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A Study of the Similarities of Entity Embeddings
Learned from Different Aspects of a Knowledge
Base for Item Recommendations
Guangyuan Piao, John G. Breslin
Insight Centre for Data Analytics @NUI Galway
03/06/2018	–	07/06/2018,	DL4KG@ESWC2018
●  Background
●  Different Aspects of Knowledge for Entities/Items
●  Similarities of Entity Embeddings from Different Aspects
●  Experiment Setup
●  Results
●  Summary and Future Work
2
Contents
3
Knowledge Bases
3
●  4.58 million things
●  411,000 creative works
●  music albums, artists
●  films/movies
●  books
●  video games
●  …
4
Knowledge Bases for Recommender Systems
4
Linked Open Data-enabled Recommender Systems (LODRS)
●  semantic similarity/distance measures [ISWC’10, AAAI’10, SAC’16]
●  graph-based algorithms such as PageRank [UMAP’16, WWW’15]
●  machine learning approaches [RecSys’12, TIST’16, WISE’17]
Chase_films … Auto_racing_films
5
Knowledge Bases for Recommender Systems
5
Linked Open Data-enabled Recommender Systems (LODRS)
●  semantic similarity/distance measures [ISWC’10, AAAI’10, SAC’16]
●  graph-based algorithms such as PageRank [UMAP’16, WWW’15]
●  machine learning approaches [RecSys’12, TIST’16, WISE’17]
Chase_films … Auto_racing_films
the similarities between those entity embeddings
learned from other aspects of KBs are not explored
6
Different Aspects of Knowledge for Entities
6
Soulless_(film)
thumbnail
visual knowledge
7
Different Aspects of Knowledge for Entities
7
Soulless_(film)
Fedor_Bondarchuk
Sergei_Minaev
…
wikiPageWikiLink
wikiPageWikiLink
Danila_Kozlovsky
…
homogeneous graph
thumbnail
visual knowledge
8
Different Aspects of Knowledge for Entities
8
Soulless_(film)
Fedor_Bondarchuk
Sergei_Minaev
…
wikiPageWikiLink
wikiPageWikiLink
Danila_Kozlovsky
…
homogeneous graph
thumbnail
visual knowledge
Maria_Kozhevnikova
starring
Sergei_Minaev
writer
…
heterogeneous graph
9
Different Aspects of Knowledge for Entities
9
Soulless_(film)
Fedor_Bondarchuk
Sergei_Minaev
…
wikiPageWikiLink
wikiPageWikiLink
Danila_Kozlovsky
…
homogeneous graph
thumbnail
visual knowledge
Soulless is a 2012 Russian black
comedy-drama film based on the novel
Soulless…
abstracts
textual knowledge
Maria_Kozhevnikova
starring
Sergei_Minaev
writer
…
heterogeneous graph
10
Deep Learning for Learning Entity Embeddings
10
homogeneous graph visual knowledge
textual knowledge heterogeneous graph
Deep Learning
11
Aim of Work
11
homogeneous graph visual knowledge
textual knowledge heterogeneous graph
Deep Learning
12
word2vec: skip-gram [NIPS’13]
12
figure: http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
13
word2vec: skip-gram [NIPS’13]
13
figure: http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
14
node2vec [KDD’16]
●  skip-gram model for homogeneous networks (dbo:wikiPageWikiLink)
●  random walk based on a defined searching strategy
●  the obtained sequence of nodes è skip-gram
Deep Learning for Learning Entity Embeddings
14
homogeneous graph
node2vec
15
doc2vec [ICML’14]
●  doc vector + contextual word vectors è next word
●  docs in our context: dbo:abstracts of domain-specific entities
●  multiclass classification
Deep Learning for Learning Entity Embeddings
15
textual knowledge
doc2vec
16
TransE [NIPS’13]
●  to satisfy for a valid triple (s, p, o)
●  for all domain-specific triples
Deep Learning for Learning Entity Embeddings
16
heterogeneous graph
TransE
17
Experiment Setup - Datasets
17
last.fm [ISMIR’15]
●  232 musical artists, and
●  the top-10 similar artists for each artist
dbbook1
●  6,181 users
●  6,733 items
●  randomly choose 300 users who have liked at least 10 books
cold-start scenario
●  give an item liked by each user,
●  we recommend top similar items of that item
[1] http://challenges.2014.eswc-conferences.org/index.php/RecSys#DBbook_dataset
18
Experiment Setup - Datasets
18
domain-specific entities
●  music: rdf:type è dbo:MusicalArtist & rdf:type è dbo:Band
●  book: rdf:type è dbo:Book
19
Experiment Setup - Evaluation Metrics
19
nDCG@N (normalized Discounted Cumulative Gain)
