This AI Task is only available if you added as a data source an RDF Graph!
To access this AI task, click AI Tasks and then AI Assistant Linker:
Create a Linker
Click Linkers to create a new Linker task.
Provide:
name it
add labels that will help to train it.
Labels are words or expressions expected in the data source that help the linker identify entities.
For example, a linker for the cocktail "Margarita" uses labels such as "Tequila", "Lime juice", and "Triple sec".
Warning
This AI Task don't work with multilingual AI assistant. Make sure to choose the assistant's language when you create it.
Go to the Create AI Assistant section for more information on how to create an AI assistant.
Aggregate & Evaluate
After creating a linker, choose to:
Aggregate data to it
Evaluate the data linked to it
On the Aggregate tab, choose which data instances to link. Click a row to cycle through:
Clicking a row alternates between:
true = exclude (means the data that matches the label you choose will be considered non relevant for the linker and these label will automatically be excluded of the train feature)
false = excluded (means the data will be considered relevant for the linker)
neutral = nothing (means the data will be considered neither relevant nor non relevant for the linker you will have to manually choose if you want to include it or not when training the linker)
On the Evaluate tab you can see the labels you have assigned to the linker "Margarita" just created.
You can click on each label to exclude or include them in the evaluation of the linker.
If some labels added on creation are detected as not related to the data sources (rdf file) there will be a 0 in the match count column. For example here the label "Rhum" has no match in the cocktails dataset for "Margarita" linker.
You necessarily have to exclude at least one label to be able to train the linker.
Click Train & Save to view evaluation results.
Now you can see the training results of the linker you created. The score represents the quality of the linker based on the labels you provided.
Here you can see that the linker has a 0.09 score meaning that it is not very good. It is because of the fact that it was excluded from the aggregate step on the line "Non-alcoholic Beverages".
Objective
Training a linker enables an API to reconcile labels with linked entities.
