Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction
DOKPE
- Rosso, Paolo University of Fribourg
- Yang, Dingqi University of Fribourg
- Cudré-Mauroux, Philippe University of Fribourg
- 2020
Published in:
- Proceedings of The Web Conference 2020. - New York : ACM. - 2020
English
Knowledge Graph (KG) embeddings are a powerful tool for predicting missing links in KGs. Existing techniques typically represent a KG as a set of triplets, where each triplet (h,r,t) links two entities h and t through a relation r, and learn entity/relation embeddings from such triplets while preserving such a structure. However, this triplet representation oversimplifies the complex nature of the data stored in the KG, in particular for hyper-relational facts, where each fact contains not only a base triplet (h,r,t), but also the associated key-value pairs (k,v). Even though a few recent techniques tried to learn from such data by transforming a hyper-relational fact into an n-ary representation (i.e., a set of key-value pairs only without triplets), they result in suboptimal models as they are unaware of the triplet structure, which serves as the fundamental data structure in modern KGs and preserves the essential information for link prediction. To address this issue, we propose HINGE, a hyper-relational KG embedding model, which directly learns from hyper-relational facts in a KG. HINGE captures not only the primary structural information of the KG encoded in the triplets, but also the correlation between each triplet and its associated key-value pairs. Our extensive evaluation shows the superiority of HINGE on various
link prediction tasks over KGs. In particular, HINGE consistently
outperforms not only the KG embedding methods learning from triplets only (by 0.81-41.45% depending on the link prediction tasks and settings), but also the methods learning from hyper-relational facts using the n-ary representation (by 13.2-84.1%).
link prediction tasks over KGs. In particular, HINGE consistently
outperforms not only the KG embedding methods learning from triplets only (by 0.81-41.45% depending on the link prediction tasks and settings), but also the methods learning from hyper-relational facts using the n-ary representation (by 13.2-84.1%).
- Faculty
- Faculté des sciences et de médecine
- Department
- Département d'Informatique
- Language
-
- English
- Classification
- Computer science and technology
- License
- CC BY-NC
- Open access status
- green
- Identifiers
- Persistent URL
- https://folia.unifr.ch/unifr/documents/325395
Statistics
Document views: 25
File downloads:
- rosso2020www_0.pdf: 87