274 citations · 579 across the 35 of their papers we have counts for
35 papers
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…
What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings
Zequn Sun, Jiacheng Huang, Xiaozhou Xu +3
Joint representation learning over multi-sourced knowledge graphs (KGs) yields transferable and expressive embeddings that improve downstream tasks. Entity alignment (EA) is a crit…
Deep Active Alignment of Knowledge Graph Entities and Schemata
Jiacheng Huang, Zequn Sun, Qijin Chen +3
Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and cl…
EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs
Xiaobin Tian, Zequn Sun, Guangyao Li +1
Entity alignment is to find identical entities in different knowledge graphs (KGs) that refer to the same real-world object. Embedding-based entity alignment techniques have been d…
Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs
Yuanning Cui, Yuxin Wang, Zequn Sun +4
Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the dev…
Inductive Knowledge Graph Reasoning for Multi-batch Emerging Entities
Yuanning Cui, Yuxin Wang, Zequn Sun +4
Over the years, reasoning over knowledge graphs (KGs), which aims to infer new conclusions from known facts, has mostly focused on static KGs. The unceasing growth of knowledge in…