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Visual Studio Code includes out-of-the-box support for. We recommend using Visual Studio Code to edit linguistic schema. It provides an important section about adding synonyms.

See Best practices to optimize Q&A in Power BI to learn about tips for designing and improving your data model.

Linguistic schemas can be edited, exported, and imported into Power BI Desktop. This format is related to the popular JSON format but provides a more flexible and easier-to-read syntax. In all cases, they describe how things are related by using everyday terms. Others relate multiple tables and columns together in complex relationships. Some relate columns with their containing tables. Some correspond directly with relationships in the data model. These phrasings come in many shapes and sizes. Or to describe the relationship between customers and phone numbers, you might say “customers have phone numbers”. Or to describe the relationship between customers and ages, you might say “ages indicate how old customers are”. For example, to describe the relationship between customers and products, you might say “customers buy products”. A phrasing is how you talk about (or phrase) the relationships between things. You're probably familiar with parts of speech, but phrasings might be a new term.

And it looks for relationships, like which columns are objects of a verb. For example, it looks for nouns, verbs, adjectives, phrasings, and other elements. When you ask Q&A a question, it looks for matches and relationships in the data to figure out the intention of your question. When you import or connect to a dataset, Power BI creates a linguistic schema based on the structure of the dataset. What's a linguistic schema?Ī linguistic schema describes terms and phrases that Q&A should understand for objects within a dataset, including parts of speech, synonyms, and phrasings. This article is for the data modelers, the people who optimize datasets to provide the best possible Q&A results. In other companies, these roles might be combined. A different set of employees would be the ones to 'consume' the data online. In some companies, employees known as data modelers or IT admins might be the ones to assemble the datasets, create the data models, and publish the datasets to Power BI. The second side is asking questions and exploring the data, or consuming. The first side is the preparation, or modeling. Power BI Desktop is where you manage your linguistic schemas. One way to improve the model is to add a linguistic schema that defines and categorizes terminology and relationships between table and column names in your dataset. The better the data model, the easier it will be for users to get quality answers. You can edit the linguistic schema to improve the Q&A answers for even better interactions.

When you ask Power BI Q&A a question, it makes a best effort to answer correctly. It's even more powerful when your data answers. It's powerful to use common phrases and natural language to ask questions of your data.

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