| Challenge: | Existing KG embedding methods ignore this temporal dimension while learning embedds of the KG elements. |
| Approach: | They propose a temporally aware KG embedding method which incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane. |
| Outcome: | The proposed method performs KG inference using temporal guidance and predicts scopes for relational facts with missing time annotations. |
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Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework (2021.emnlp-main)
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| Challenge: | Various temporal knowledge graph (KG) completion models have been proposed . knowledge graphs are typically static and store facts in their current state . |
| Approach: | They propose to use temporal embeddings and a score function to model temporal knowledge graphs . they classify the temporal embedded methods into two classes: timestamp and time-dependent . |
| Outcome: | The proposed models outperform current models on ICEWS datasets with 3000 experiments and 13159 GPU hours. |
Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs (2024.findings-emnlp)
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| Challenge: | Existing models of temporal fact reasoning do not explicitly specify temporal information for each fact. |
| Approach: | They propose a new type of data structure called hyper-relational TKG to study temporal fact reasoning over HKGs. |
| Outcome: | The proposed model is based on two new benchmark HTKG datasets . it provides additional key-value pairs (i.e., qualifiers) for each KG fact . |
Leveraging 3D Gaussian for Temporal Knowledge Graph Embedding (2025.findings-emnlp)
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| Challenge: | Representation learning in knowledge graphs (KGs) has focused on static data, yet many real-world knowledge graph are inherently dynamic. |
| Approach: | They propose a temporal embedding method inspired by 3D Gaussian Splatting where entities, relations, and timestamps are modeled as 3D gaussian distributions with learnable structured covariance. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three benchmark TKG datasets. |
Temporal Knowledge Graph Reasoning with Dynamic Hypergraph Embedding (2024.lrec-main)
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| Challenge: | Existing models that model temporal dynamics with knowledge graphs and graph convolution networks lack high-order interactions between objects in TKG, which is an important factor to predict future facts. |
| Approach: | They propose to embed temporal knowledge graph reasoning by constructing hypergraphs based on temporal information graphs at different timestamps and then adapt dynamic meta-embedding to fit TKG. |
| Outcome: | The proposed method outperforms baseline models on public TKG datasets and provides good interpretation for the predicted results. |
TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation (2020.coling-main)
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| Challenge: | Existing knowledge graphs that contain time information for entities and relations have been used for learning and inference. |
| Approach: | They propose a temporal evolution of entity embedding that defines the temporal rotation from the initial time to the current time in the complex vector space. |
| Outcome: | The proposed model outperforms existing state-of-the-art models for link prediction on three different TKGs. |
EventKE: Event-Enhanced Knowledge Graph Embedding (2021.findings-emnlp)
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| Challenge: | Experimental results show that events can greatly improve the quality of KG embeddings on multiple downstream tasks. |
| Approach: | They propose an event-enhanced KG embedding model that incorporates events into KGs . they first incorporate event nodes by building a heterogeneous network with event argument links . |
| Outcome: | The proposed model incorporates event nodes into the original knowledge graphs . it can be used to fuse event information into the KG embeddings on multiple tasks . |
Towards Understanding the Geometry of Knowledge Graph Embeddings (P18-1)
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| Challenge: | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embeddable methods. |
| Approach: | They propose to use KG embedding methods to represent entities and relations as vectors in a high-dimensional space. |
| Outcome: | The proposed methods represent entities and relations in KGs as vectors in a high-dimensional space. |
Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs (2021.emnlp-main)
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| Challenge: | Existing embedding-based approaches disregard time information that exists in many large-scale knowledge graphs, leaving much room for improvement. |
| Approach: | They propose a Time-aware Entity Alignment approach that incorporates relation and time information into a vector space and uses Graph Neural Networks to learn entity representations. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on real-world TKG datasets due to the inclusion of time information. |
Knowledge Graph Alignment with Entity-Pair Embedding (2020.emnlp-main)
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| Challenge: | Existing methods for Knowledge Graph (KG) alignment are not satisfactory. |
| Approach: | They propose a method that directly learns embeddings of entity-pairs for KG alignment. |
| Outcome: | The proposed approach can achieve state-of-the-art on five real-world datasets. |
ECOLA: Enhancing Temporal Knowledge Embeddings with Contextualized Language Representations (2023.findings-acl)
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Zhen Han, Ruotong Liao, Jindong Gu, Yao Zhang, Zifeng Ding, Yujia Gu, Heinz Koeppl, Hinrich Schütze, Volker Tresp
| Challenge: | Existing enhancement approaches cannot be applied to temporal knowledge graphs (tKGs) existing enhancement approaches assume knowledge embedding is time-independent, whereas entity embedded in tKG models evolves . |
| Approach: | They propose to use textual data to enhance temporal knowledge embedding by Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations (ECOLA) to evaluate ECOLA, they introduce three new datasets for training and evaluation. |
| Outcome: | The proposed model significantly improves Hits@1 on the link prediction task. |