AmpliGraph: A TensorFlow-Based Library for Knowledge Graph Embeddings
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Re: AmpliGraph: A TensorFlow-Based Library for Knowledge Graph Embeddings
#2Cool. KGE methods are becoming more and more useful as companies are trying to find ways to interface some internal knowledge graph with machine learning techniques. I expect this space to grow substantially!
Re: AmpliGraph: A TensorFlow-Based Library for Knowledge Graph Embeddings
#3Looks very seriously made and documented, congrats ! Was looking at it a bit closely the other days and put it onto my list of future tools. There's been another somewhat related library released by facebook recently, https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-...
Re: AmpliGraph: A TensorFlow-Based Library for Knowledge Graph Embeddings
#4Looks very seriously made and documented, congrats ! Was looking at it a bit closely the other days and put it onto my list of future tools. There's been another somewhat related library released by facebook recently, https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-...
Thanks! Feel free to play around with it - and of course any feedback is much appreciated (GitHub, email or on our Slack channel https://join.slack.com/t/ampligraph/shared_invite/enQtNTc2NT.... I was not aware of pytorch-biggraph. Looks cool. It's good to see there's a lot going on in graph representation learning!
Re: AmpliGraph: A TensorFlow-Based Library for Knowledge Graph Embeddings
#5Cool. KGE methods are becoming more and more useful as companies are trying to find ways to interface some internal knowledge graph with machine learning techniques. I expect this space to grow substantially!
Indeed. Here in Accenture Labs we use it in quite a lot of diverse applicative scenarios. Besides, KG embeddings can be used for other tasks beyond link prediction (e.g. link-based clustering).
Re: AmpliGraph: A TensorFlow-Based Library for Knowledge Graph Embeddings
#6Graph embeddings are one of my favorite underused things in ML.
I've used them to do things like characterise users based on follow/follower patterns, but there are many more applications.
In the past I've had great success with Facebook Research's StarSpace
Re: AmpliGraph: A TensorFlow-Based Library for Knowledge Graph Embeddings
#7Graph embeddings are one of my favorite underused things in ML. I've used them to do things like characterise users based on follow/follower patterns, but there are many more applications. In the past I've had great success with Facebook Research's StarSpace
Can you share more about that project? Sounds cool.
Re: AmpliGraph: A TensorFlow-Based Library for Knowledge Graph Embeddings
#8btw, we are hiring research engineers here in our Dublin Lab. Send me an email if interested: luca.costabello@accenture.com
https://www.accenture.com/ie-en/careers/jobdetails?src=&id=0...
Re: AmpliGraph: A TensorFlow-Based Library for Knowledge Graph Embeddings
#9Can you help me understand, what are possible inputs to ampligraph?
I think the main use-case is plugging in an existing knowledge graph, and it filling in the gaps, correct?
Can I augment this will really high-quality embeddings for the nodes, that were learned over auxiliary unlabelled text?
What are other ways I can augment the data set?
Is this useful only when there are many edge-types, or is it also good when there are very few?
It looks promising, I just couldn't immediately grok when I use should look to this library.
Re: AmpliGraph: A TensorFlow-Based Library for Knowledge Graph Embeddings
#10Can you help me understand, what are possible inputs to ampligraph? I think the main use-case is plugging in an existing knowledge graph, and it filling in the gaps, correct? Can I augment this will really high-quality embeddings for the nodes, that were learned over auxiliary unlabelled text? What are other ways I can augment the data set? Is this useful only when there are many edge-types, or is it also good when t…
I like the README.md for StarSpace[1] because it has lots of examples which get you thinking.
I used graph embeddings as input to a classifier to classify people when follower/followee information was easy to gather but text wasn't.
Basically anything that can be represented as a graph can be used. There is some interesting work being done using code syntax trees as input which uses a very similar approach. See code2vec[2]
I'm not aware of any way to transfer text embeddings into graph emneddings, but you can could concatenate them and use them together (I've done this before) or maybe do some dimension reduction or do a multi-task learning thing and try to learn some combined representation.
I'm not ware of the scalability limits for this particular library, but Facebook Research's pytorch-biggraph[3] (released 2 days ago) scales to trillions of edges and billions of nodes.
[1] https://github.com/facebookresearch/StarSpace
[2] https://arxiv.org/abs/1803.09473
[3] https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-...