Englisch und Deutsch Kontext sprachbasierter KI - Inhaltserkennung, Begriffsklärung von Homophonen und polysemen Wörtern im Kontext.

Meaning depends on the external reality we observe in order to communicate with each other. External reality is the same for all people, regardless of their language. Of course, eskimos have more words for snow. If there was not identicity of meaning, Machine Translation from one language into another will be impossible. We have shown, in an academic publication, that there are around 4,000 word meanings common to all Languages, present in 40-50 thousand most used words. At present we work with 2,209 German word meanings and 1586 English word meanings, 177 of them are concepts. Different combinations of the meanings within the sentence and within the text provide accurate picture of the content of the sentence and of the text. Content Recognition means understanding the meaning of the word, the meaning of the sentence and the meaning of the text, as a whole. It includes such names as Data Mining, Entity Extraction, Text Classification, Text Attribution, Annotation. Without accurate Content Recognition there cannot be accurate Question Answering, Automated Reasoning, etc.

Our patented technology is superior to that of the competition, because with it one can find information that is not explicitly mentioned with words, information that is not contained in the synonyms of the particular word met in the text, information that is not present in the concept the word belongs to, information that cannot be found using statistical and co-occurrence methods.. No hitherto known analysis can detect this information. Nevertheless, this implicit information is understood, implicitly, consciously or unconsciously, by everybody who reads the text. The accuracy of our content recognition is based our Automated reasoning.

English and German off-line chatbots for Windows PC. Our chatbots use our content recognition technology. Our chatbot provides probable and accurate answers to questions. The answers are probable when the chatbot does not have sufficient information to provide an accurate answer. For example, if we ask "Where are my trousers?", the answer can be "Probably in the wardrobe". Of course, the trousers can also be in the washing machine, hanged to dry after washing, etc. Also, if we say "John is married to Ann.", our chatbot will ask "Do they have children?". Of course, such a statement can give rise to other questions as well. The dialogue with the chatbot can be continued only if the user asks something or tells something, in writing. The chatbot can remember the following user information, such as location of a key, medicament and money. For example, if the user writes "My blood pressure medicament is on the table.", the chatbot will remember where is the medicament. If the user forgets where is blood pressure medicament, the user can ask the chatbot "Where is my blood pressure medicament?". The chatbot will retrieve the information. This ability of the chatbot can be extended to cover any valuable information in the next versions. Our chatbot, uses our patented Automated Reasoning in Natural Language to find implicit information in Natural Language Sentences. Nowadays, Online chatbots are used to engage the user in conversation and to provide information about your services, if the user asks what you offer. Your competitors will also know what you offer and they will soon follow suit. Your innovative services will no longer be new, because your competitors will also offer the same services and you will lose customers.

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