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Status Future consideration
Created by Guest
Created on Oct 10, 2017

Develop a collection of REST APIs and SDKs that allow to automate IBM Watson Knowledge Studio functions

Develop functions that allow to automate IBM Watson Knowledge Studio activities in Python like:

1- Upload entities and relationships

2- Upload dicts associating it to entities

3- Upload files to be annotate

4- Run annotators, specially dictionnary pre-annotator

5- Get sentences to be annotated

6- Set annotations: mention, relationship and coreference

7- Evaluate ground truth 

  • Guest
    May 24, 2018


    In working with a number of internal product teams within IBM that have used WKS or in the process of using it this requirement keeps coming up.  Various teams have proposed ideas around their needs. As we look to merge our tooling into Watson Studio there are capabilities within it that should work nicely, especially around the continuous learning techniques.   In addition to the the above, we need the following endpoints 

    a) Ability to evaluate a WKS model given a test set or blind set and produce metrics + confusion matrix

    b) Ability to version and deploy a WKS model

    c) Ability to collect and store feedback 

    d) Ability to provide continuous learning (Enable continuous learning, Provide thresholds for automatic re-training, accuracy thresholds for publishing a model  - Note : All this exist in Studio today for WML)

  • Guest
    Mar 21, 2018

    @Andrew Freed

    Making Watson smarter with use (harnessing end user feedback) is a very nice idea. We already have a placeholder in the roadmap, but haven't had time to discuss it seriously. As the NLU is currently working on a NLU Train API, I'm sure that soon we'll have technology to support the continuous learning idea.

    It seems that there are several different scenarios that require WKS API. I think that your idea leans more towards the NLU Train API, because I'm not sure that you need WKS for the use case that you describe.

  • Guest
    Mar 21, 2018

    Could you describe the scenario (workflow) that you envision for the API that you describe?

    Where would the artifacts (type system, docs) come from?

    How does step 6 fit into the workflow, and why do you need an API for it? (This is the human annotation activity.)

    What is the user benefit (business value) for your case? Are you saving time, scaling activities, replacing WKS GUI, or something else?

    The more information you provide, better I'll understand your case and better the chances to promote this idea to the WKS roadmap. Thanks.

  • Guest
    Dec 14, 2017

    Marc, we are planning to separate the publishing of trained models (done by WKS human annotators or project managers) from deploying the models to NLU/WDS (done by developers). NLU is working on a MMA (model management API). When this is ready, the API that you need would belong to NLU, not to WKS as WKS will be responsible for developing and publishing the models, not for managing the model deployment across services.

    If you feel that the other WKS API cases are needs, please elaborate on the business/client benefits of having such APIs. I don't see such benefits apart from someone trying to replicate WKS in a different application.

  • Guest
    Oct 12, 2017

    My specific request is to provide an API to automate the deployment of a MLM to WDS. This is for a key partner who is looking to scale & automate Discovery collections using a pre-defined WKS model.