# (1) Import dependencies
import dask.dataframe as dd
import pandas as pd
import torch

# (2) Load pre-trained KGE
kge = torch.load('model.pt')

# (3) Extract/Convert Quaternion-valued relation embeddings From Torch To Numpy 
relation_embeddings=torch.cat((kge['emb_rel_real.weight'].data, kge['emb_rel_i.weight'].data,
kge['emb_rel_j.weight'].data, kge['emb_rel_k.weight'].data),1).detach().numpy()

# (4) Load relation indexes.
relation_to_idx = dd.read_parquet('relation_to_idx.gzip').compute()

# (5) Convert (3) from Numpy narray to Pandas DataFrame
df = dd.from_array(relation_embeddings).compute()

# (6) Set (4) into (5)
df=df.set_index(relation_to_idx.index)

# (7) Enjoy :)
df.head()                                                       0         1   ...        98        99
http://www.w3.org/2000/01/rdf-schema#subClassOf  2.485571 -2.207192  ... -0.642734  1.148525
http://www.w3.org/2002/07/owl#equivalentClass   -0.425896  0.868642  ... -2.312936  1.743008
http://www.w3.org/1999/02/22-rdf-syntax-ns#type  0.638292  0.396915  ...  1.138350 -1.355508
http://creativecommons.org/ns#license            0.059268  0.050355  ... -0.002978  0.176659
http://purl.org/dc/terms/license                -0.283764 -0.026041  ... -0.095821 -0.177008

[5 rows x 100 columns]