Papers with embedding-based
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks (D18-1)
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| Challenge: | Existing approaches to align multilingual knowledge graphs with counterparts in different languages are not effective. |
| Approach: | They propose a novel approach for cross-lingual KG alignment via graph convolutional networks . they train GCNs to embed entities of each language into a unified vector space . |
| Outcome: | The proposed approach gets the best performance on real multilingual KGs compared with other embedding-based approaches. |
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models (2024.naacl-long)
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| Challenge: | Existing methods to forecast links on temporal knowledge graphs are embedding-based . but they face a strong challenge in modeling the unseen zero-shot relations . |
| Approach: | They propose to embed knowledge graphs (TKGF) entities and relations based on observed contexts into embedding-based methods to model unseen zero-shot relations. |
| Outcome: | The proposed methods show strong performance on traditional TKG forecasting benchmarks, but they face a strong challenge in modeling unseen zero-shot relations that have no prior graph context. |
Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion? (2024.naacl-long)
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| Challenge: | Knowledge Graph Completion (KGC) is a task that infers unseen relationships between entities . traditional embedding-based methods infer missing links using only training data . a pre-trained language model (PLM)-based KGC may be ineffective in practical applications . |
| Approach: | They propose to use knowledge Graph Completion (KGC) to infer unseen relationships . traditional embedding-based KGC methods infer missing links only from training data . they argue that pre-trained language models acquire inference abilities through pre-training . |
| Outcome: | The proposed method improves performance even though it does not use memorized knowledge. |
ENPMR-Bench: Benchmarking Proactive Memory Retrieval for Emotional Support Agents (2026.findings-acl)
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| Challenge: | Existing research treats memory as a mechanism for factual retention, neglecting its role in shaping users’ emotional experiences. |
| Approach: | They propose a benchmark for evaluating Emotional Need-aware Proactive Memory Retrieval (ENPMR) it enables agents to infer users’ latent emotional needs and proactively retrieve appropriate memories to support empathetic interaction. |
| Outcome: | The proposed benchmark includes over 1,800 memory-augmented dialogues and defines structured mappings between emotional needs and supportive memory types. |