Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction

24 views

Published on

WWW 2020 presentation
Paolo Rosso, eXascale Infolab

Published in: Technology
  • Be the first to comment

  • Be the first to like this

Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction

  1. 1. Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction Paolo Rosso, Dingqi Yang, Philippe Cudré-Mauroux eXascale Infolab University of Fribourg, Switzerland April 2020 – The Web Conference 2020 0
  2. 2. Knowledge Graph (KG) • Multi-relational graph composed of entities and relations • Each fact is represented as a triplet head entity, relation, tail entity • A fact indicates that two entities are connected by a specific relation 1
  3. 3. Knowledge Graph embeddings • Represent entities and relations in a Knowledge Graph using a vector space • Learn low-dimensional vector representation of entities and relations from a Knowledge Graph while preserving the graph properties Marie Curie University of Paris educated at Semantic search Question-answering Query expansion Recommendation systems 2 TransE
  4. 4. Hyper-relational facts • Triplet-based representation of a KG oversimplifies the complex nature of hyper-relational data • A hyper-relational data represents a fact containing multiple relations and entities • A hyper-relational is represented by a triplet and a set of key-value pairs 3
  5. 5. Hyper-relational facts representations 1. Triplet only: keeping only the base triplet (irreversible information loss) 2. Reification: adding an artificial entity to represent the base triplet and use it as head entity for the k-v pair (creates additional triplets) 3. Relation paths: creating a relation path of relation-key and use it as a relation to connect head and key (creates additional triplets) … we need a method to better learn hyper-relation facts! 4
  6. 6. 5 Marie Curie educated at University of Paris Hyper-relational facts representations Triplet only
  7. 7. Hyper-relational facts representations 1. Triplet only: keeping only the base triplet (irreversible information loss) 2. Reification: adding an artificial entity to represent the base triplet and use it as head entity for the k-v pair (creates additional triplets) 3. Relation paths: creating a relation path of relation-key and use it as a relation to connect head and key (creates additional triplets) … we need a method to better learn hyper-relation facts! 6
  8. 8. 7 q := Marie Curie educated at University of Paris q academic major Physics q academic degree Master of Science Hyper-relational facts representations Reification
  9. 9. Hyper-relational facts representations 1. Triplet only: keeping only the base triplet (irreversible information loss) 2. Reification: adding an artificial entity to represent the base triplet and use it as head entity for the k-v pair (creates additional triplets) 3. Relation paths: creating a relation path of relation-key and use it as a relation to connect head and key (creates additional triplets) … we need a method to better learn hyper-relation facts! 8
  10. 10. 9 Marie Curie educated at University of Paris rk1 := educated at AND academic major rk2 := educated at AND academic degree Marie Curie rk1 Physics Marie Curie rk2 Master of Science Relation paths
  11. 11. Hyper-relational facts representations 1. Triplet only: keeping only the base triplet (irreversible information loss) 2. Reification: adding an artificial entity to represent the base triplet and use it as head entity for the k-v pair (creates additional triplets) 3. Relation paths: creating a relation path of relation-key and use it as a relation to connect head and key (creates additional triplets) 10
  12. 12. 11 • Hyper-relational fact can be transformed into n-ary representation • E.g., relation education is extracted from the hyper-relational fact • N-ary representation {education_head:Marie Curie, education_tail:University of Paris, education_major:Physics, education_degree:Master of Science} … but triplets serve as the fundamental data structure in the modern KGs because they preserve the essential information for link prediction N-Ary Representation
  13. 13. 12 • Hyper-relational fact can be transformed into n-ary representation • E.g., relation education is extracted from the hyper-relational fact • N-ary representation {education_head:Marie Curie, education_tail:University of Paris, education_major:Physics, education_degree:Master of Science} … but triplets serve as the fundamental data structure in the modern KGs because they preserve the essential information for link prediction N-Ary Representation
  14. 14. Limitation of N-Ary Representation 13 • Null model: one triplet is extracted from the n-ary representation of each hyper- relational fact via randomly sampled relation path (h, rhki, vi) • E.g., Marie Curie education_head_education_major Physics • Null hypothesis: the information for link prediction encoded by the original base triplets is not greater than the triplets created by the null model • We test Null hypothesis on link prediction tasks using models on original basic triplets and null model • Rejected Null hypothesis - results: • Performance of the baselines on original base triplets is consistently and significantly better than the performance from the null model • Information encoded in original base triplets is greater than the information encoded in null model
