Issue for using pykg2vec

[PAVITHRA-CP]

Hi,

I have a graph based on the wordnet knowledge base. I want to convert each relation edge into a vector with ConvE and TransE model using pykg2vec. Can anyone help me with the same?

Thanks!

[PAVITHRA-CP]

How to use the wordnet18RR dataset. When I give "WordNet18RR" instead of "Freebase15k" it is showing "NotImplementedError: WN18RR dataset config not found!"

[baxtree]

As shown on this line https://github.com/Sujit-O/pykg2vec/blob/e285cd3a655eea7c4ee804dee8004cfaa677269c/pykg2vec/utils/kgcontroller.py#L552 , there is an underscore expected in the dataset name. So try WordNet18_RR or wn18_rr?

[PAVITHRA-CP]

[baxtree]

Hi, @PAVITHRA-CP, I guess you have been following experiment.py. I don't think it is up to date coz I can no longer see DataPrep, SMEBilinear or SMELinear defined anywhere. You may want to follow https://github.com/Sujit-O/pykg2vec/blob/master/examples/train.py to conduct the training on WordNet18_RR.

[PAVITHRA-CP]

I went through train.py, but while running that what all the arguments I have to pass. When I run the code without giving any arguments it automatically downloading FB15K.

Thank you for your great help.

[baxtree]

Looking closer to README.md, you will find you need to pass in the right flags: https://github.com/Sujit-O/pykg2vec#using-well-known-dataset

The following command worked for me: python pykg2vec/examples/train.py -mn TransE -ds WordNet18_RR

[PAVITHRA-CP]

What is "config = config_def(args=args)". I'm getting error that "'NoneType' object is not callable".

[PAVITHRA-CP]

Hi SIr,

I'm Confused with this code on after creating the model if we want to check the model what will be the input format and what will be the label?

[PAVITHRA-CP]

Hi Sir,

That code worked for me and the training was done. Thanks for your help.

Can you please help me to sort out: 1) How to test the model 2) What should be the format of input to be checked 3) What will be the output

[baxtree]

Glad it works for you now.

The most useful feature so far to me is the inference and I used it to do Knowledge Graph completion. In this case, the input are the trained model itself plus two triple parts (e.g., head and tail) and the output are top-k predicted triples containing the missing 3rd triple part (e.g, relation).

Take a look at those lines after https://github.com/Sujit-O/pykg2vec/blob/e285cd3a655eea7c4ee804dee8004cfaa677269c/pykg2vec/test/test_inference.py#L42 and run the test if you want to see the top-k predication.

[PAVITHRA-CP]

Hi Sir,

Thank you for your answers.

I'm using this for the implementation of 'Word sense disambiguation'. So I need to build a model with wordnet using kg2vec. For which I'm using this.

So from this dataset I will get top-k IDS as output? But testing and validating data is also in same format. There is no difference in training , testing, and validating data.

So what is the basics learning of this model while training the code.

[PAVITHRA-CP]

Hi Sir,

When I run the test_inference.py I got the result like this:

But I'm not getting what to infer from this result. If I want to check any other one triplet how to check that?

Please help me sir.

Thank @you.baxtree

[louisccc]

move the discussion to #136