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Leverage enrichments during NLQ or keyword search
Customer : IBM Teacher Advisor with Watson
Use-Case : As a teacher, I want to be able to search using NL be able to get the right lessons and activities for my K-5 students.
Description : TA with Watson team has a custom WKS model that is trained on the education content for K-5 focused on Math curriculum. They want to be able to use Watson Discovery Service to provide relevant educational content for utterances like "I need to know what tactile activities are available for fractions". With an enriched Discovery collection, there is no way to get a matching passage like this "XX Activities are good for kids that can use physical activity YYYY...." because there is no semantic search being applied during search. The alternative is to use "Filters" in the query which require the application or the user to provide that ahead of time. Alternatively, they can run NLU on the same custom model to get the entities and run a filtered search. Either way, it is more work on part of the query building exercise.
Need : Just like the DeepQA factoid pipeline Primary Search components where the constituents of a query (LAT, Focus, Entities, Relations) are automatically considered while performing search. This yielded far better results in the first place.
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