Download A Review of Relational Machine Learning for Knowledge Graphs

A Review of Relational Machine
Learning for Knowledge Graphs
CVML Reading Group
Xiao Lin
M. Nickel, K. Murphy, V. Tresp, E. Gabrilovich. “A Review of
Relational Machine Learning for Knowledge Graphs. ” arXiv 2015.
2
• VQA
• Knowledge base
• MRF vs embeddings
3
• The relational learning problem
• Neural embedding models
4
“Leonard Nimoy was an actor who played the
character Spock in the science-fiction movie Star Trek”
5
“Leonard Nimoy was an actor who played the
character Spock in the science-fiction movie Star Trek”
subject
Leonard Nimoy
Leonard Nimoy
Leonard Nimoy
Spock
Star Trek
predicate
profession
starred in
played
character in
genre
object
Actor
Star Trek
Spock
Star Trek
Science-fiction
“SPO
triples”
6
Knowledge Graphs
“Leonard Nimoy was an actor who played the
character Spock in the science-fiction movie Star Trek”
Leonard Nimoy
Leonard Nimoy
Leonard Nimoy
Spock
Star Trek
profession
starred in
played
character in
genre
Actor
Star Trek
Spock
Star Trek
Science-fiction
7
Knowledge Graphs
“Leonard Nimoy was an actor who played the
character Spock in the science-fiction movie Star Trek”
Actor
Spock
Science-fiction
profession
played
character in
starred in
Leonard Nimoy
genre
Star Trek
8
Knowledge Graphs
a.k.a. Relational data
a.k.a. Linked data
Actor
Spock
Science-fiction
profession
played
character in
starred in
genre
Leonard Nimoy Star Trek
9
Knowledge Graphs
a.k.a.
a.k.a.
a.k.a.
a.k.a.
a.k.a.
a.k.a.
Semantic network (1960s)
Knowledge base (1970s)
(2000s)
Semantic web
(2010s)
Big data
Knowledge graph (2012)
Artificial Intelligence
Actor
Spock
Science-fiction
profession
played
character in
starred in
genre
Leonard Nimoy Star Trek
10
Query Knowledge Graphs
Was Leonard Nimoy in Star Trek?
Actor
Spock
Science-fiction
Leonard Nimoy starred in Star Trek
profession
played
character in
starred in
genre
Leonard Nimoy Star Trek
11
Query Knowledge Graphs
Was Leonard Nimoy in Star Trek?
Actor
Spock
Science-fiction
Leonard Nimoy starred in Star Trek
True
profession
played
character in
starred in
genre
Leonard Nimoy Star Trek
12
Query Knowledge Graphs
Which character did Leonard
Nimoy play in Star Trek?
Leonard Nimoy played (_X_)
∧
(_X_) character in Star Trek
Actor
Spock
Science-fiction
profession
played
character in
starred in
genre
Leonard Nimoy Star Trek
13
Query Knowledge Graphs
Which character did Leonard
Nimoy play in Star Trek?
Leonard Nimoy played (Spock)
∧
True
(Spock) character in Star Trek
Actor
Spock
Science-fiction
profession
played
character in
starred in
genre
Leonard Nimoy Star Trek
True
14
Query Knowledge Graphs
Which character did Leonard
Nimoy play in Star Trek?
Leonard Nimoy played (Spock)
∧
True
(Spock) character in Star Trek
Actor
Spock
Science-fiction
profession
played
character in
starred in
genre
Leonard Nimoy Star Trek
True
15
Query Knowledge Graphs
Was Leonard Nimoy in Star Trek?
Actor
Spock
Science-fiction
Leonard Nimoy starred in Star Trek
profession
played
character in
genre
Leonard Nimoy Star Trek
16
Relational Machine Learning
• Goal: Infer relationships between objects and answer queries
Database of (partially)
annotated relations
17
Relational Machine Learning
• Goal: Infer relationships between objects and answer queries
Database of (partially)
annotated relations
Learn rules or
models
*
ℱ (
*
,
) = 0/1
18
Relational Machine Learning
• Goal: Infer relationships between objects and answer queries
Database of (partially)
annotated relations
Learn rules or
models
*
ℱ (
Answer queries on
unseen relations
*
,
) = 0/1
19
Relational Machine Learning
• Goal: Infer relationships between objects and answer queries
Database of (partially)
annotated relations
WordNet
Wikipedia
Freebase
YAGO
NELL
ReVerb
Knowledge Vault
Web
….
