Search Results for author: Lingbing Guo

Found 19 papers, 13 papers with code

Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

2 code implementations8 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.

Entity Alignment Image Classification +4

Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs

1 code implementation13 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.

Entity Alignment Knowledge Graphs

Domain-Agnostic Molecular Generation with Chemical Feedback

1 code implementation26 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.

Language Modelling Molecular Docking +1

DSKG: A Deep Sequential Model for Knowledge Graph Completion

1 code implementation30 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 $(subject, relation, object)$.

Knowledge Graph Completion Relation

MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid

1 code implementation29 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)

Knowledge Graphs Multi-modal Entity Alignment

ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment

2 code implementations16 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.

Entity Alignment Knowledge Graphs

TransEdge: Translating Relation-contextualized Embeddings for Knowledge Graphs

1 code implementation22 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.

Entity Alignment Entity Embeddings +3

Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment

1 code implementation30 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)

Benchmarking Knowledge Graph Embeddings +2

Newton-Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems

1 code implementation24 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.

The Power of Noise: Toward a Unified Multi-modal Knowledge Graph Representation Framework

1 code implementation11 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).

Knowledge Graph Completion Misconceptions +3

Universal Multi-modal Entity Alignment via Iteratively Fusing Modality Similarity Paths

1 code implementation9 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.

Knowledge Graphs Multi-modal Entity Alignment

Recurrent Skipping Networks for Entity Alignment

no code implementations6 Nov 2018 Lingbing Guo, Zequn Sun, Ermei Cao, Wei Hu

We consider the problem of learning knowledge graph (KG) embeddings for entity alignment (EA).

Entity Alignment

Towards Principled Representation Learning for Entity Alignment

no code implementations1 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.

Entity Alignment Machine Translation +1

Decentralized Knowledge Graph Representation Learning

no code implementations16 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.

Entity Alignment Graph Representation Learning

Unleashing the Power of Transformer for Graphs

no code implementations18 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.

Knowledge Graphs

Revisit and Outstrip Entity Alignment: A Perspective of Generative Models

no code implementations24 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).

Entity Alignment Generative Adversarial Network

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