no code implementations • 29 Dec 2022 • Zhuo Chen, Jiaoyan Chen, Wen Zhang, Lingbing Guo, Yin Fang, Yufeng Huang, Yuxia Geng, Jeff Z. Pan, Wenting Song, Huajun Chen
As an important variant of entity alignment (EA), multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) with relevant images attached.
1 code implementation • Findings (ACL) 2022 • Lingbing Guo, Yuqiang Han, Qiang Zhang, Huajun Chen
Embedding-based methods have attracted increasing attention in recent entity alignment (EA) studies.
no code implementations • 18 Feb 2022 • Lingbing Guo, Qiang Zhang, Huajun Chen
Our experiments demonstrate DET has achieved superior performance compared to the respective state-of-the-art methods in dealing with molecules, networks and knowledge graphs with various sizes.
no code implementations • 21 Oct 2021 • Lingbing Guo, Zequn Sun, Mingyang Chen, Wei Hu, Qiang Zhang, Huajun Chen
Embedding-based entity alignment (EEA) has recently received great attention.
no code implementations • 1 Jan 2021 • Lingbing Guo, Zequn Sun, Mingyang Chen, Wei Hu, Huajun Chen
In this paper, we define a typical paradigm abstracted from the existing methods, and analyze how the representation discrepancy between two potentially-aligned entities is implicitly bounded by a predefined margin in the scoring function for embedding learning.
no code implementations • 16 Oct 2020 • Lingbing Guo, Weiqing Wang, Zequn Sun, Chenghao Liu, Wei Hu
Knowledge graph (KG) representation learning methods have achieved competitive performance in many KG-oriented tasks, among which the best ones are usually based on graph neural networks (GNNs), a powerful family of networks that learns the representation of an entity by aggregating the features of its neighbors and itself.
1 code implementation • 22 Apr 2020 • Zequn Sun, Jiacheng Huang, Wei Hu, Muchao Chen, Lingbing Guo, Yuzhong Qu
We refer to such contextualized representations of a relation as edge embeddings and interpret them as translations between entity embeddings.
1 code implementation • 6 Jun 2019 • Qingheng Zhang, Zequn Sun, Wei Hu, Muhao Chen, Lingbing Guo, Yuzhong Qu
Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs.
1 code implementation • 13 May 2019 • Lingbing Guo, Zequn Sun, Wei Hu
Moreover, triple-level learning is insufficient for the propagation of semantic information among entities, especially for the case of cross-KG embedding.
no code implementations • 6 Nov 2018 • Lingbing Guo, Zequn Sun, Ermei Cao, Wei Hu
We consider the problem of learning knowledge graph (KG) embeddings for entity alignment (EA).
1 code implementation • 30 Oct 2018 • Lingbing Guo, Qingheng Zhang, Weiyi Ge, Wei Hu, Yuzhong Qu
Knowledge graph (KG) completion aims to fill the missing facts in a KG, where a fact is represented as a triple in the form of $(subject, relation, object)$.