Re: [Nmlrg] Generic knowledge definition and format?

[Sheng Jiang <[e-mail]>]

Hi, Albert,

Thank for your reply. Yes, we do know you have another two use cases. In order to be more concentrate, I planned to discuss them one by one. I will raise some questions for them later in different threads.

>Unfortunately we don´t have yet an experiment for knowledge
>extraction, mainly due to the lack of real-world data. Knowledge
>extraction should (in my view) use statistical techniques such as
>K-means, K-nearest neighbors and Correlation Analysis. Such techniques
>operate over the data-set and hence, the result have the same
>semantics than the data-set.

I do understand and agree your above statement. Allow me to ask my questions in another way, if you can obtain data as you want, what data items will you choose to perform you machine learning process? What analysis tasks will you assign to the machine learning process? What feather or patent may you use? What kind of knowledge are you expecting from it?

I know you may not really have the answer. But let's discuss the potential. Then maybe later, we could be able to talk about where we may obtain the data to do some experimental.

Regards,

Sheng

>Albert
>
>On Tue, Nov 17, 2015 at 9:04 AM, Sheng Jiang <[e-mail]>
>wrote:
>> Hi, Albert,
>>
>> Thanks for your interesting presentation in the proposed NMLRG meeting in
>Yokohama. It is a pity we do not have enough time discuss. However, mail list
>discussion may give us more flexibility to reach deep details. Since you have
>multiple use cases, let's discuss one by one for better communication.
>>
>> In your use case 4: knowledge extraction, have you already done some
>prototype? Would you share some concrete example of knowledge? Actually,
>I am thinking whether it is feasible to give a generic definition of knowledge
>and whether it is possible to design a generic format/language to describe
>knowledge. If so, these knowledge may be much easier to be understood and
>trigger follow up actions.
>>
>> Regards,
>>
>> Sheng