Back Office or BMS
This term refers to the platform that allows you to administrate the bot.
Content of the bot
This is a question / answer pair set by an administrator. Example:"What's your name? - the algorithm analyzes the sentence through a distance calculation and searches for the closest knowledge. The "What is your name" knowledge is calculated as the closest knowledge, with the highest matching score. He understands this knowledge and gives the answer associated with it: "My name is XXX".
A formulation is a different way of asking the same question.
The knowledge base includes all the knowledges of your bot.
This is a knowledge base that groups so-called social interactions common to all projects. It allows the bot to bring a more human / intimate look to the dialog.
A decision tree represents a knowledge item divided in several branches to offer several user paths
These are specific and custom rules that you can add to enhance the rules of your internaut activity type knowledge.
a redirection within a knowledge represents the action of redirecting to another knowledge on the call (perhaps automatic or inserted in clickable form).
This is a category of knowledge to classify and find your knowledge more easily (by tag, etc.). You can also add subtags.
Matching groups are groups of words with a similar meaning in a specific context.
For example: In the 'How to edit' matching group, there is 'How to change'/ 'Edit item' / ...
Global sentences represent bot sentences or answers during a dialog that are not handled directly by the knowledge base and that can be used to handle situations that are unique to the dialog such as the misunderstanding of a user sentence.
Depending the understanding of the bot, it may propose knowledge that it considers close to the user's question. This is what we call the bot's reword. However, some knowledge has no interest in being reworded even if the matching result indicates that this knowledge is close to the user's question and could therefore correspond to his initial request. This is the case for example for social knowledge or knowledge such as "I want to cancel my contract".
The slot knowledge is used to capture and record information. Thus, if the user asks the bot "I want to buy a train ticket to Paris on July 8", the bot is able to extract the information "Paris" and "July 8". In addition, if there is missing information that the bot needs to continue the user request, it will be able to check the missing slots and request the information from the user.
This is a space on which the bot relies to give an answer. Within the same knowledge base, you can create multiple consultation spaces; the advantage being that you can give a different answer to the same question from one space to another.
Example: "How are you? » - « How are you doing? " - "Are you okay?" - "How are you alright?".
the context condition is used to check that a condition is effective to trigger a knowledge.
A bounce condition is used to redirect to other knowledge after triggering a decision tree.
Close knowledge are knowledge items with a high similarity rate to a user question. You can look up close knowledge when using the search function (in the test dialog box or in a dialog history).
A dialog represents all interactions between the bot (the operator in the case of a Livechat dialog) and the user.
An interaction represents a question / answer exchange between the bot and the user.
Example:"What's the weather like?" + "The weather is nice.". This exchange is equivalent to an interaction.
The qualification mode allows the use of knowledge in the published and validated state. Dialogs are not counted in the analytics.
Chatbot or Chatbox
Chatbot:** the chatbot represents the concept of a conversational bot that allows performing a natural language textual dialog via a dialog box (also called chatbox).
The Livechat service allows the connection between the user and a human operator to exchange as direct messaging via the dialog box.
The meta bot lets your main bot find knowledge in other bots.