# Efficiently Embedding Dynamic Knowledge Graphs

arXiv: https://arxiv.org/abs/1910.06708 · HTML: https://mlsanity.com/paper/1910.06708/

## Key facts

- Cited by 19 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 15 Oct 2019.

## Built on by (most cited first, 19 of 19)

- [Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning](https://mlsanity.com/paper/2104.10353/) (2021)
- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [A Survey on Embedding Dynamic Graphs](https://mlsanity.com/paper/2101.01229/) (2021)
- [Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.00327/) (2021)
- [Continual Learning of Knowledge Graph Embeddings](https://mlsanity.com/paper/2101.05850/) (2021)
- [Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs](https://mlsanity.com/paper/2211.15845/) (2022)
- [Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Information](https://mlsanity.com/paper/2112.08140/) (2021)
- [Relphormer: Relational Graph Transformer for Knowledge Graph Representations](https://mlsanity.com/paper/2205.10852/) (2022)
- [Survey on English Entity Linking on Wikidata](https://mlsanity.com/paper/2112.01989/) (2021)
- [Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams](https://mlsanity.com/paper/2201.10983/) (2022)
- [On a Generalized Framework for Time-Aware Knowledge Graphs](https://mlsanity.com/paper/2207.09964/) (2022)
- [KisanQRS: A Deep Learning-based Automated Query-Response System for Agricultural Decision-Making](https://mlsanity.com/paper/2411.08883/) (2024)
- [Neurosymbolic Methods for Dynamic Knowledge Graphs](https://mlsanity.com/paper/2409.04572/) (2024)
- [Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata's Revision History](https://mlsanity.com/paper/2112.05003/) (2021)
- [Conversational Recommendation as Retrieval: A Simple, Strong Baseline](https://mlsanity.com/paper/2305.13725/) (2023)
- [Poisson-Gamma Modeling of Inter-Relational Dependencies in Dynamic Knowledge Graphs](https://mlsanity.com/paper/2607.02872/) (2026)
- [A Strategy for Implementing description Temporal Dynamic Algorithms in Dynamic Knowledge Graphs by SPIN](https://mlsanity.com/paper/2401.07890/) (2024)
- [VN Network: Embedding Newly Emerging Entities with Virtual Neighbors](https://mlsanity.com/paper/2402.14033/) (2024)
- [Improving Continual Learning of Knowledge Graph Embeddings via Informed Initialization](https://mlsanity.com/paper/2511.11118/) (2025)

## Abstract (arXiv)

Knowledge graph (KG) embedding encodes the entities and relations from a KG into low-dimensional vector spaces to support various applications such as KG completion, question answering, and recommender systems. In real world, knowledge graphs (KGs) are dynamic and evolve over time with addition or deletion of triples. However, most existing models focus on embedding static KGs while neglecting dynamics. To adapt to the changes in a KG, these models need to be retrained on the whole KG with a high time cost. In this paper, to tackle the aforementioned problem, we propose a new context-aware Dynamic Knowledge Graph Embedding (DKGE) method which supports the embedding learning in an online fashion. DKGE introduces two different representations (i.e., knowledge embedding and contextual element embedding) for each entity and each relation, in the joint modeling of entities and relations as well as their contexts, by employing two attentive graph convolutional networks, a gate strategy, and translation operations. This effectively helps limit the impacts of a KG update in certain regions, not in the entire graph, so that DKGE can rapidly acquire the updated KG embedding by a proposed online learning algorithm. Furthermore, DKGE can also learn KG embedding from scratch. Experiments on the tasks of link prediction and question answering in a dynamic environment demonstrate the effectiveness and efficiency of DKGE.