In the April update of Power BI Desktop we added the ability to import and export the linguistic schema for your model. When you ask Q&A a question it tries to parse your question and identify the nouns, verbs, adjectives, and other elements of the question. In the linguistic schema you tag columns in the table as different grammatic elements to help it do this, and define words that a users might use to phrase a question. For instance, you state the columns that are the subject and the object of the verb. Q&A uses all this information together with any enhancements that you make to provide a better answer, auto completion, and summary of the questions.
You can use the Synonyms pane to add alternative words that users can use to refer to tables, columns, and measures in your model. To add phrasings to your model you’ll manually edit the linguistic schema. You can export and import the linguistic for a model by opening it in Power BI Desktop, going to the Modeling tab in the ribbon, finding the Q&A group and clicking Linguistic Schema.
Samples
Learning from examples is a good way to start editing your own linguistic schemas. You can download a sample .yaml file and sample .pbix file from here. These contain examples of common phrasings and synonyms for an Olympic medals dataset.
Linguistic schema YAML files
Linguistic schema files are represented in a YAML format. This format is related to the very popular JSON format but provides a more flexible and easier-to-read syntax.
Set up an editor for Linguistic Schema YAML files
We recommend using Visual Studio Code to edit linguistic schema YAML files. Visual Studio Code includes out-of-the-box support for YAML files and can be extended to specifically validate that files conform to the Power BI linguistic schema format.
- Install Visual Studio Code
- Associate YAML files with Visual Studio Code
- Double-click on a linguistic schema YAML file (.lsdl.yaml) that you exported from Power BI Desktop.
- Choose “Visual Studio Code” and select “Always use this app to open .yaml files”.
- Alternately you can right-click on a .lsdl.yaml file and choose “Open with Code”
- In Visual Studio Code, install YAML Support by Red Hat extension
- Click the Extensions tab (last one on the left) or CTRL+SHIFT+X
- Search for “yaml” and select “YAML Support by Red Hat” in the list
- Click Install and then Reload
- If you make a change to a .lsdl.yaml file that does not conform to the linguistic schema format, you should now see validation squiggles like this to indicate issues:
Editing the linguistic schema
When you first export your linguistic schema from Desktop, most or all of the content in the file will be automatically generated by the Q&A engine. These generated entities, words (synonyms), relationships and phrasings are designated with a State: Generated tag and are included in the file mostly for informational purposes but can be a useful starting point for your own changes. When you import your linguistic schema file back into Power BI Desktop, anything that is marked State: Generated is actually ignored (and later regenerated) so if you’d like to make a change to some generated content, make sure to remove the corresponding State: Generated tag as well. Similarly, if you want to remove some generated content, you’ll need to change the State: Generated tag to State: Deleted so that it won’t be regenerated when you import your linguistic schema file.
Adding phrasings to the linguistic schema
A phrasing is how you talk about (or “phrase”) the relationships between things. For example, to describe the relationship between customers and products, you might say “customers buy products”. Or to describe the relationship between customers and ages, you might say “ages indicate how old customers are”. Or to describe the relationship between customers and phone numbers, you might simply say “customers have phone numbers”.
These phrasings come in a variety of shapes and sizes. Some correspond directly with relationships in the semantic model. Some relate columns with their containing tables. Others relate multiple tables and columns together in complex relationships. In all cases, they describe how things are related using everyday terms.
Where Do Phrasings Come From?
Many simple phrasings are added to the linguistic schema automatically, based on the structure of the model and some guesses based on column names. For example:
- Most columns will be related to their containing table with a simple phrasing like “products have descriptions”
- Model relationships result in default phrasings for both directions of the relationship like “orders have products” and “products have orders”.
- Some model relationships can, based on their column names, get a more complex default phrasing like “orders are shipped to cities”
There are plenty of ways your users will talk about things that Q&A can’t guess, however. For those, you may want to add your own phrasings manually.
Why Should I Add Phrasings?
The first reason for adding a phrasing is to define a new term. For example, if you want to be able to ask “list the oldest customers”, you must first teach Q&A what you mean by “old”. You would do so by adding a phrasing like “ages indicate how old customers are”.
The second reason for adding a phrasing is to resolve ambiguity. Basic keyword search only goes so far when words have more than one meaning. For example, “flights to Chicago” means something quite different than “flights from Chicago”, but Q&A won’t know which one you mean unless you add the phrasings “flights are from departure cities” and “flights are to arrival cities”. Similarly, the distinction between “cars that John sold to Mary” and “cars that John bought from Mary” will only be understood once you add the phrasings “customers buy cars from employees” and “employees sell customers cars”.
