Temporal Knowledge Graph Completion
18 papers with code • 0 benchmarks • 0 datasets
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In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations.
In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time.
Temporal knowledge bases associate relational (s, r, o) triples with a set of times (or a single time instant) when the relation is valid.
Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings.
DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion
Product manifolds enable our approach to better reflect a wide variety of geometric structures on temporal KGs.
The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge.
Representation learning approaches for knowledge graphs have been mostly designed for static data.
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.
Temporal knowledge graph completion (TKGC) has become a popular approach for reasoning over the event and temporal knowledge graphs, targeting the completion of knowledge with accurate but missing information.