31 citations · 104 across the 20 of their papers we have counts for
20 papers
Untargeted Adversarial Attack on Knowledge Graph Embeddings
Tianzhe Zhao, Jiaoyan Chen, Yanchi Ru +3
Knowledge graph embedding (KGE) methods have achieved great success in handling various knowledge graph (KG) downstream tasks. However, KGE methods may learn biased representations…
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Zhuo Chen, Yichi Zhang, Yin Fang +12
Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the semantic web community's exploration into multi-modal dimensions unlocking new avenues for…
Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model
Yuxia Geng, Jiaoyan Chen, Yuhang Zeng +5
Both graph structures and textual information play a critical role in Knowledge Graph Completion (KGC). With the success of Pre-trained Language Models (PLMs) such as BERT, they ha…
Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer
Wen Zhang, Yushan Zhu, Mingyang Chen +5
Knowledge graphs (KG) are essential background knowledge providers in many tasks. When designing models for KG-related tasks, one of the key tasks is to devise the Knowledge Repres…
Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs
Mingyang Chen, Wen Zhang, Yuxia Geng +3
Knowledge graphs (KGs) have become valuable knowledge resources in various applications, and knowledge graph embedding (KGE) methods have garnered increasing attention in recent ye…
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…
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…
Contextual Semantic Embeddings for Ontology Subsumption Prediction
Jiaoyan Chen, Yuan He, Yuxia Geng +3
Automating ontology construction and curation is an important but challenging task in knowledge engineering and artificial intelligence. Prediction by machine learning techniques s…
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey
Jiaoyan Chen, Yuxia Geng, Zhuo Chen +5
Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision. As sufficient labeled train…
Zero-shot Visual Question Answering using Knowledge Graph
Zhuo Chen, Jiaoyan Chen, Yuxia Geng +3
Incorporating external knowledge to Visual Question Answering (VQA) has become a vital practical need. Existing methods mostly adopt pipeline approaches with different components f…
Benchmarking Knowledge-driven Zero-shot Learning
Yuxia Geng, Jiaoyan Chen, Xiang Zhuang +5
External knowledge (a.k.a. side information) plays a critical role in zero-shot learning (ZSL) which aims to predict with unseen classes that have never appeared in training data.…
Knowledge-aware Zero-Shot Learning: Survey and Perspective
Jiaoyan Chen, Yuxia Geng, Zhuo Chen +3
Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a.k.a. side information) has been widely investigat…
OntoZSL: Ontology-enhanced Zero-shot Learning
Yuxia Geng, Jiaoyan Chen, Zhuo Chen +5
Zero-shot Learning (ZSL), which aims to predict for those classes that have never appeared in the training data, has arisen hot research interests. The key of implementing ZSL is t…
Ontology-guided Semantic Composition for Zero-Shot Learning
Jiaoyan Chen, Freddy Lecue, Yuxia Geng +2
Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relatio…
Generative Adversarial Zero-shot Learning via Knowledge Graphs
Yuxia Geng, Jiaoyan Chen, Zhuo Chen +4
Zero-shot learning (ZSL) is to handle the prediction of those unseen classes that have no labeled training data. Recently, generative methods like Generative Adversarial Networks (…
Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract]
Yuxia Geng, Jiaoyan Chen, Ernesto Jimenez-Ruiz +1
Transfer learning which aims at utilizing knowledge learned from one problem (source domain) to solve another different but related problem (target domain) has attracted wide resea…