Learn rules or
models
ILP
MLN
Random walk
Word2Vec
Tensor factorization
Neural embeddings
…
Answer queries on
unseen relations
(S,P,O) exist=1 or not=0
(?,P,O)
(S,P,?)
(S,P1,?) ∧ (?,P2,O)
20
Relational Machine Learning
• Applications
• Bioinformatics
Given that:
Some DNA produces some protein
Some protein has some function
Some protein interacts some other protein
What does this protein do?
21
Relational Machine Learning
• Applications
• Bioinformatics
• Question Answering (QA)
“Find a table for 4
tonight in Chicago.”
“Read my latest
emails”
“How’s the weather
tomorrow?”
“Why did the chickens
cross the road?”
22
Relational Machine Learning
• Applications
• Bioinformatics
• Question Answering (QA)
• Search
23
Relational Machine Learning
• Applications
• Bioinformatics
• Question Answering (QA)
• Search
24
Relational Machine Learning
• Applications
•
•
•
•
Bioinformatics
Question Answering (QA)
Search
Visualization
Reviews for La Baguette Bakery, Stanford, CA
[Wang et al. 2012]
25
Relational Machine Learning
• Applications
•
•
•
•
•
•
•
Bioinformatics
Question Answering (QA)
Search
Visualization
Social networks
Recommendation
Controversial stuff
http://arstechnica.co.uk/security/2016/02/the-nsas-skynetprogram-may-be-killing-thousands-of-innocent-people/
26
Relational Machine Learning
What I really do
27
Relational Machine Learning
• Goal: Infer relationships between objects and answer queries
Database of (partially)
annotated relations
Learn rules or
models
*
ℱ (
Answer queries on
unseen relations
*
,
) = 0/1
28
Relational Machine Learning
• Goal: Infer relationships between objects and answer queries
Database of (partially)
annotated relations
WordNet
Wikipedia
Freebase
YAGO
NELL
ReVerb
Knowledge Vault
Web
….
Learn rules or
models
ILP
MLN
Random walk
Word2Vec
Tensor factorization
Neural embeddings
…
Answer queries on
unseen relations
(S,P,O) exist=1 or not=0
(S,?,O)
(S,P,?)
(S,P1,?) ∧ (?,P2,O)
29
Relational Machine Learning
• Challenges
Database of (partially)
annotated relations
• Quantity
• Collaborative
• Automatic IE
• Negative examples
• Locally closed world
• Noise
• Grounding
• Barrack Obama
• /m/03gh4
• Bias
30
Relational Machine Learning
• Challenges
Database of (partially)
annotated relations
• Quantity
• Collaborative
• Automatic IE
• Negative examples
• Locally closed world
• Noise
• Grounding
Learn rules or
models
•
•
•
•
Accuracy
Efficiency
Scalability
Interpretability
• Barrack Obama
• /m/03gh4
• Bias
31
Relational Machine Learning
• Challenges
Database of (partially)
annotated relations
• Quantity
• Collaborative
• Automatic IE
• Negative examples
• Locally closed world
• Noise
• Grounding
Learn rules or
models
•
•
•
•
Accuracy
Efficiency
Scalability
Interpretability
Answer queries on
unseen relations
• Fast inference
• Scale to complex queries
• Conjunctions 
• Barrack Obama
• /m/03gh4
• Bias
32
Relational Machine Learning Models
• Symbolic vs. Connectionist
