Knowledge Graph Embedding

Jul 24, 20220 likes318 views

This document summarizes a presentation on KBGAN, an approach that uses adversarial learning to generate high-quality negative examples for training knowledge graph embedding models. KBGAN trains a generator and discriminator in an adversarial manner, with the generator providing negative examples to improve the discriminator's ability to distinguish positive and negative triples. Experimental results on standard knowledge graph datasets show KBGAN improves over methods that randomly sample negative examples, achieving better performance on knowledge graph completion tasks based on hits@10 and MRR evaluation metrics.

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Knowledge Graph
Embeddings:
Recent Advances
William Wang
Joint work with Liwei Cai
CIPS Summer School 2018

Outline
• Related Work
• KBGAN: Algorithm
• Experiments
• Conclusion

Related Work
• Embedding-based method
• RESCAL, Nickel et al, 2011
• TransE, Bordes et al, 2013
• Neural Tensor Network, Socher et al, 2013
• TransR/CTransR, Lin et al, 2015
• Complex Embeddings, Trouillon et al, 2016
3
Embedding methods allow us to compare, and
find similar entities in the vector space.

RESCAL (Nickel et al., 2011)
• Tensor factorization on the
• (head)entity-(tail)entity-relation tensor.
4

TransE (Bordes et al., 2013)
5
• Assumption: in the vector space, when adding
the relation to the head entity, we should get
close to the target tail entity.
• Margin based loss function:
• Minimize the distance between (h+l) and t.
• Maximize the distance between (h+l) to a randomly
sampled tail t’ (negative example).

Neural Tensor Networks
(Socher et al., 2013)
6
• Model the bilinear interaction between entity
pairs with tensors.

Poincaré Embeddings
(Nickel and Kiela, 2017)
• Idea: learn hierarchical KB representations by
looking at hyperbolic space.
7

ConvE (Dettmers et al, 2018)
8
• 1. Reshape the head and relation embeddings into
“images”.
• 2. Use CNNs to learn convolutional feature maps.

It all started in 2013
Me: How did you get negative
examples from knowledge graphs?
William Cohen: We did some
samplings from the knowledge
graph.
Me: OK... ( )

Reality about Knowledge Bases
•Only positive facts are stored, and
no negative examples are stored.
• This makes sense, for efficiency
considerations.
•But for machine learning (e.g.,
margin-based models)
• We often need negative examples.

Negative Sampling is Pervasive
• TransE (Bordes et al., 2013): Replace head/tail
entity with a randomly sampled entity from KB
to create a negative example.
• Margin-based loss function:
• Positive Examples: Minimize the distance between
(h+l) and t.
• Negative Examples: Maximize the distance between
(h+l) to a randomly sampled tail t’ (negative
example).

Negative Sampling’s Main
Issue
•Main Issue for KB Embedding:
• It often generates low-quality negative
examples that do not help you learn
good embedding models.
LocatedIn(NewOrleans,
Louisiana)
LocatedIn(NewOrleans,
Google)

KBGAN: Learning to Generate
High-Quality Negative
Examples
Idea: use adversarial learning to iteratively learn
better negative examples.

KBGAN: Overview
• Both G and D are KG embedding models.
• Input:
• Pre-trained generator G with score function 𝑓𝐺 ℎ, 𝑟, 𝑡 .
• Pre-trained discriminator D with score function 𝑓𝐷 ℎ, 𝑟, 𝑡 .
• Adversarial Learning:
• Use softmax to score and rank negative triples.
• Update D with original positive examples and highly-ranked
negative examples.
• Pass the reward for policy gradient update for G.
• Output:
• Adversarially trained KG embedding discriminator D.

KBGAN: Adversarial Negative
Training
For each positive triple from the minibatch:
1. Generator: Rank negative examples.
2. Discriminator: Standard margin-based
update.

KBGAN: Adversarial Training
(cont’d)
3. Compute the Reward for the Generator.
r = −𝑓𝐷 ℎ′, 𝑟, 𝑡′ .
4. Policy gradient update for the Generator.
The baseline b is total reward sum / mini-batch size.

Experimental Settings
• Datasets: three standard KB completion datasets.
• Hyperparameters: documented in details in the
paper.
• Metrics: Hits@10 and Mean Reciprocal Rank
(MRR).

