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An update, based on my testing on my customer's project I've been able to show some success at making use of the WKS types and subtypes in my collection.
What I did was use Query Expansion to add my WKS entity subtypes to the natural language query. For example:
{
"expansions": [{
"input_terms": [
"ball", "rocking horse", "yoyo"
],
"expanded_terms": [
"TOY"
]
}, {
"input_terms": [
"apple", "banana", "finger lime"
],
"expanded_terms": [
"FRUIT"
]
}
]
}
Where TOY and FRUIT were entity subtypes of PRODUCT in my WKS model.
When I added these query expansions to my collection that had my WKS model applied, I found that searching on a natural language query like this:
"How can I purchase a rocking horse"
Would also find documents talking about purchasing that had the TOY entity subtype listed against them.
In my testing I could see definite improvements using this method, with 30% more relevant documents found in the top 5 returned results against my test question set. I expect that we'd see the same or better improvements if this Aha! idea was implemented.
Fully agree, without Discovery leveraging a WKS custom model in a NLQ it is hard to justify the labor intensive work on a WKS custom model.
My interest in this feature is prompted by my customer (UBank) who are struggling to see the benefits of a WKS model that understands their products and work instructions, when it's not really used by the Natural Language Query feature to find the most relevant document.
I think we'd get some good benefit out of applying the WKS model to the query and having the annotations found influence the results. Currently the WKS model is producing metadata that can be filtered on, but even if I use NLU to apply the model to the query, I don't want to exclude results that don't include the entities and relationships detected in the natural language question, I just want to effect the sorting/ranking/relevance.