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Status Future consideration
Created by Guest
Created on Jan 25, 2018
Merged idea

This idea has been merged into another idea. To comment or vote on this idea, please visit WDS-I-20 Applying NLU enrichments on the question.

Leverage enrichments during NLQ or keyword search Merged

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. 

  • Guest
    Mar 9, 2018

    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.

  • Guest
    Feb 20, 2018

    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.

  • Guest
    Feb 7, 2018

    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.

  • Guest
    Feb 7, 2018

    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.