Structure
In the UNL approach, information conveyed by natural language is represented, sentence by sentence, as a hypergraph composed of a set of directed binary labeled links (referred to as relations) between nodes or hypernodes (the Universal Words, or simply UW), which stand for concepts. UWs can also be annotated with attributes representing context information.
As a matter of example, the English sentence ‘The sky was blue?!’ can be represented in UNL as follows:
In the example above, "sky(icl>natural world)" and "blue(icl>color)", which represent individual concepts, are UWs; "aoj" (= attribute of an object) is a directed binary semantic relation linking the two UWs; and "@def", "@interrogative", "@past", "@exclamation" and "@entry" are attributes modifying UWs.
UWs are supposed to represent universal concepts which are expressed in English words or in any other natural language in order to be humanly readable. They consist of a "headword" (the UW root) and a "constraint list" (the UW suffix between parentheses), the latter being used to disambiguate the general concept conveyed by the former. The set of UWs is organized in an ontology-like structure (the so-called "UW System"), where upper concepts are used to disambiguate the lower ones through "icl" (= is a kind of), "iof" (= is an instance of) and "equ" (= is equal to) relations.
Relations are expected to represent semantic links between words in every existing language. They can be ontological (such as "icl" and "iof" referred to above), logical (such as "and" and "or") and thematic (such as "agt" = agent, "ins" = instrument, "tim" = time, "plc" = place, etc.). There are currently 46 relations in the UNL Specs, and they define the syntax of UNL.
Attributes represent information that cannot be conveyed by UWs and relations. Normally, they represent information on tense (".@past", "@future", etc.), reference ("@def", "@indef", etc.), modality ("@can", "@must", etc.), focus ("@topic", "@focus", etc.), and so on.
Under the UNL Program, the process of representing natural language sentences in UNL graphs is called enconverting, and the process of generating natural language sentences out of UNL graphs is called deconverting. The former, which involves natural language analysis and understanding, is supposed to be carried out semi-automatically (i.e., in a computer-aided human basis); the latter is expected to be done fully automatically.
Read more about this topic: Universal Networking Language
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