Build a question answering solution in Azure

The Language service includes a question answering capability, which enables you to define a knowledge base of question and answer pairs that can be queried using natural language input. The knowledge base can be published to a REST endpoint and consumed by client applications, commonly bots.

The knowledge base can be created from existing sources, including:

Web sites containing frequently asked question (FAQ) documentation.
Files containing structured text, such as brochures or user guides.
Built-in chit chat question and answer pairs that encapsulate common conversational exchanges.

A question answering knowledge base is a form of language model, which raises the question of when to use question answering, and when to use the conversational language understanding capabilities of the Language service.

The two features are similar in that they both enable you to define a language model that can be queried using natural language expressions. However, there are some differences in the use cases that they are designed to address, as shown in the following table:

	Question answering	Language understanding
Usage pattern	User submits a question, expecting an answer	User submits an utterance, expecting an appropriate response or action
Query processing	Service uses natural language understanding to match the question to an answer in the knowledge base	Service uses natural language understanding to interpret the utterance, match it to an intent, and identify entities
Response	Response is a static answer to a known question	Response indicates the most likely intent and referenced entities
Client logic	Client application typically presents the answer to the user	Client application is responsible for performing appropriate action based on the detected intent

The two services are in fact complementary. You can build comprehensive natural language solutions that combine conversational language understanding models and question answering knowledge bases.

To create a question answering solution, you can use the REST API or SDK to write code that defines, trains, and publishes the knowledge base. However, it is more common to use the Language Studio web interface to define and manage a knowledge base.

Although you can often create an effective knowledge base that consists of individual question and answer pairs, sometimes you might need to ask follow-up questions to elicit more information from a user before presenting a definitive answer. This kind of interaction is referred to as a multi-turn conversation.

The chitchat option allows several personalities.