Temporal Knowledge Graph Completion using Box Embeddings

Understand

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.