33
Relational Machine Learning Models
• Symbolic vs. Connectionist
• MRF vs. Embedding
• MRF (Markov Logic Nets)
 Unobserved relations are jointly
inferred
 Learn a set of rules (factors) and
weights (MCMC)
 Infer all unobserved relations
jointly by maximizing probability
-------------------This is not an MRF---------------------Actor
Spock
Science-fiction
profession
played
character in
genre
Leonard Nimoy Star Trek
-------------------This is not an MRF---------------------𝒓: ∀𝒙∀𝒚∀𝒛 𝑷𝒍𝒂𝒚𝒆𝒅_𝒊𝒏(𝒙, 𝒚) ∧ 𝑪𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓_𝒊𝒏(𝒚, 𝒛)
→ 𝑺𝒕𝒂𝒓𝒓𝒆𝒅_𝒊𝒏(𝒙, 𝒚)
Satisfied 𝑥𝑟 times
Weight=𝜃𝑟
𝑆𝑤𝑜𝑟𝑙𝑑 =
𝜃𝑟 𝑥𝑟
𝑟∈𝑅𝑢𝑙𝑒𝑠
34
Relational Machine Learning Models
• Symbolic vs. Connectionist
• MRF vs. Embedding
• MRF
• Embedding (RESCAL)
 Unobserved relations are
independent given SPO
embeddings
 Learn SPO embeddings
(matrix/tensor factorization,
neural nets)
 Predict relations
Actor
Spock
Science-fiction
profession
played
character in
genre
Leonard Nimoy Star Trek
SPO Embeddings
𝑆𝑐𝑜𝑟𝑒 𝑆, 𝑃, 𝑂 = 𝑒𝑆 ⊗ 𝑒𝑂 𝑒𝑃
= 𝑒𝑆𝑇 𝐴𝑃 𝑒𝑂
35
-1
Cat
Cat ≠Dog
1
Dog
1
-1
Cat
-1
1
Dog
-1
36
Relational Machine Learning Models
• Symbolic vs. Connectionist
• MRF vs. Embedding
• (S,P,O)=0/1 classification performance [Nickel et al. 2015]
37
Relational Machine Learning Models
• Symbolic vs. Connectionist
• MRF vs. Embedding
• Graph mining
 Predict relations using graph
features, usually independently
given graph features
 In degree, out degree
 Common ancestor
 Random walk
 S->O paths
…
Actor
Spock
Science-fiction
profession
played
character in
genre
Leonard Nimoy Star Trek
𝑆𝑐𝑜𝑟𝑒 𝑆, 𝑃, 𝑂 = 𝑤𝑃 𝜙𝑆𝑃𝑂
38
Neural Embedding Models
• Predicting a score based on Subject, Object, Predicate embeddings
39
Neural Embedding Models
• Predicting a score based on Subject, Object, Predicate embeddings
S
LookupTable
O
P
LookupTable
𝑒𝑆
𝑒𝑂
𝑒𝑃
40
Neural Embedding Models
• Predicting a score based on Subject, Object, Predicate embeddings
S
LookupTable
O
P
LookupTable
𝑒𝑆
𝑒𝑂
Something that
allows backprop
𝑆𝑐𝑜𝑟𝑒(𝑆, 𝑃, 𝑂)
𝑒𝑃
41
Neural Embedding Models
• Predicting a score based on Subject, Object, Predicate embeddings
P
S
LookupTable
O
𝑒𝑆
𝑒𝑂
Something that
allows backprop
𝑆𝑐𝑜𝑟𝑒𝑃 (𝑆, 𝑂)
42
Neural Embedding Models
• Predicting a score based on Subject, Object, Predicate embeddings
E-MLP
𝑒𝑆
Linear
Linear
𝑆𝑐𝑜𝑟𝑒𝑃 (𝑆, 𝑂)
Linear
Linear
𝑆𝑐𝑜𝑟𝑒 (𝑆, 𝑃, 𝑂)
𝑒𝑂
ER-MLP
[Dong et al. 2014]
𝑒𝑆
𝑒𝑂
𝑒𝑃
43
Neural Embedding Models
• Predicting a score based on Subject, Object, Predicate embeddings
Structured
Embedding (SE)
[Bordes et al. 2011]
𝑒𝑆
𝑒𝑂
𝑒𝑆
TransE
Linear
Linear
-L1 distance
𝑆𝑐𝑜𝑟𝑒𝑃 (𝑆, 𝑂)
-L2 distance
𝑆𝑐𝑜𝑟𝑒(𝑆, 𝑃, 𝑂)
CAddTable
𝑒𝑂
[Bordes et al. 2013]
𝑒𝑃
𝑒𝑆
TransR
Linear
CAddTable
𝑒𝑂
[Lin et al. 2015]
𝑒𝑃
Linear
-L2
distance
𝑆𝑐𝑜𝑟𝑒(𝑆, 𝑃, 𝑂)
44
Neural Embedding Models
• Predicting a score based on Subject, Object, Predicate embeddings
RESCAL
[Nickel et al. 2011]
NTN
[Socher et al. 2013]
HolE
[Nickel et al. 2015]
𝑒𝑆
𝑒𝑆𝑇 𝐴𝑃 𝑒𝑂
