Universal meaning and understanding
How - Universal meaning and understanding
When meaning is expressed, it is almost always done within a certain context. When we do not provide sufficient context there is a chance that a meaning is not properly understood. From life experience, we know that when we assume the context is clear, whereas in fact it is not, something can go wrong.
The same is true for words that can have several meanings. For instance, when you just see the word 'bit' without further contextual information, you cannot know whether bit is placed in the context of 'a little bit', 'to bite', 'the horse's mouth', the information bit of a computer's memory and so on.
Once sufficient context is provided, you can understand what is truly meant.
For translations, it is very relevant to understand context. Translation tools can guess context, but if they don't know for certain, they will not provide correct translations.
The same is true for datamodels. Datamodels tend to describe the data that is presented, but when you see a data field 'name', is it clear what is meant?
Fundamentally, 1LANGUAGE facilitates words to be expressed with minimalised but sufficient context, so meaning can become efficiently clear. 1LANGUAGE breaks down meaning to its smallest possible level. Within nature, the smallest possible quantity is referred to as a quantum. In line with this, 1LANGUAGE applies 'quantum semantics' to define meaning. Together, the quantum semantic building blocks can be combined to make a 'composite expression'. As the composite expression contains contextual 'blocks of meaning', it can be interpreted, understood and processed from its relevant contexts.
This may sound all very abstract, so let's show an example for the concept 'name'.
1LANGUAGE first defines meaning, by establishing the context “to what question does the expression respond (in this case for the concept 'name')”.
1LANGUAGE distinguishes for instance the interrogatives 'who', 'what', 'how', 'where and when', 'how much or how many' and 'why'.
For the concept 'name', let's assume in this case, we want to respond to the question 'who?' or 'who is it?').
After establishing the interrogative context, 1LANGUAGE defines further context of 'name', as name can be applied differently in contexts of people, organisations or animals for instance. In the case of people, 'name' can be a first name, last name, middle name, alias, artist name, full formal legal name and so on. For an organisation, 'name' can be a legal entity name, a brand name, etc.
It is here, where we can define further contexts such as 'name in the context of people' and in the context of 'first name' to a more exact meaning.
When we bring these contexts together, we literally would get:
“In the context of responding to the question who, name, in the context of a person's first name.”
This provides for more available context to interpret and translate 'name' into its intended meaning, for instance into other languages or into other software applications.
1LANGUAGE provides an open dictionary with terms and expressions, including translations that everyone can use. Every term or expression has a unique index number, the USEN (Uniform Entity and Transaction Protocol Semantic Extension Number).
Rather than providing a term or expression, you can also communicate with a USEN.
Below is an example of how it works.
Suppose we have the following terms with their USEN numbers and translations.
|Language||USEN: 11||USEN: Q1||USEN: Q1_1||USEN: Q1_1_1|
|English||who||name||person's name||first name|
|Spanish||quien||nombre||nombre de persona||primer nombre|
|Arabic||من الذى||اسم||اسم الشخص||الاسم الاول|
|Hindi||कौन||नाम||व्यक्ति का नाम||प्रथम नाम|
Using the underscore to express 'in the context of', we would be able to represent:
“In the context of responding to the question who, name, in the context of a person's first name” with the following formula:
Suppose I1_Q1_1_1 becomes the name of a datafield of an entity (someone or something that is identifiable), representing this concept of first name, then:
- I could ask 谁 (who - in Chinese) to the entity, which would be represented as 'I1'. Querying the data fields of the entity, the datafield 'I1_Q1_1_1' would be returned as a datafield containing 'I1'. In Chinese, I would be answered 名字 ('a person's first name') in response to my Chinese question 谁 (who)?.
- Likewise, I can ask название (name – in Russian) or प्रथम नाम (first name – in Hindi) and be responded 'first name' (with its data) in the respective language.
- I can get an answer in any language, no matter in what language I originally used to define 'first name' in the first place.
The above is a simplification of how quantum semantics, hyperstructured data and translations work with 1 LANGUAGE. While communication with USENs isn't very different from what is presented above, 1LANGUAGE can facilitate considerably more contextual structure than in this example. Moreover, the above doesn't go into details on how 1LANGUAGE is resolving synonyms, language ambiguities, how consensus on definitions is managed and many more features and functionalities.
The bottom line here is that the hyperstructuring of information, with the quantum semantics of 1LANGUAGE, can facilitate universal meaning and understanding, across languages, between man and machine and human and artificial intelligence.