●  takes into account the relevant items as well as their rank positions
P@N (precision)
●  mean probability of items in the top-N list are relevant
R@N (recall)
●  mean probability of relevant items retrieved in the top-N list
20
Compared Methods
20
Resim [SAC’16]
●  semantic distance/similarity measure for LODRS
Cos(Vtk:Doc2Vec)
●  cosine similarity for embeddings learned from textual knowledge
Cos(Vhmk:Node2Vec)
●  cosine similarity for embeddings learned from homogeneous graph
Cos(Vhtk:TransE)
●  cosine similarity for embeddings learned from heterogeneous graph
Cos([Vx, Vy])
●  cosine similarity for concatenated embeddings
21
Results – last.fm
21
●  best performance: Cos(Vhmk:Node2Vec) followed by Cos(Vhtk:TransE)
●  Cos([Vhtk:TransE, Vtk:Doc2Vec]) é Cos(Vhtk:TransE) or Cos(Vtk:Doc2Vec)
●  Cos([all]) & Cos(Vhmk:Node2Vec) é Resim
22
Results – dbbook
22
●  best performance: Cos(Vhmk:Node2Vec) followed by Cos(Vhtk:TransE)
●  Cos([Vhtk:TransE, Vtk:Doc2Vec]) é Cos(Vhtk:TransE) or Cos(Vtk:Doc2Vec)
23
Summary and Future Work
23
summary
●  best perf.: embeddings learned from the homogeneous graph
●  concatenated embeddings learned from the heterogeneous graph &
textual knowledge é compared to the ones learned from each
future work
●  other state-of-the-art approaches for learning embeddings from
different aspects
●  textual knowledge: tweet2vec [SIGIR’16] etc.
●  heterogeneous graph: ETransE [PCS’17], rdf2vec [SWJ,18]
●  how to choose domain-specific knowledge (triples) better?
●  e.g., domain-specific subgraph extraction [BigData’16]
●  other approaches beyond concatenation for combining embeddings
learned from different aspects
funded by
thank you for your attention!
Guangyuan Piao
homepage: http://parklize.github.io
e-mail: guangyuan.piao@insight-centre.org
scholar: https://goo.gl/tgK9bk
slideshare: http://www.slideshare.net/parklize

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A Study of the Similarities of Entity Embeddings Learned from Different Aspects of a Knowledge Base for Item Recommendations

  • 1. A Study of the Similarities of Entity Embeddings Learned from Different Aspects of a Knowledge Base for Item Recommendations Guangyuan Piao, John G. Breslin Insight Centre for Data Analytics @NUI Galway 03/06/2018 – 07/06/2018, DL4KG@ESWC2018
  • 2. ●  Background ●  Different Aspects of Knowledge for Entities/Items ●  Similarities of Entity Embeddings from Different Aspects ●  Experiment Setup ●  Results ●  Summary and Future Work 2 Contents
  • 3. 3 Knowledge Bases 3 ●  4.58 million things ●  411,000 creative works ●  music albums, artists ●  films/movies ●  books ●  video games ●  …
  • 4. 4 Knowledge Bases for Recommender Systems 4 Linked Open Data-enabled Recommender Systems (LODRS) ●  semantic similarity/distance measures [ISWC’10, AAAI’10, SAC’16] ●  graph-based algorithms such as PageRank [UMAP’16, WWW’15] ●  machine learning approaches [RecSys’12, TIST’16, WISE’17] Chase_films … Auto_racing_films
  • 5. 5 Knowledge Bases for Recommender Systems 5 Linked Open Data-enabled Recommender Systems (LODRS) ●  semantic similarity/distance measures [ISWC’10, AAAI’10, SAC’16] ●  graph-based algorithms such as PageRank [UMAP’16, WWW’15] ●  machine learning approaches [RecSys’12, TIST’16, WISE’17] Chase_films … Auto_racing_films the similarities between those entity embeddings learned from other aspects of KBs are not explored
  • 6. 6 Different Aspects of Knowledge for Entities 6 Soulless_(film) thumbnail visual knowledge
  • 7. 7 Different Aspects of Knowledge for Entities 7 Soulless_(film) Fedor_Bondarchuk Sergei_Minaev … wikiPageWikiLink wikiPageWikiLink Danila_Kozlovsky … homogeneous graph thumbnail visual knowledge
  • 8. 8 Different Aspects of Knowledge for Entities 8 Soulless_(film) Fedor_Bondarchuk Sergei_Minaev … wikiPageWikiLink wikiPageWikiLink Danila_Kozlovsky … homogeneous graph thumbnail visual knowledge Maria_Kozhevnikova starring Sergei_Minaev writer … heterogeneous graph
