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洞察 - Knowledge Graph Embedding
Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval
SpherE, a novel knowledge graph embedding model, can expressively model one-to-many, many-to-one, and many-to-many relations, enabling efficient knowledge graph set retrieval.
Exploring Connected Subgraph Explanations for Knowledge Graph Completion
KGExplainer, a model-agnostic method, identifies connected subgraph explanations and distills an evaluator to assess them quantitatively, overcoming the limitations of existing KGE-based explanation methods that focus on exploring key paths or isolated edges as explanations.
Decentralized Attention Network for Generating Robust Entity Embeddings in Open-World Knowledge Graphs
The core message of this paper is that by distributing the relational information of an entity exclusively over its neighbors, the proposed Decentralized Attention Network (DAN) can generate high-quality embeddings for both known and unknown entities in knowledge graphs.
