150 citations · 375 across the 20 of their papers we have counts for
20 papers
Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer
Yin Fang, Zhuo Chen, Xiaohui Fan +1
Machine learning, notably deep learning, has significantly propelled molecular investigations within the biochemical sphere. Traditionally, modeling for such research has centered…
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…
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification
Juan Li, Ruoxu Wang, Ningyu Zhang +3
Relation classification aims to extract semantic relations between entity pairs from the sentences. However, most existing methods can only identify seen relation classes that occu…
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction
Haiyang Yu, Ningyu Zhang, Shumin Deng +3
Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings. However, people can gras…
FedE: Embedding Knowledge Graphs in Federated Setting
Mingyang Chen, Wen Zhang, Zonggang Yuan +2
Knowledge graphs (KGs) consisting of triples are always incomplete, so it's important to do Knowledge Graph Completion (KGC) by predicting missing triples. Multi-Source KG is a com…
The Devil is the Classifier: Investigating Long Tail Relation Classification with Decoupling Analysis
Haiyang Yu, Ningyu Zhang, Shumin Deng +3
Long-tailed relation classification is a challenging problem as the head classes may dominate the training phase, thereby leading to the deterioration of the tail performance. Exis…
Contrastive Triple Extraction with Generative Transformer
Hongbin Ye, Ningyu Zhang, Shumin Deng +4
Triple extraction is an essential task in information extraction for natural language processing and knowledge graph construction. In this paper, we revisit the end-to-end triple e…
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…
Neural Entity Summarization with Joint Encoding and Weak Supervision
Junyou Li, Gong Cheng, Qingxia Liu +4
In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts. Many applications require abridged versions of entity descriptions…
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 (…
Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection
Shumin Deng, Ningyu Zhang, Jiaojian Kang +3
Event detection (ED), a sub-task of event extraction, involves identifying triggers and categorizing event mentions. Existing methods primarily rely upon supervised learning and re…
Relation Adversarial Network for Low Resource Knowledge Graph Completion
Ningyu Zhang, Shumin Deng, Zhanlin Sun +3
Knowledge Graph Completion (KGC) has been proposed to improve Knowledge Graphs by filling in missing connections via link prediction or relation extraction. One of the main difficu…
Transfer Learning for Relation Extraction via Relation-Gated Adversarial Learning
Ningyu Zhang, Shumin Deng, Zhanlin Sun +3
Relation extraction aims to extract relational facts from sentences. Previous models mainly rely on manually labeled datasets, seed instances or human-crafted patterns, and distant…
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification
Shumin Deng, Ningyu Zhang, Zhanlin Sun +2
Text classification tends to be difficult when data are deficient or when it is required to adapt to unseen classes. In such challenging scenarios, recent studies have often used m…
Augmenting Transfer Learning with Semantic Reasoning
Freddy Lecue, Jiaoyan Chen, Jeff Z. Pan +1
Transfer learning aims at building robust prediction models by transferring knowledge gained from one problem to another. In the semantic Web, learning tasks are enhanced with sema…
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Wen Zhang, Bibek Paudel, Wei Zhang +2
Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions --- bi-di…
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…
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
Ningyu Zhang, Shumin Deng, Zhanlin Sun +3
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for…
Knowledge-based Transfer Learning Explanation
Jiaoyan Chen, Freddy Lecue, Jeff Z. Pan +2
Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation…
Learning from Ontology Streams with Semantic Concept Drift
Freddy Lecue, Jiaoyan Chen, Jeff Pan +1
Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and…