274 citations · 493 across the 14 of their papers we have counts for
14 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…
Newton-Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems
Lingbing Guo, Weiqing Wang, Zhuo Chen +5
Reasoning system dynamics is one of the most important analytical approaches for many scientific studies. With the initial state of a system as input, the recent graph neural netwo…
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…
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…
Distributed Representations of Entities in Open-World Knowledge Graphs
Lingbing Guo, Zhuo Chen, Jiaoyan Chen +7
Graph neural network (GNN)-based methods have demonstrated remarkable performance in various knowledge graph (KG) tasks. However, most existing approaches rely on observing all ent…
TransEdge: Translating Relation-contextualized Embeddings for Knowledge Graphs
Zequn Sun, Jiacheng Huang, Wei Hu +3
Learning knowledge graph (KG) embeddings has received increasing attention in recent years. Most embedding models in literature interpret relations as linear or bilinear mapping fu…
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…
Multi-view Knowledge Graph Embedding for Entity Alignment
Qingheng Zhang, Zequn Sun, Wei Hu +3
We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorpora…
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
Lingbing Guo, Zequn Sun, Wei Hu
We study the problem of knowledge graph (KG) embedding. A widely-established assumption to this problem is that similar entities are likely to have similar relational roles. Howeve…
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…
Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
Zequn Sun, Wei Hu, Chengkai Li
Entity alignment is the task of finding entities in two knowledge bases (KBs) that represent the same real-world object. When facing KBs in different natural languages, conventiona…