Temporal Knowledge Graph Completion Using Box Embeddings


  • Johannes Messner University of Oxford
  • Ralph Abboud University of Oxford
  • Ismail Ilkan Ceylan University of Oxford




Machine Learning (ML)


Knowledge graph completion is the task of inferring missing facts based on existing data in a knowledge graph. Temporal knowledge graph completion (TKGC) is an extension of this task to temporal knowledge graphs, where each fact is additionally associated with a time stamp. Current approaches for TKGC primarily build on existing embedding models which are developed for static knowledge graph completion, and extend these models to incorporate time, where the idea is to learn latent representations for entities, relations, and timestamps and then use the learned representations to predict missing facts at various time steps. In this paper, we propose BoxTE, a box embedding model for TKGC, building on the static knowledge graph embedding model BoxE. We show that BoxTE is fully expressive, and possesses strong inductive capacity in the temporal setting. We then empirically evaluate our model and show that it achieves state-of-the-art results on several TKGC benchmarks




How to Cite

Messner, J., Abboud, R., & Ceylan, I. I. (2022). Temporal Knowledge Graph Completion Using Box Embeddings. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 7779-7787. https://doi.org/10.1609/aaai.v36i7.20746



AAAI Technical Track on Machine Learning II