  • 9. 9 Different Aspects of Knowledge for Entities 9 Soulless_(film) Fedor_Bondarchuk Sergei_Minaev … wikiPageWikiLink wikiPageWikiLink Danila_Kozlovsky … homogeneous graph thumbnail visual knowledge Soulless is a 2012 Russian black comedy-drama film based on the novel Soulless… abstracts textual knowledge Maria_Kozhevnikova starring Sergei_Minaev writer … heterogeneous graph
  • 10. 10 Deep Learning for Learning Entity Embeddings 10 homogeneous graph visual knowledge textual knowledge heterogeneous graph Deep Learning
  • 11. 11 Aim of Work 11 homogeneous graph visual knowledge textual knowledge heterogeneous graph Deep Learning
  • 12. 12 word2vec: skip-gram [NIPS’13] 12 figure: http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
  • 13. 13 word2vec: skip-gram [NIPS’13] 13 figure: http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
  • 14. 14 node2vec [KDD’16] ●  skip-gram model for homogeneous networks (dbo:wikiPageWikiLink) ●  random walk based on a defined searching strategy ●  the obtained sequence of nodes è skip-gram Deep Learning for Learning Entity Embeddings 14 homogeneous graph node2vec
  • 15. 15 doc2vec [ICML’14] ●  doc vector + contextual word vectors è next word ●  docs in our context: dbo:abstracts of domain-specific entities ●  multiclass classification Deep Learning for Learning Entity Embeddings 15 textual knowledge doc2vec
  • 16. 16 TransE [NIPS’13] ●  to satisfy for a valid triple (s, p, o) ●  for all domain-specific triples Deep Learning for Learning Entity Embeddings 16 heterogeneous graph TransE
  • 17. 17 Experiment Setup - Datasets 17 last.fm [ISMIR’15] ●  232 musical artists, and ●  the top-10 similar artists for each artist dbbook1 ●  6,181 users ●  6,733 items ●  randomly choose 300 users who have liked at least 10 books cold-start scenario ●  give an item liked by each user, ●  we recommend top similar items of that item [1] http://challenges.2014.eswc-conferences.org/index.php/RecSys#DBbook_dataset
  • 18. 18 Experiment Setup - Datasets 18 domain-specific entities ●  music: rdf:type è dbo:MusicalArtist & rdf:type è dbo:Band ●  book: rdf:type è dbo:Book
  • 19. 19 Experiment Setup - Evaluation Metrics 19 nDCG@N (normalized Discounted Cumulative Gain) ●  takes into account the relevant items as well as their rank positions P@N (precision) ●  mean probability of items in the top-N list are relevant R@N (recall) ●  mean probability of relevant items retrieved in the top-N list
  • 20. 20 Compared Methods 20 Resim [SAC’16] ●  semantic distance/similarity measure for LODRS Cos(Vtk:Doc2Vec) ●  cosine similarity for embeddings learned from textual knowledge Cos(Vhmk:Node2Vec) ●  cosine similarity for embeddings learned from homogeneous graph Cos(Vhtk:TransE) ●  cosine similarity for embeddings learned from heterogeneous graph Cos([Vx, Vy]) ●  cosine similarity for concatenated embeddings
  • 21. 21 Results – last.fm 21 ●  best performance: Cos(Vhmk:Node2Vec) followed by Cos(Vhtk:TransE) ●  Cos([Vhtk:TransE, Vtk:Doc2Vec]) é Cos(Vhtk:TransE) or Cos(Vtk:Doc2Vec) ●  Cos([all]) & Cos(Vhmk:Node2Vec) é Resim
  • 22. 22 Results – dbbook 22 ●  best performance: Cos(Vhmk:Node2Vec) followed by Cos(Vhtk:TransE) ●  Cos([Vhtk:TransE, Vtk:Doc2Vec]) é Cos(Vhtk:TransE) or Cos(Vtk:Doc2Vec)
  • 23. 23 Summary and Future Work 23 summary ●  best perf.: embeddings learned from the homogeneous graph ●  concatenated embeddings learned from the heterogeneous graph & textual knowledge é compared to the ones learned from each future work ●  other state-of-the-art approaches for learning embeddings from different aspects ●  textual knowledge: tweet2vec [SIGIR’16] etc. ●  heterogeneous graph: ETransE [PCS’17], rdf2vec [SWJ,18] ●  how to choose domain-specific knowledge (triples) better? ●  e.g., domain-specific subgraph extraction [BigData’16] ●  other approaches beyond concatenation for combining embeddings learned from different aspects
  • 24. funded by thank you for your attention! Guangyuan Piao homepage: http://parklize.github.io e-mail: guangyuan.piao@insight-centre.org scholar: https://goo.gl/tgK9bk slideshare: http://www.slideshare.net/parklize