370 citations · 1.8k across the 232 of their papers we have counts for
27 papers · 1 filter
Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules
Wen Zhang, Shumin Deng, Mingyang Chen +5
Knowledge Graphs (KGs), representing facts as triples, have been widely adopted in many applications. Reasoning tasks such as link prediction and rule induction are important for t…
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training
Ganqiang Ye, Wen Zhang, Zhen Bi +3
Representation learning models for Knowledge Graphs (KG) have proven to be effective in encoding structural information and performing reasoning over KGs. In this paper, we propose…
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…
LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training
Shumin Deng, Jiacheng Yang, Hongbin Ye +6
Natural language generation from structured data mainly focuses on surface-level descriptions, suffering from uncontrollable content selection and low fidelity. Previous works leve…
Molecular Contrastive Learning with Chemical Element Knowledge Graph
Yin Fang, Qiang Zhang, Haihong Yang +7
Molecular representation learning contributes to multiple downstream tasks such as molecular property prediction and drug design. To properly represent molecules, graph contrastive…
Principled Representation Learning for Entity Alignment
Lingbing Guo, Zequn Sun, Mingyang Chen +3
Embedding-based entity alignment (EEA) has recently received great attention. Despite significant performance improvement, few efforts have been paid to facilitate understanding of…
Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding
Mingyang Chen, Wen Zhang, Yushan Zhu +4
Knowledge graphs (KGs) consisting of a large number of triples have become widespread recently, and many knowledge graph embedding (KGE) methods are proposed to embed entities and…
Learning to Ask for Data-Efficient Event Argument Extraction
Hongbin Ye, Ningyu Zhang, Zhen Bi +5
Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles. In this study, we cast EAE as a question-based cloze task and e…
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting
Xiang Chen, Lei Li, Shumin Deng +6
Most NER methods rely on extensive labeled data for model training, which struggles in the low-resource scenarios with limited training data. Existing dominant approaches usually s…
Knowledge Perceived Multi-modal Pretraining in E-commerce
Yushan Zhu, Huaixiao Tou, Wen Zhang +4
In this paper, we address multi-modal pretraining of product data in the field of E-commerce. Current multi-modal pretraining methods proposed for image and text modalities lack ro…
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
Ningyu Zhang, Luoqiu Li, Xiang Chen +5
Large-scale pre-trained language models have contributed significantly to natural language processing by demonstrating remarkable abilities as few-shot learners. However, their eff…
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.…
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Ningyu Zhang, Mosha Chen, Zhen Bi +20
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice. With the development of biomedical langua…
Document-level Relation Extraction as Semantic Segmentation
Ningyu Zhang, Xiang Chen, Xin Xie +6
Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the en…
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
Ningyu Zhang, Qianghuai Jia, Shumin Deng +8
Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search. Prior conceptual graph construction approaches typically extract high-…
MLBiNet: A Cross-Sentence Collective Event Detection Network
Dongfang Lou, Zhilin Liao, Shumin Deng +2
We consider the problem of collectively detecting multiple events, particularly in cross-sentence settings. The key to dealing with the problem is to encode semantic information an…
Billion-scale Pre-trained E-commerce Product Knowledge Graph Model
Wen Zhang, Chi-Man Wong, Ganqiang Ye +3
In recent years, knowledge graphs have been widely applied to organize data in a uniform way and enhance many tasks that require knowledge, for example, online shopping which has g…
Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph
Chi-Man Wong, Fan Feng, Wen Zhang +7
Conversational Recommender Systems (CRSs) in E-commerce platforms aim to recommend items to users via multiple conversational interactions. Click-through rate (CTR) prediction mode…
Interventional Aspect-Based Sentiment Analysis
Zhen Bi, Ningyu Zhang, Ganqiang Ye +3
Recent neural-based aspect-based sentiment analysis approaches, though achieving promising improvement on benchmark datasets, have reported suffering from poor robustness when enco…
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
Xiang Chen, Ningyu Zhang, Xin Xie +6
Recently, prompt-tuning has achieved promising results for specific few-shot classification tasks. The core idea of prompt-tuning is to insert text pieces (i.e., templates) into th…
Disentangled Contrastive Learning for Learning Robust Textual Representations
Xiang Chen, Xin Xie, Zhen Bi +4
Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-o…
Text-guided Legal Knowledge Graph Reasoning
Luoqiu Li, Zhen Bi, Hongbin Ye +3
Recent years have witnessed the prosperity of legal artificial intelligence with the development of technologies. In this paper, we propose a novel legal application of legal provi…
Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction
Luoqiu Li, Xiang Chen, Zhen Bi +6
Recent neural-based relation extraction approaches, though achieving promising improvement on benchmark datasets, have reported their vulnerability towards adversarial attacks. Thu…