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In semantics, dynamic and formal equivalence are approaches to translation that prioritize either the meaning or literal structure of the source text respectively. The distinction was originally articulated by Eugene Nida in the context of Bible translation .
In computer metadata, semantic equivalence is a declaration that two data elements from different vocabularies contain data that has similar meaning. There are three types of semantic equivalence statements: Class or concept equivalence. A statement that two high level concepts have similar or equivalent meaning. Property or attribute equivalence.
Semantic translation takes advantage of semantics that associate meaning with individual data elements in one dictionary to create an equivalent meaning in a second system. An example of semantic translation is the conversion of XML data from one data model to a second data model using formal ontologies for each system such as the Web Ontology ...
A semantic translation's goal is to stay as close as possible to the semantic and syntactic structures of the source language, allowing the exact contextual meaning of the original. [21] A communicative translation's goal is to produce an effect on the readers as close as possible to that as produced upon the readers of the original. [22]
In formal language theory, weak equivalence of two grammars means they generate the same set of strings, i.e. that the formal language they generate is the same. In compiler theory the notion is distinguished from strong (or structural) equivalence, which additionally means that the two parse trees [clarification needed] are reasonably similar in that the same semantic interpretation can be ...
Nida's dynamic-equivalence theory is often held in opposition to the views of philologists who maintain that an understanding of the source text (ST) can be achieved by assessing the inter-animation of words on the page, and that meaning is self-contained within the text (i.e. much more focused on achieving semantic equivalence).
The output of S-Match is a set of semantic correspondences called mappings attached with one of the following semantic relations: disjointness (⊥), equivalence (≡), more specific (⊑) and less specific (⊒). In our example the algorithm will return a mapping between "car" and "automobile" attached with an equivalence relation.
The semantic gap characterizes the difference between two descriptions of an object by different linguistic representations, for instance languages or symbols. According to Andreas M. Hein, the semantic gap can be defined as "the difference in meaning between constructs formed within different representation systems". [ 1 ]