English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Temporal Knowledge Graphs store events in the form of subjects, relations,objects, and timestamps which are often represented by dynamic heterogeneousgraphs. Event forecasting is a critical and challenging task in TemporalKnowledge Graph reasoning that predicts the subject or object of an event inthe future. To obtain temporal embeddings multi-step away in the future,existing methods learn generative models that capture the joint distribution ofthe observed events. To reduce the high computation costs, these methods relyon unrealistic assumptions of independence and approximations in training andinference. In this work, we propose SeDyT, a discriminative framework thatperforms sequence modeling on the dynamic entity embeddings to solve themulti-step event forecasting problem. SeDyT consists of two components: aTemporal Graph Neural Network that generates dynamic entity embeddings in thepast and a sequence model that predicts the entity embeddings in the future.Compared with the generative models, SeDyT does not rely on any heuristic-basedprobability model and has low computation complexity in both training andinference. SeDyT is compatible with most Temporal Graph Neural Networks andsequence models. We also design an efficient training method that trains thetwo components in one gradient descent propagation. We evaluate the performanceof SeDyT on five popular datasets. By combining temporal Graph Neural Networkmodels and sequence models, SeDyT achieves an average of 2.4% MRR improvementwhen not using the validation set and more than 10% MRR improvement when usingthe validation set.

SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity Embeddings