Conclusion
• We propose an adversarial learning approach
for generating high-quality negative examples.
• Our approach is model-agnostic, and it can be
applied to various knowledge graph
embedding models.
• Our work has shown improvements with
various settings on two datasets.

Thank you!
• Code: https://github.com/cai-lw/KBGAN

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Knowledge Graph Embedding

1. Knowledge Graph Embeddings: Recent Advances William Wang Joint work with Liwei Cai CIPS Summer School 2018

2. Outline • Related Work • KBGAN: Algorithm • Experiments • Conclusion

3. Related Work • Embedding-based method • RESCAL, Nickel et al, 2011 • TransE, Bordes et al, 2013 • Neural Tensor Network, Socher et al, 2013 • TransR/CTransR, Lin et al, 2015 • Complex Embeddings, Trouillon et al, 2016 3 Embedding methods allow us to compare, and find similar entities in the vector space.

4. RESCAL (Nickel et al., 2011) • Tensor factorization on the • (head)entity-(tail)entity-relation tensor. 4

5. TransE (Bordes et al., 2013) 5 • Assumption: in the vector space, when adding the relation to the head entity, we should get close to the target tail entity. • Margin based loss function: • Minimize the distance between (h+l) and t. • Maximize the distance between (h+l) to a randomly sampled tail t’ (negative example).

6. Neural Tensor Networks (Socher et al., 2013) 6 • Model the bilinear interaction between entity pairs with tensors.

7. Poincaré Embeddings (Nickel and Kiela, 2017) • Idea: learn hierarchical KB representations by looking at hyperbolic space. 7

8. ConvE (Dettmers et al, 2018) 8 • 1. Reshape the head and relation embeddings into “images”. • 2. Use CNNs to learn convolutional feature maps.

9. It all started in 2013 Me: How did you get negative examples from knowledge graphs? William Cohen: We did some samplings from the knowledge graph. Me: OK... ( )

10. Reality about Knowledge Bases •Only positive facts are stored, and no negative examples are stored. • This makes sense, for efficiency considerations. •But for machine learning (e.g., margin-based models) • We often need negative examples.

11. Negative Sampling is Pervasive • TransE (Bordes et al., 2013): Replace head/tail entity with a randomly sampled entity from KB to create a negative example. • Margin-based loss function: • Positive Examples: Minimize the distance between (h+l) and t. • Negative Examples: Maximize the distance between (h+l) to a randomly sampled tail t’ (negative example).

12. Negative Sampling’s Main Issue •Main Issue for KB Embedding: • It often generates low-quality negative examples that do not help you learn good embedding models. LocatedIn(NewOrleans, Louisiana) LocatedIn(NewOrleans, Google)

13. KBGAN: Learning to Generate High-Quality Negative Examples Idea: use adversarial learning to iteratively learn better negative examples.

14. KBGAN: Overview • Both G and D are KG embedding models. • Input: • Pre-trained generator G with score function 𝑓𝐺 ℎ, 𝑟, 𝑡 . • Pre-trained discriminator D with score function 𝑓𝐷 ℎ, 𝑟, 𝑡 . • Adversarial Learning: • Use softmax to score and rank negative triples. • Update D with original positive examples and highly-ranked negative examples. • Pass the reward for policy gradient update for G. • Output: • Adversarially trained KG embedding discriminator D.

15. KBGAN: Adversarial Negative Training For each positive triple from the minibatch: 1. Generator: Rank negative examples. 2. Discriminator: Standard margin-based update.

16. KBGAN: Adversarial Training (cont’d) 3. Compute the Reward for the Generator. r = −𝑓𝐷 ℎ′, 𝑟, 𝑡′ . 4. Policy gradient update for the Generator. The baseline b is total reward sum / mini-batch size.

17. Experimental Settings • Datasets: three standard KB completion datasets. • Hyperparameters: documented in details in the paper. • Metrics: Hits@10 and Mean Reciprocal Rank (MRR).

18. Experimental Results

19. Convergence Analysis

20. Conclusion • We propose an adversarial learning approach for generating high-quality negative examples. • Our approach is model-agnostic, and it can be applied to various knowledge graph embedding models. • Our work has shown improvements with various settings on two datasets.

21. Thank you! • Code: https://github.com/cai-lw/KBGAN

Knowledge Graph Embedding

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