29 citations · 286 across the 54 of their papers we have counts for
56 papers
Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning
Yuxia Geng, Runkai Zhu, Jiaoyan Chen +7
Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL). However, due to the featur…
Large Language Models and Knowledge Graphs: Opportunities and Challenges
Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo +13
Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm. This inflection point marks a shift from explicit knowledge representation to a renew…
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
Zhuo Chen, Lingbing Guo, Yin Fang +6
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 assoc…
DeepOnto: A Python Package for Ontology Engineering with Deep Learning
Yuan He, Jiaoyan Chen, Hang Dong +4
Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need…
Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement
Hang Dong, Jiaoyan Chen, Yuan He +1
Mentions of new concepts appear regularly in texts and require automated approaches to harvest and place them into Knowledge Bases (KB), e.g., ontologies and taxonomies. Existing d…
Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
Lingbing Guo, Zhuo Chen, Jiaoyan Chen +3
Recent embedding-based methods have achieved great successes in exploiting entity alignment from knowledge graph (KG) embeddings of multiple modalities. In this paper, we study emb…
An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering
Nan Hu, Yike Wu, Guilin Qi +4
Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP). It is now the consensu…
Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment
Yangning Li, Jiaoyan Chen, Yinghui Li +3
Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering. Existing EA methods mostly focus on utilizing the graph structures and entity attri…
Knowledge-augmented Few-shot Visual Relation Detection
Tianyu Yu, Yangning Li, Jiaoyan Chen +8
Visual Relation Detection (VRD) aims to detect relationships between objects for image understanding. Most existing VRD methods rely on thousands of training samples of each relati…
Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking
Hang Dong, Jiaoyan Chen, Yuan He +2
Discovering entity mentions that are out of a Knowledge Base (KB) from texts plays a critical role in KB maintenance, but has not yet been fully explored. The current methods are m…
Language Model Analysis for Ontology Subsumption Inference
Yuan He, Jiaoyan Chen, Ernesto Jiménez-Ruiz +2
Investigating whether pre-trained language models (LMs) can function as knowledge bases (KBs) has raised wide research interests recently. However, existing works focus on simple,…
Dual Box Embeddings for the Description Logic EL++
Mathias Jackermeier, Jiaoyan Chen, Ian Horrocks
OWL ontologies, whose formal semantics are rooted in Description Logic (DL), have been widely used for knowledge representation. Similar to Knowledge Graphs (KGs), ontologies are o…
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…
Low-resource Personal Attribute Prediction from Conversation
Yinan Liu, Hu Chen, Wei Shen +1
Personal knowledge bases (PKBs) are crucial for a broad range of applications such as personalized recommendation and Web-based chatbots. A critical challenge to build PKBs is extr…
Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge
Yangning Li, Jiaoyan Chen, Yinghui Li +3
Contextual synonym knowledge is crucial for those similarity-oriented tasks whose core challenge lies in capturing semantic similarity between entities in their contexts, such as e…
Understanding Adverse Biological Effect Predictions Using Knowledge Graphs
Erik Bryhn Myklebust, Ernesto Jimenez-Ruiz, Jiaoyan Chen +2
Extrapolation of adverse biological (toxic) effects of chemicals is an important contribution to expand available hazard data in (eco)toxicology without the use of animals in labor…
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…
Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph
Shumin Deng, Chengming Wang, Zhoubo Li +11
Business Knowledge Graphs (KGs) are important to many enterprises today, providing factual knowledge and structured data that steer many products and make them more intelligent. De…
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…
Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes
Hang Dong, Víctor Suárez-Paniagua, Huayu Zhang +7
Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to…
Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching
Yuan He, Jiaoyan Chen, Hang Dong +3
Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the a…