Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

TL;DR

Know-Evolve introduces a deep temporal reasoning model for dynamic knowledge graphs, effectively capturing evolving entity representations and predicting fact occurrences over time.

Contribution

It presents a novel deep evolutionary knowledge network that models non-linear temporal evolution and improves reasoning over dynamic, timestamped knowledge graphs.

Findings

Significantly outperforms existing relational learning methods.

Accurately predicts occurrence and recurrence times of facts.

Effective on large-scale real-world datasets.

Abstract

The availability of large scale event data with time stamps has given rise to dynamically evolving knowledge graphs that contain temporal information for each edge. Reasoning over time in such dynamic knowledge graphs is not yet well understood. To this end, we present Know-Evolve, a novel deep evolutionary knowledge network that learns non-linearly evolving entity representations over time. The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings. We demonstrate significantly improved performance over various relational learning approaches on two large scale real-world datasets. Further, our method effectively predicts occurrence or recurrence time of a fact which is novel compared to prior reasoning approaches in multi-relational setting.