2 code implementations • 15 Apr 2024 • Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Binbin Hu, Ziqi Liu, Huajun Chen, Wen Zhang
To overcome their inherent incompleteness, multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given MMKGs, leveraging both structural information from the triples and multi-modal information of the entities.
1 code implementation • 11 Mar 2024 • Zhuo Chen, Yin Fang, Yichi Zhang, Lingbing Guo, Jiaoyan Chen, Huajun Chen, Wen Zhang
In this work, to evaluate models' ability to accurately embed entities within MMKGs, we focus on two widely researched tasks: Multi-modal Knowledge Graph Completion (MKGC) and Multi-modal Entity Alignment (MMEA).
2 code implementations • 16 Feb 2024 • Yangyifei Luo, Zhuo Chen, Lingbing Guo, Qian Li, Wenxuan Zeng, Zhixin Cai, JianXin Li
Entity alignment (EA) aims to identify entities across different knowledge graphs that represent the same real-world objects.
6 code implementations • 8 Feb 2024 • Zhuo Chen, Yichi Zhang, Yin Fang, Yuxia Geng, Lingbing Guo, Xiang Chen, Qian Li, Wen Zhang, Jiaoyan Chen, Yushan Zhu, Jiaqi Li, Xiaoze Liu, Jeff Z. Pan, Ningyu Zhang, Huajun Chen
In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm.
1 code implementation • 10 Oct 2023 • Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Wen Zhang, Huajun Chen
In this paper, we explore methods to incorporate structural information into the LLMs, with the overarching goal of facilitating structure-aware reasoning.
1 code implementation • 9 Oct 2023 • Bolin Zhu, Xiaoze Liu, Xin Mao, Zhuo Chen, Lingbing Guo, Tao Gui, Qi Zhang
The objective of Entity Alignment (EA) is to identify equivalent entity pairs from multiple Knowledge Graphs (KGs) and create a more comprehensive and unified KG.
1 code implementation • 30 Jul 2023 • Zhuo Chen, Lingbing Guo, Yin Fang, Yichi Zhang, Jiaoyan Chen, Jeff Z. Pan, Yangning Li, Huajun Chen, Wen Zhang
As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual information.
Ranked #1 on Multi-modal Entity Alignment on UMVM-oea-d-w-v2 (using extra training data)
1 code implementation • 24 May 2023 • Lingbing Guo, Weiqing Wang, Zhuo Chen, Ningyu Zhang, Zequn Sun, Yixuan Lai, Qiang Zhang, Huajun Chen
Reasoning system dynamics is one of the most important analytical approaches for many scientific studies.
no code implementations • 24 May 2023 • Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Yin Fang, Wen Zhang, Huajun Chen
We then reveal that their incomplete objective limits the capacity on both entity alignment and entity synthesis (i. e., generating new entities).
1 code implementation • 26 Jan 2023 • Yin Fang, Ningyu Zhang, Zhuo Chen, Lingbing Guo, Xiaohui Fan, Huajun Chen
The generation of molecules with desired properties has become increasingly popular, revolutionizing the way scientists design molecular structures and providing valuable support for chemical and drug design.
1 code implementation • 29 Dec 2022 • Zhuo Chen, Jiaoyan Chen, Wen Zhang, Lingbing Guo, Yin Fang, Yufeng Huang, Yichi Zhang, Yuxia Geng, Jeff Z. Pan, Wenting Song, Huajun Chen
Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images.
Ranked #1 on Entity Alignment on FBYG15k (using extra training data)
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, Zhuo Chen, Jiaoyan Chen, Yichi Zhang, Zequn Sun, Zhongpo Bo, Yin Fang, Xiaoze Liu, Huajun Chen, Wen Zhang
DAN leverages neighbor context as the query vector to score the neighbors of an entity, thereby distributing the entity semantics only among its neighbor embeddings.
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 $(subject, relation, object)$.