The final reason for adding a phrasing is to improve restatements. Rather than Q&A echoing back to you “Show the customers and their products”, it would be clearer if it were to say “Show the customers and the products they bought” or “Show the customers and the products they reviewed”, depending on how it understood the question. Adding custom phrasings allows restatements to be more explicit and unambiguous.
What Kinds of Phrasings Are There?
To understand the different types of phrasings, you’re first going to need to remember a couple of very basic grammar terms:
- A noun is a person, place, or thing.
- Examples: car, teenager, Marty, flux capacitor
- A verb is an action or state of being.
- Examples: hatch, burst, devour, eject
- An adjective is a descriptive word that modifies a noun.
- Examples: powerful, magical, golden, stolen
- A preposition is a word used before a noun to relate it to a previous noun, verb or adjective
- Examples: of, for, near, from
Attribute Phrasings
Attribute phrasings are the workhorse of Q&A, used when one thing is acting as an attribute of another thing. They’re simple, straightforward and perform most of the heavy lifting when a subtler, more detailed phrasing hasn’t been defined. Attribute phrasings are described using the basic verb “have” (e.g. “products have categories”), and automatically also allow questions to be asked using the prepositions “of” and “for” (e.g. “categories of products”, “orders for products”) and possessive (e.g. “John’s orders”). Attribute phrasings are used in questions such as this:
- Which customers have orders?
- Show orders that have chai
- List customers with orders
- What is the category of each product?
- Count Robert King’s orders
The overwhelming majority of attribute phrasings needed in your model will be automatically generated, based on table/column containment and model relationships, so you typically won’t need to create them yourself.
This is an example of how an attribute phrasing looks inside of the linguistic schema:
product_has_category:
Binding: {Table: Products}
Phrasings:
- Attribute: {Subject: product, Object: product.category}
Name Phrasings
Name phrasings are key for tables in your model which represent things that have names. For example, a “product names are names of products” phrasing is essential for being able to use product names in questions. While a name phrasing also enables “named” as a verb (e.g. “List customers named John Smith”), it is most important when used in conjunction with other phrasings, to allow a name value to be used to refer to a particular table row. For example, in “Customers that bought chai”, Q&A can tell that the value “chai” is referring to the whole row of the product table, rather than merely a value in the product name column. Name phrasings are used in questions such as this:
- Which employees are named Robert King
- Who is named Ernst Handel
- What did Robert King buy?
Assuming you used a sensible naming convention for name columns in your model (e.g. “Name” or “ProductName” rather than “PrdNm”), the majority of name phrasings needed in your model will be automatically generated, so you usually won’t need to create them yourself.
This is an example of how a name phrasing looks inside of the linguistic schema:
employee_has_name:
Binding: {Table: Employees}
Phrasings:
- Name:
Subject: employee
Name: employee.name
Adjective Phrasings
Adjective phrasings define new adjectives used describe things in your model. For example, a “happy customers are customers where rating > 6” phrasing is needed to ask questions like “list the happy customers in Des Moines”. There are several forms of adjective phrasings, for use in different situations.
Simple adjective phrasings define a new adjective based on a condition, such as “discontinued products are products where status = D”. Simple adjective phrasings are used in questions such as this:
- Which products are discontinued?
- List the discontinued products
- Products that are backordered
This is an example of how a simple adjective phrasing looks inside of the linguistic schema:
product_is_discontinued:
Binding: {Table: Products}
Conditions:
- Target: product.discontinued
Operator: Equals
Value: true
Phrasings:
- Adjective:
Subject: product
Adjectives: [discontinued]
Measurement adjective phrasings define a new adjective based on a numeric value that indicates the extent to which the adjective applies, such as “lengths indicate how long rivers are”. Measurement adjective phrasings are used in questions such as this:
- List the long rivers
- Which rivers are the longest?
- How long is the Rio Grande?
This is an example of how a measurement adjective phrasing looks inside of the linguistic schema:
river_has_length:
Binding: {Table: Rivers}
Phrasings:
- Adjective:
Subject: river
Adjectives: [long]
Antonyms: [short]
Measurement: river.length
Dynamic adjective phrasings define a set of new adjectives based on values in a column in the model, such as “colors describe products”. Dynamic adjective phrasings are used in questions such as this:
- List the red products
- Which products are green?
