Thinknowlogy is grammar-based software,
designed to utilize Natural Laws of Intelligence in grammar,
in order to create intelligence through natural language in software,
which is demonstrated by:
• Programming in natural language;
• Reasoning in natural language:
• drawing conclusions (more advanced than scientific solutions),
• making assumptions (with self-adjusting level of uncertainty),
• asking questions (about gaps in the knowledge),
• detecting conflicts in the knowledge,
• detecting some cases of semantic ambiguity;
• Multilingualism, proving: Natural Laws of Intelligence are universal.
Thinknowlogy is open source. So, everyone can benefit
According to the evolution theory, there is no systematic relationship between reasoning and language, while the biblical worldview assumes that reasoning and language are closely related. Being based on the biblical worldview, only Thinknowlogy is able to process the meaning of words like definite article “the”, conjunction “or”, possessive verb “has / have” and past tense verbs “was / were” and “had”. Thinknowlogy has therefore worldwide unique results:
• Preserving the meaning: The current approach to knowledge technology is to reduce rich, meaningful sentences to linked keywords, after which is tried to reconstruct the original meaning by use of semantic vocabularies, statistics, et cetera. Thinknowlogy is purely grammar-based. It preserves the meaning throughout the system, by which no reconstruction is needed;
• Reasoning expressed in readable sentences: Automated reasoners are only able to produce a keyword, while Thinknowlogy is able to express its reasoning output as a readable sentence;
• Autonomous structuring of unstructured texts: IBM's Watson needs raw processing power "to find a needle in the haystack of unstructured texts" (quote from IBM). If computer systems were able to automatically structure their knowledge base, a search would take significantly less processing power. Thinknowlogy utilizes Natural Laws of Intelligence in grammar to autonomously structure its knowledge base;
• Detecting some cases of semantic ambiguity: Disambiguation is considered the biggest problem of NLP. The implementation of detecting some cases of semantic ambiguity is my first step towards a fundamental solution. Later I will implement autonomous semantic disambiguation.
The evolution theory is only an interpretation of the past, that fails to contribute to the future.