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 . |
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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. |
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| Challenge: | Temporal knowledge graph reasoning is a crucial task for answering time-dependent questions within a knowledge graph (KG). |
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| Challenge: | Existing Temporal Knowledge Graphs (TKGs) only contain their core entities and form them as quadruples. |
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| Challenge: | Existing methods for Temporal Knowledge Graph reasoning capture intra- and inter-time latent relations between entities that appear at different times. |
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| Challenge: | Existing KG embedding methods ignore this temporal dimension while learning embedds of the KG elements. |
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| Challenge: | Existing models for Temporal Knowledge Graph reasoning capture repetitive history, ignoring the entity's multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning. |
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| Challenge: | Temporal Knowledge Graphs (TKGs) represent dynamic facts as timestamped relations between entities. Large Language Models (LLMs) have sparked interest in using pretrained generative models for TKG completion. |
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| Challenge: | Current knowledge graph models focus on embedding entities and relations, overlooking the broader structure of the entire knowledge graph. |
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| Challenge: | Existing models rely on historical information to learn embeddings for entities, but ignore the evolution of facts. |
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