Positive Labels for Negative Example in Test Data (Link Prediction Task) about kg-bert

[yao8839836]

@batselem

Hi, thank you for reading the code, link_prediction gives the rank of every enity in correct (positive) test triples, so the labels are "1".

[spacewalk01]

Thank you very much for a quick response!
Yes but tail_corrupt_list contains corrupted triples. How did you label them? As I skim through it, it looks like all the triples in tail_corrupt_list get "1" label.

Thank you.

[spacewalk01]

Ah, even though they all get label "1", you only get the rank of the first triple which is the original correct triple: rank1 = np.where(argsort1 == 0)[0][0]
Am I correct? Thanks.

[yao8839836]

@batselem

Yes, all the triples in tail_corrupt_list get "1" label. This is inappropriate when writing the code，but only the "1" of the target postive tiple is used to obtain values (probability of being correct) of the target postive tiple and corrputed tiples.

        # get the dimension corresponding to current label 1 of all corrupted triples and the positive test triple
        rel_values = preds[:, all_label_ids[0]]

Here all_label_ids[0] is the label of the target positive triple.
The "1" of corrupted triples are not used in any computation, and the ranks should be correct.

[spacewalk01]

I understand it, Thank you very much. Excellent work!

[yao8839836]

@batselem

Thank you!