Follow project on Twitter

2.3.5. Controlled Natural Language

Controlled Natural Language (CNL) reasoners allow the user to enter Predicate Logic as a natural language-like sentence. However, Predicate Logic doesn’t naturally go beyond the present tense of basic verb “to be”. So, also CNL reasoners doesn’t naturally go beyond verb “is/are”.

As a consequence, CNL reasoners are unable to convert a sentence like “Paul is a son of John” to “John has a son, called Paul” – and vice versa – in a generic way (=through an algorithm), because the latter sentence contains verb “has”. As a workaround, this conversion needs to be programmed for each and ever relationship:

• First of all, a rule must be added: “If a man(1) is-a-son-of a man(2) then the man(2) has-a-son-called the man(1)”;
• In order to trigger this rule, the relationship between “Paul” and “John” needs to be written with hyphens between the words: “Paul is-a-son-of John”. And the outcome will also contain hyphens: “John has-a-son-called Paul”;
• And the above must be repeated for each and ever similar noun: for “daughter”, for “father”, for “mother”, for “teacher”, for “student”, and so on.

This engineered workaround is clearly not generic, and therefore not scientific.

Besides that, while predicate logic describes both the Inclusive OR and Exclusive OR (XOR) function, CNL reasoners don't implement conjunction “or”. So, CNL reasoners are unable to generate the following question:

> Given: “Every person is a man or a woman.
> Given: “Addison is a person.

• Generated question:
< “Is Addison a man or a woman?

As a workaround for lacking an implementation of conjunction “or”, CNL reasoners need three sentences to describe sentence “Every person is a man or a woman” in a similar way:
• “Every man is a person.”;
• “Every woman is a person.”;
• “No woman is a man and no man is a woman.”.

Even though their workaround sentence “No woman is a man and no man is a woman” describes an Exclusive OR (XOR) function, scientists still fail to implement automatically generated questions in a generic way (=through an algorithm).

Both problems mentioned above – the inability to convert a sentence through an algorithm and the inability to generate a question through an algorithm – make clear that scientists fail to integrate reasoning (=natural intelligence) and natural language in artificial systems.

Lawyers have no problems to write down logic in legal documents, using natural language. So, why do scientists fail to integrate logic and natural language in artificial systems?

Legal documents are of course accurate in their description: “either ... or ...” is used to describe an Exclusive OR function, and the combination “and/or” is used to describe an Inclusive OR function. In daily life, instead of the combination “and/or”, we add “or both” to the sentence. In most other cases of conjunction “or”, we mean an Exclusive OR function.

So, in daily life, “Coffee or tea?” – short for “Either coffee or tea?” – describes an Exclusive OR function, while “Warm milk or a sleeping pill? Or both?” describes an Inclusive OR function.

Note: In these examples, the conjunction separates a series of words of the same word type. In these cases, a series of singular nouns. But also in imperative sentences like “Do …, or you'll have to face the consequences”, conjunction “or” implements an Exclusive OR function. Because the sender gives the receiver an exclusive choice: “Either do …, or you'll have to face the consequences”.