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2.3.1. Fundamental flaw in knowledge technology

Natural language is like algebra and a programming language:

In natural language, knowledge and logic are combined, in the same way as constants and variables are combined with symbols (and functions) of logic in algebra and programming languages.

In natural language, keywords – mainly nouns and proper nouns – provide the knowledge, while words like definite article “the”, conjunction “or”, basic verb “is/are”, possessive verb “has/have” and past tense verbs “was/were” and “had” provide the logical structure.

However, in knowledge technology, the logical structure of nature is ignored, because scientists are ignorant of this structure that is provided by nature. Instead of using this natural structure, keywords are linked by an artificially created structure (semantic techniques). Hence the struggling of this field to grasp the deeper meaning expressed by humans, and to automatically construct readable sentences from derived knowledge.

In other words, this field has a blind spot on the conjunction of logic and language:

A science integrates its involved disciplines. However, the field of AI and knowledge technology doesn't. It is unable to integrate (automated) reasoning and natural language:

• Reasoners (like Prolog) are able to reason, but their results – derived knowledge – can't be expressed in readable and automatically constructed sentences;
Chatbots, Virtual (Personal) Assistants and Natural Language Generation (NLG) techniques are unable to reason logically. They are only able to select human-written sentences, in which they may fill-in keywords;
Controlled Natural Language (CNL) reasoners are very limited in integrating both disciplines. They are limited to sentences with present tense verb “is/are”, and don't accept words like definite article “the”, conjunction “or”, possessive verb “has/have” and past tense verbs “was/were” and “had”.

Some people believe that meaning will emerge “by itself” (see Evolutionary Intelligence), while others believe that the meaning is preserved by parsing all words of a sentence. But they all fail to integrate reasoning and natural language, and to solve ambiguity.