  15. 15. HINGE: Hyper-relatIonal kNowledge Graph Embedding • KG embedding model to learn hyper-relational facts in a KG • Capturing the primary structural information of the KG encoded in the triplets in order to preserve the essential information for link prediction • Capturing the correlation between each triplet and its associated key- value pairs (if any) 14
  16. 16. HINGE overview 15
  17. 17. HINGE architecture • The base triplet encodes the primary structural information and capture essential information for link prediction • CNN to learn from the base triplet and capture the triple-wise relatedness between h,r,t embeddings • The triple-wise relatedness vector used to characterize the plausibility of the base triplet of being true 16
  18. 18. HINGE architecture • CNN to learn from each key-value pair associated with the triplet together and capture the quintuple-wise relatedness between h,r,t,k,v embeddings • The quintuple-wise relatedness vector used to characterize the plausibility of the base triplet associated with the k-v pair being true 17
  19. 19. HINGE architecture • Concatenate the triple-wise and quintuple- wise relatedness future vectors • Get the minimum value along each dimension • For a valid hyper-relational fact, the relatedness vectors should be high • The minimum score along each dimension is expected to be high • Fully connected layer to output the score for the input hyper-relational fact 18
  20. 20. HINGE architecture 19
  21. 21. Experiments • Learning from triplets facts only: • Translational distance models: TransE, TransH, TransR and TransD • Semantic matching models: Rescal, DistMult, ComplEx, Analogy and ConvE • Learning from hyper-relational facts: • m-TransH, RAE, NaLP, NaLP-Fix 20
  22. 22. Datasets • Hyper-relational datasets • JF17K: filtered from Freebase to have a significant presence of hyper-relational facts • WikiPeople: extracted from Wikidata and focuses on entities of type human • JF17K and WikiPeople contain triplet facts and hyper-relational facts • Dataset configurations: Triplet Only, Relation Path, Reification, Original Hyper-Relational 21
  23. 23. Evaluation Tasks and Metrics • Link prediction: given two elements of a triplet in a fact, predict the missing one • E.g., (?,r,t), (h,?,t) or (h,r,?) • Mean Reciprocal Rank (MRR), Hits@10 and Hits@1 22
  24. 24. Comparison with Baselines Learning from Hyper-Relational Facts 23 • HINGE outperforms all baselines learning from hyper-relational facts • Among the baselines: • NaLP shows the best performance (it learns the relatedness between k-v pairs but it is not aware of the triplet structure) • m-TransH and RAE learn only from entities
  25. 25. Comparison with Baselines Learning from Triplets Only Dataset Transformation Setting WikiPeople JF17K Head/Tail Prediction (%) Relation Prediction (%) Head/Tail Prediction (%) Relation Prediction (%) Basic 0.81 18.55 41.45 8.41 Relation Path 2.85 24.65 22.96 12.24 Reification 3.28 22.22 29.71 6.53 24
  26. 26. Comparison with Baselines Learning from Triplets Only Dataset Transformation Setting WikiPeople JF17K Head/Tail Prediction (%) Relation Prediction (%) Head/Tail Prediction (%) Relation Prediction (%) Basic 0.81 18.55 41.45 8.41 Relation Path 2.85 24.65 22.96 12.24 Reification 3.28 22.22 29.71 6.53 • Basic setting discards the key-value pairs (information loss) 25
  27. 27. Comparison with Baselines Learning from Triplets Only Dataset Transformation Setting WikiPeople JF17K Head/Tail Prediction (%) Relation Prediction (%) Head/Tail Prediction (%) Relation Prediction (%) Basic 0.81 18.55 41.45 8.41 Relation Path 2.85 24.65 22.96 12.24 Reification 3.28 22.22 29.71 6.53 • Basic setting discards the key-value pairs (information loss) • Exception for the head/tail prediction on WikiPeople with Basic setting (dominance of triplet) 26
  28. 28. Comparison with Baselines Learning from Triplets Only Dataset Transformation Setting WikiPeople JF17K Head/Tail Prediction (%) Relation Prediction (%) Head/Tail Prediction (%) Relation Prediction (%) Basic 0.81 18.55 41.45 8.41 Relation Path 2.85 24.65 22.96 12.24 Reification 3.28 22.22 29.71 6.53 • Basic setting discards the key-value pairs (information loss) • Exception for the head/tail prediction on WikiPeople with Basic setting (dominance of triplet) • Relation Path and Reification create extra entities/relations (not capturing essential information for link prediction) 27
  29. 29. Conclusions • We investigate the problem of hyper-relational KG embedding showing that triplets serve as the fundamental data structure in modern KGs encoding the essential information for link prediction • We introduce HINGE, a KG embedding model that captures the information encoded in the triplets and the correlation between each triplet and its associated key-value pairs • We show that the triplet structure is the fundamental structure for link prediction and show the limitations of a commonly used representation scheme • We observe an improvement on various link prediction tasks with different data transformation settings 28
  30. 30. Any questions? exascale.info Paper link: bit.ly/2VaDNC8 29

×