106 citations · 200 across the 7 of their papers we have counts for
7 papers · 1 filter
Joint Pre-training and Local Re-training: Transferable Representation Learning on Multi-source Knowledge Graphs
Zequn Sun, Jiacheng Huang, Jinghao Lin +3
In this paper, we present the ``joint pre-training and local re-training'' framework for learning and applying multi-source knowledge graph (KG) embeddings. We are motivated by the…
I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning
Yang Liu, Zequn Sun, Guangyao Li +1
Knowledge graph (KG) embedding seeks to learn vector representations for entities and relations. Conventional models reason over graph structures, but they suffer from the issues o…
Knowledge Association with Hyperbolic Knowledge Graph Embeddings
Zequn Sun, Muhao Chen, Wei Hu +3
Capturing associations for knowledge graphs (KGs) through entity alignment, entity type inference and other related tasks benefits NLP applications with comprehensive knowledge rep…
A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
Zequn Sun, Qingheng Zhang, Wei Hu +4
Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the same real-world object. Recent advancement in KG embedding impels the advent of embedd…
Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
Zequn Sun, Chengming Wang, Wei Hu +4
Graph neural networks (GNNs) have emerged as a powerful paradigm for embedding-based entity alignment due to their capability of identifying isomorphic subgraphs. However, in real…
Recurrent Skipping Networks for Entity Alignment
Lingbing Guo, Zequn Sun, Ermei Cao +1
We consider the problem of learning knowledge graph (KG) embeddings for entity alignment (EA). Current methods use the embedding models mainly focusing on triple-level learning, wh…