31 citations · 131 across the 30 of their papers we have counts for
12 papers · 1 filter
MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
Zhuo Chen, Jiaoyan Chen, Wen Zhang +8
Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images. However, current…
Tele-Knowledge Pre-training for Fault Analysis
Zhuo Chen, Wen Zhang, Yufeng Huang +14
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge no…
Relational Message Passing for Fully Inductive Knowledge Graph Completion
Yuxia Geng, Jiaoyan Chen, Jeff Z. Pan +4
In knowledge graph completion (KGC), predicting triples involving emerging entities and/or relations, which are unseen when the KG embeddings are learned, has become a critical cha…
Knowledge Graph Completion with Pre-trained Multimodal Transformer and Twins Negative Sampling
Yichi Zhang, Wen Zhang
Knowledge graphs (KGs) that modelings the world knowledge as structural triples are inevitably incomplete. Such problems still exist for multimodal knowledge graphs (MMKGs). Thus,…
Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph
Yufeng Huang, Zhuo Chen, Jiaoyan Chen +3
Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image. However, previous methods fa…
LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection
Zhuo Chen, Yufeng Huang, Jiaoyan Chen +5
Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge. Most existing methods exploit pre-tra…
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning
Zhuo Chen, Yufeng Huang, Jiaoyan Chen +5
Zero-shot learning (ZSL) aims to predict unseen classes whose samples have never appeared during training. One of the most effective and widely used semantic information for zero-s…
Disentangled Ontology Embedding for Zero-shot Learning
Yuxia Geng, Jiaoyan Chen, Wen Zhang +6
Knowledge Graph (KG) and its variant of ontology have been widely used for knowledge representation, and have shown to be quite effective in augmenting Zero-shot Learning (ZSL). Ho…
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting
Mingyang Chen, Wen Zhang, Zhen Yao +4
We study the knowledge extrapolation problem to embed new components (i.e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting. In this…
NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
Wen Zhang, Xiangnan Chen, Zhen Yao +10
NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs. It implements three different series of Knowledge Graph Embedding (KGE) met…
Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective
Wen Zhang, Jiaoyan Chen, Juan Li +3
Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and industry. Conventional KG reasoning based on symbolic logic is deterministic, with reasoning re…
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
Ningyu Zhang, Xin Xu, Liankuan Tao +19
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base pop…