Papers with KG embeddings
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. |
Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction (2024.findings-emnlp)
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| Challenge: | Knowledge graph embeddings (KGE) models are often used to predict missing links for knowledge graphs (KGs) however, multiple KG embedds can give conflicting predictions for unseen queries. |
| Approach: | They define predictive multiplicity in link prediction and introduce evaluation metrics to measure it using commonly used benchmark datasets. |
| Outcome: | The proposed methods significantly mitigat conflicts by 66% to 78% in link prediction. |
Constraint-based Multi-hop Question Answering with Knowledge Graph (2022.naacl-industry)
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| Challenge: | Recent work addresses multi-hop KGQA, which requires reasoning across numerous edges of the KG. |
| Approach: | They propose to use KG embeddings to reduce KG sparsity by performing missing link prediction. |
| Outcome: | Empirical results show that the proposed method produces state-of-the-art results on three KGQA datasets. |
Conformalized Answer Set Prediction for Knowledge Graph Embedding (2025.naacl-long)
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| Challenge: | Knowledge graph embeddings (KGE) map entities and predicates into numerical vectors, providing non-classical reasoning capabilities based on similarities and analogies between entities and relations. |
| Approach: | They propose to use knowledge graph embeddings to provide non-classical reasoning capabilities by exploiting similarities and analogies between entities and relations. |
| Outcome: | The proposed model can generate answer sets with probabilistic guarantees on four benchmark datasets and is scaled well with respect to the difficulty of the query. |
Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference (2022.coling-1)
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| Challenge: | Existing knowledge graphs are incomplete and therefore lack interpretability. |
| Approach: | They propose a closed-loop neural-symbolic learning framework EngineKG to address the natural incompleteness of knowledge graphs. |
| Outcome: | The proposed model outperforms baselines on link prediction tasks on four real-world datasets. |
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 . |
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (P19-1)
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| Challenge: | Existing knowledge graphs (KGs) are incomplete or partial information, in the form of missing relations between entities, which gives rise to the task of knowledge base completion (also known as relation prediction). |
| Approach: | They propose to capture both entity and relation features in any given neighborhood and encapsulate relation clusters and multi-hop relations in their attention-based model. |
| Outcome: | The proposed model captures both entity and relation features in any given neighborhood and also encapsulates relation clusters and multi-hop relations. |
Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs (2023.findings-emnlp)
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| Challenge: | Complex Query Answering (CQA) is a challenge task of Knowledge Graphs due to incompleteness of KGs. |
| Approach: | They propose a query embedding approach that decouples the training for simple and complex queries. |
| Outcome: | The proposed approach decouples training for simple and complex queries and achieves state-of-the-art performance over three public benchmarks. |