- Count issues that are active
This is an example of how a dynamic adjective phrasing looks inside of the linguistic schema:
product_has_color:
Binding: {Table: Products}
Phrasings:
- DynamicAdjective:
Subject: product
Adjective: product.color
Noun Phrasings
Noun phrasings define new nouns that describe subsets of things in your model. For example, a “flops are movies where net profit < 0” phrasing is needed to ask questions like “count the flops by year”. There are two forms of noun phrasings, for use in different situations.
Simple noun phrasings define a new noun based on a condition, such as “contractors are employees where full time = false”. Simple noun phrasings are used in questions such as this:
- Which employees are contractors?
- Count the contractors in Portland
This is an example of how a simple noun phrasing looks inside of the linguistic schema:
employee_is_contractor:
Binding: {Table: Employees}
Conditions:
- Target: employee.full_time
Operator: Equals
Value: false
Phrasings:
- Noun:
Subject: employee
Nouns: [contractor]
Dynamic noun phrasings define a set of new nouns based on values in a column in the model, such as “jobs define subsets of employees”. Dynamic noun phrasings are used in questions such as this:
- List the cashiers in Chicago
- Which employees are baristas?
This is an example of how a dynamic noun phrasing looks inside of the linguistic schema:
employee_has_job:
Binding: {Table: Employees}
Phrasings:
- DynamicNoun:
Subject: employee
Noun: employee.job
Preposition Phrasings
Preposition phrasings are used to describe how things in your model are related via prepositions. For example, a “cities are in countries” phrasing improves understanding of questions like “count the cities in Washington”. Some preposition phrasings are created automatically when a column is recognized as a geographical entity. Preposition phrasings are used in questions such as this:
- Count the customers in New York
- List the books about linguistics
- Which city is John Galt in?
- How many books are by Stephen Pinker?
This is an example of how a preposition phrasing looks inside of the linguistic schema:
customers_are_in_cities:
Binding: {Table: Customers}
Phrasings:
- Preposition:
Subject: customer
Prepositions: [in]
Object: customer.city
Verb Phrasings
Verb phrasings are used to describe how things in your model are related via verbs. For example, a “customers buy products” phrasing improves understanding of questions like “who bought cheese?” and “what did John buy?” Verb phrasings are the most flexible of all of the types of phrasings, often relating more than two things to each other, such as in “employees sell customers products”. Verb phrasings are used in questions such as this:
- Who sold what to whom?
- Which employee sold chai to John?
- How many customers were sold chai by Mary?
- List the products that Mary sold to John.
- Which discontinued products were sold to Chicago customers by Boston employees?
Verb phrasings can also contain prepositional phrases, thereby adding to their flexibility, such as in “athletes win medals at competitions” or “customers are given refunds for products”. Verb phrasings with prepositional phrases are used in questions such as this:
- How many athletes won a gold medal at the Visa Championships?
- Which customers were given a refund for cheese?
- At which competition did Danell Leyva win a bronze medal?
Some verb phrasings are created automatically when a column is recognized as containing both a verb and a preposition.
This is an example of how a verb phrasing looks inside of the linguistic schema:
customers_buy_products_from_salespeople:
Binding: {Table: Orders}
Phrasings:
- Verb:
Subject: customer
Verbs: [buy, purchase]
Object: product
PrepositionalPhrases:
- Prepositions: [from]
Object: salesperson
Relationships with Multiple Phrasings
Frequently, a single relationship can be described in more than one way. In this case, a single relationship can have more than one phrasing. It is quite common for the relationship between a table entity and a column entity to have both an attribute phrasing and another phrasing. For example, in the relationship between customer and customer name, you will want both an attribute phrasing (e.g. “customers have names”) and a name phrasing (e.g. “customer names are the names of customers”) so you can ask both types of questions.
This is an example of how a relationship with two phrasings looks inside of the linguistic schema:
customer_has_name:
Binding: {Table: Customers}
Phrasings:
- Attribute: {Subject: customer, Object: customer.name}
- Name:
Subject: customer
Object: customer.name
Another example would be adding the alternate phrasing “employees sell customers products” to the “customers buy products from employees” relationship. Note that you do not need to add variations like “employees sell products to customers” or “products are sold to customers by employees”, since the “by” and “to” variations of the subject and indirect object are inferred automatically by Q&A.
Linguistic schema specification
Phrasings are one part of the full linguistic schema capabilities, but are by far the most useful and common. You can download the full spec along with a sample here. There’s a lot of flexibility available in the full linguistic schema, so if you have any other questions try asking on the Power BI Community