malllabiisc / hyte Goto Github PK
View Code? Open in Web Editor NEWEMNLP 2018: HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding
License: Apache License 2.0
EMNLP 2018: HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding
License: Apache License 2.0
Hi,
I see that you are not filtering entities in result_eval.py. Shouldn't you filter out entities already seen?
I have been researched static knowledge graph completion. Now I am going to research TKGC, and I've never seen stat.txt,I'd be grateful if someone could give me a explanation.
can you upload the dataset file, thanks!
While testing, it looks like the model only looks at the start time of an interval. Is that correct?
I run the training shell. And my loss down to 0.0. But when it will be stoped? Or Maybe Stop It by myself ???
Hi
I'm curious about this code snippet
for i in range (len(train_triples)-1,-1,-1): # range(start, stop, step)
if i not in keep_idx:
del train_triples[i]
# if start / end time is like -405 or like 100, delete
# But why?
Why do you delete those train facts?
I couldn't find any code to calculate hit@10 metric for the tail and head prediction in the result_eval file.
So, I wrote a piece of code after the line number 68. Let me know if it is correct or not?
tail_array = np.array(ranks_tail)
head_array = np.array(ranks_head)
hit_at_10_tail = tail_array[np.where( tail_array < 10 ) ]
hit_at_10_head = head_array[np.where( head_array < 10 ) ]
print('Epoch {} : test_tail HIT@10 {}\t test_head HIT@!) {}'.format(k ,len(hit_at_10_tail)/float(len(tail_array))*100, len(hit_at_10_head)/float(len(head_array))*100))
hi I want to run this code on my system but the data is too large, how can I execute code on local system?
Thanking for sharing your code!
I'm having a hard time figuring out how you implement the "time-dependent negative sampling(TDNS)" in your paper, can you help me on this? I think the negative sampling process is implemented in line 243-270 of "time_proj.py", isn't this the normal sampling(or "TANS" in your paper)? Or did I miss something?
Thanks for your outstanding work, and I want to know if you have the plan to release pytorch version code?
in time_project.py line 40:
num_batches = len(data) // self.p.batch_size
error: unresolved attribute reference 'p' for class
Hi thanks for the code.
But I found some problematic part in your code
in
time_proj.py
in def load_data()
there is this code snippet
with open(self.p.dataset,'r') as filein:
for line in filein:
train_triples.append([int(x.strip()) for x in line.split()[0:3]])
triple_time[count] = [x.split('-')[0] for x in line.split()[3:5]]
count+=1
self.p.dataset means the trainset of data.
and the line
triple_time[count] = [x.split('-')[0] for x in line.split()[3:5]]
splits time interval part with '-'
but inside YAGO's dataset, there is fact like this
And this fact's start time will be parsed like this
It seems problematic to me...
Isn't it???
python time_proj.py -data_type yago -margin 10 -model MODEL_NAME -test_freq 25
hello,I cannot find the parameter MODEL_NAME.
where can find file processed_data.pkl ?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.