Knowledge Router: Learning Disentangled Representations for Knowledge Graphs (2021.naacl-main)
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| Challenge: | Existing approaches to learning from relational patterns and structural information ignore the intrinsic complexity of KGs. |
| Approach: | They propose to learn latent properties of KG entities by using a neighborhood mechanism to disentangle the inner properties of each entity. |
| Outcome: | The proposed method significantly improves performance on key metrics on several benchmark datasets. |
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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. |
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AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding (2020.findings-emnlp)
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| Challenge: | Existing knowledge graphs are incomplete whether they are constructed manually or automatically, limiting the effectiveness when exploited for downstream applications. |
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| Challenge: | Existing knowledge graph embedding models embed entities and relations into latent vectors without leveraging rich information from relation structure. |
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| Challenge: | Existing models for knowledge-to-text generation use RDF triples or key-value pairs to generate a natural language description. |
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| Challenge: | Knowledge graph embedding (KGE) is a computational approach to learn continuous vector representations of relations and entities in knowledge graphs. |
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RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding (2021.eacl-main)
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| Challenge: | Existing methods for embedding entities and relations in knowledge graphs are heuristically motivated and theoretical understanding of such embeddables is underdeveloped. |
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Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation (2023.emnlp-main)
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| Challenge: | Existing approaches to learning on Knowledge Graphs (KGs) are not critical for learning on KGs. |
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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 . |
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LinkNBed: Multi-Graph Representation Learning with Entity Linkage (P18-1)
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| Challenge: | Knowledge graphs have emerged as an important model for studying complex multi-relational data. |
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