𝑆𝑐𝑜𝑟𝑒𝑃 (𝑆, 𝑂)
𝑒𝑂
𝑒𝑆
𝑒𝑂
𝑒𝑆
𝑒𝑂
𝑒𝑃
𝑒𝑆𝑇 𝒜𝑃 𝑒𝑂
Linear
𝑒𝑃𝑇 (𝑒𝑆 ∗ 𝑒𝑂 )
𝑆𝑐𝑜𝑟𝑒𝑃 (𝑆, 𝑂)
𝑆𝑐𝑜𝑟𝑒(𝑆, 𝑃, 𝑂)
45
Neural Embedding Models
• WordNet (WN18)
•
•
•
•
Words
40943 S&O (words)
18 Predicates (relations)
151442 triples
Search Engine
Has_instance
Direct hypernym
Trademark
Google
Yahoo Ask Jeeves
46
Neural Embedding Models
• WordNet (WN18)
•
•
•
•
• Freebase (FB15k)
•
•
•
•
Words
40943 S&O (words)
18 Predicates (relations)
151442 triples
General facts
14951 S&O (words)
1345 Predicates (relations)
592213 triples
Opus 13
Composition type
Also_known_as
Piano sonata
Notable_for
Pathétique
Notable_for
/type/object/key
Top_equivalent_webpage
Pianosonates
http://imslp.org/wiki/index.html?curid=1410
47
Neural Embedding Models
• WordNet (WN18)
•
•
•
•
Words
40943 S&O (words)
18 Predicates (relations)
151442 triples
• Freebase (FB15k)
•
•
•
•
General facts
14951 S&O (words)
1345 Predicates (relations)
592213 triples
• (?,P,O) => S from all possible {S}
48
Neural Embedding Models
• WordNet (WN18)
•
•
•
•
Words
40943 S&O (words)
18 Predicates (relations)
151442 triples
• Freebase (FB15k)
•
•
•
•
General facts
14951 S&O (words)
1345 Predicates (relations)
592213 triples
• (?,P,O) => S from all possible {S}
• Locally Closed World Assumption (LCWA)
• Multiple S
• Raw
• Filter: remove other true S’
49
Neural Embedding Models
• WordNet (WN18)
•
•
•
•
• Freebase (FB15k)
•
•
•
•
Words
40943 S&O (words)
18 Predicates (relations)
151442 triples
General facts
14951 S&O (words)
1345 Predicates (relations)
592213 triples
• Accuracy @1,3,10
• Mean Reciprocal Rank (MRR)
1
𝑀𝑅𝑅 =
𝑁
𝑛
𝑖=1
1
𝑅𝑎𝑛𝑘
50
Neural Embedding Models
• [Nickel et al. 2015]
• WordNet
Method
ER-MLP
TransE
TransR
RESCAL
HolE
MRR
Filtered
Raw
Accuracy @x (filtered?)
1
3
10
0.712
0.495
0.605
0.528
0.351
0.427
62.6
11.3
33.5
77.5
88.8
87.6
86.3
94.3
94.3
0.890
0.938
0.603
0.616
84.2
93.0
90.4
94.5
92.8
94.9
51
Neural Embedding Models
• [Nickel et al. 2015]
• Freebase
Method
ER-MLP
TransE
TransR
RESCAL
HolE
MRR
Filtered
Raw
Accuracy @x (filtered?)
1
3
10
0.288
0.463
0.346
0.155
0.222
0.198
17.3
29.7
21.8
31.7
57.8
40.4
50.1
74.9
58.2
0.354
0.524
0.189
0.232
23.5
40.2
40.9
61.3
58.7
73.9
52
Neural Embedding Models
• [Nickel et al. 2015]
• Freebase
Method
ER-MLP
TransE
TransR
RESCAL
HolE
MRR
Filtered
Raw
Accuracy @x (filtered?)
1
3
10
0.288
0.463
0.346
0.155
0.222
0.198
17.3
29.7
21.8
31.7
57.8
40.4
50.1
74.9
58.2
0.354
0.524
0.189
0.232
23.5
40.2
40.9
61.3
58.7
73.9
53
Neural Embedding Models
• Qualitative: Word2Vec visualizations (again) [Mikolov et al. 2013]
54
Big Knowledge Graphs
• Confident predictions [Dong et al. 2014]
• Prob(SPO)>0.9
55
Big Knowledge Graphs
• Confident predictions
• Google Knowledge Vault
[Dong et al. 2014]
• Web information extraction
• Neural embedding
• Path ranking
56
Knowledge base and VQA
• Incorporating entity and relation embeddings
• Improving VQA model
• When entities and relations become compositional
57