities on their own.
In the following classic examples, as humans, we are able to interpret the prepositional phrase according to the context because we use our world knowledge, stored in our lexicons:I saw a man/star/molecule with a microscope/telescope/binoculars.
With a large enough ontology as a source of knowledge however, the possible interpretations of ambiguous words in a specific context can be reduced.