
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.


coremsg

expressive-and-interpretable-knowledge-graph-embedding-for-set-retrieval


Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval


title_rewrite


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.


exploring-connected-subgraph-explanations-for-knowledge-graph-completion


Exploring Connected Subgraph Explanations for Knowledge Graph Completion



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.


decentralized-attention-network-for-generating-robust-entity-embeddings-in-open-world-knowledge-graphs


Decentralized Attention Network for Generating Robust Entity Embeddings in Open-World Knowledge Graphs
