Search Results for author: Huajun Chen

Found 78 papers, 41 papers with code

Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting

1 code implementation10 May 2022 Mingyang Chen, Wen Zhang, Zhen Yao, Xiangnan Chen, Mengxiao Ding, Fei Huang, Huajun Chen

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.

Knowledge Graphs Link Prediction +1

Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction

1 code implementation7 May 2022 Xiang Chen, Ningyu Zhang, Lei LI, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen

To deal with these issues, we propose a novel Hierarchical Visual Prefix fusion NeTwork (HVPNeT) for visual-enhanced entity and relation extraction, aiming to achieve more effective and robust performance.

Named Entity Recognition Relation Extraction

Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning

2 code implementations4 May 2022 Xiang Chen, Lei LI, Ningyu Zhang, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen

Note that the previous parametric learning paradigm can be viewed as memorization regarding training data as a book and inference as the close-book test.

Decision Making Few-Shot Learning +1

Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion

1 code implementation4 May 2022 Xiang Chen, Ningyu Zhang, Lei LI, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen

Since most MKGs are far from complete, extensive knowledge graph completion studies have been proposed focusing on the multimodal entity, relation extraction and link prediction.

Information Retrieval Knowledge Graph Completion +4

Contrastive Demonstration Tuning for Pre-trained Language Models

1 code implementation9 Apr 2022 Xiaozhuan Liang, Ningyu Zhang, Siyuan Cheng, Zhen Bi, Zhenru Zhang, Chuanqi Tan, Songfang Huang, Fei Huang, Huajun Chen

Pretrained language models can be effectively stimulated by textual prompts or demonstrations, especially in low-data scenarios.

Pretrained Language Models

PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application

no code implementations2 Mar 2022 Wen Zhang, Chi-Man Wong, Ganqinag Ye, Bo Wen, Hongting Zhou, Wei zhang, Huajun Chen

On the one hand, it could provide item knowledge services in a uniform way with service vectors for embedding-based and item-knowledge-related task models without accessing triple data.

Knowledge Graphs Sequential Recommendation

Unleashing the Power of Transformer for Graphs

no code implementations18 Feb 2022 Lingbing Guo, Qiang Zhang, Huajun Chen

Our experiments demonstrate DET has achieved superior performance compared to the respective state-of-the-art methods in dealing with molecules, networks and knowledge graphs with various sizes.

Knowledge Graphs

Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer

no code implementations17 Feb 2022 Yin Fang, Qiang Zhang, Zhuo Chen, Xiaohui Fan, Huajun Chen

Machine learning, especially deep learning, has greatly advanced molecular studies in the biochemical domain.

Molecular Property Prediction

Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective

1 code implementation16 Feb 2022 Shumin Deng, Ningyu Zhang, Hui Chen, Feiyu Xiong, Jeff Z. Pan, Huajun Chen

Knowledge Extraction (KE) which aims to extract structural information from unstructured texts often suffers from data scarcity and emerging unseen types, i. e., low-resource scenarios.

Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective

no code implementations15 Feb 2022 Wen Zhang, Jiaoyan Chen, Juan Li, Zezhong Xu, Jeff Z. Pan, Huajun Chen

Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and industry.

Prompt-Guided Injection of Conformation to Pre-trained Protein Model

no code implementations7 Feb 2022 Qiang Zhang, Zeyuan Wang, Yuqiang Han, Haoran Yu, Xurui Jin, Huajun Chen

To incorporate conformational knowledge to PTPMs, we propose an interaction-conformation prompt (IC prompt) that is learned through back-propagation with the protein-protein interaction task.

Language Modelling Masked Language Modeling +1

Ontology-enhanced Prompt-tuning for Few-shot Learning

no code implementations27 Jan 2022 Hongbin Ye, Ningyu Zhang, Shumin Deng, Xiang Chen, Hui Chen, Feiyu Xiong, Xi Chen, Huajun Chen

Specifically, we develop the ontology transformation based on the external knowledge graph to address the knowledge missing issue, which fulfills and converts structure knowledge to text.

Event Extraction Few-Shot Learning +2

OntoProtein: Protein Pretraining With Gene Ontology Embedding

1 code implementation ICLR 2022 Ningyu Zhang, Zhen Bi, Xiaozhuan Liang, Siyuan Cheng, Haosen Hong, Shumin Deng, Jiazhang Lian, Qiang Zhang, Huajun Chen

We construct a novel large-scale knowledge graph that consists of GO and its related proteins, and gene annotation texts or protein sequences describe all nodes in the graph.

Contrastive Learning Knowledge Graphs +1

Kformer: Knowledge Injection in Transformer Feed-Forward Layers

1 code implementation15 Jan 2022 Yunzhi Yao, Shaohan Huang, Ningyu Zhang, Li Dong, Furu Wei, Huajun Chen

Knowledge-Enhanced Model have developed a diverse set of techniques for knowledge integration on different knowledge sources.

Language Modelling Question Answering

Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings

1 code implementation14 Jan 2022 Ningyu Zhang, Xin Xie, Xiang Chen, Shumin Deng, Chuanqi Tan, Fei Huang, Xu Cheng, Huajun Chen

Previous knowledge graph embedding approaches usually map entities to representations and utilize score functions to predict the target entities, yet they struggle to reason rare or emerging unseen entities.

Knowledge Graph Embedding Knowledge Graph Embeddings +1

DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population

2 code implementations10 Jan 2022 Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Xin Xie, Xiang Chen, Zhoubo Li, Lei LI, Xiaozhuan Liang, Yunzhi Yao, Shumin Deng, Wen Zhang, Zhenru Zhang, Chuanqi Tan, Fei Huang, Guozhou Zheng, Huajun Chen

We present a new open-source and extensible knowledge extraction toolkit, called DeepKE (Deep learning based Knowledge Extraction), supporting standard fully supervised, low-resource few-shot and document-level scenarios.

Attribute Type Extraction Cross-Domain Named Entity Recognition +2

Low-resource Learning with Knowledge Graphs: A Comprehensive Survey

no code implementations18 Dec 2021 Jiaoyan Chen, Yuxia Geng, Zhuo Chen, Jeff Z. Pan, Yuan He, Wen Zhang, Ian Horrocks, Huajun Chen

We first introduced the KGs used in ZSL and FSL studies as well as the existing and potential KG construction solutions, and then systematically categorized and summarized KG-aware ZSL and FSL methods, dividing them into different paradigms such as the mapping-based, the data augmentation, the propagation-based and the optimization-based.

Data Augmentation Few-Shot Learning +4

Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules

no code implementations16 Dec 2021 Wen Zhang, Shumin Deng, Mingyang Chen, Liang Wang, Qiang Chen, Feiyu Xiong, Xiangwen Liu, Huajun Chen

We first identity three important desiderata for e-commerce KG systems: 1) attentive reasoning, reasoning over a few target relations of more concerns instead of all; 2) explanation, providing explanations for a prediction to help both users and business operators understand why the prediction is made; 3) transferable rules, generating reusable rules to accelerate the deployment of a KG to new systems.

Entity Embeddings Graph Attention +4

Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training

no code implementations8 Dec 2021 Ganqiang Ye, Wen Zhang, Zhen Bi, Chi Man Wong, Chen Hui, Huajun Chen

Representation learning models for Knowledge Graphs (KG) have proven to be effective in encoding structural information and performing reasoning over KGs.

Entity Alignment Graph Representation Learning +3

Molecular Contrastive Learning with Chemical Element Knowledge Graph

1 code implementation1 Dec 2021 Yin Fang, Qiang Zhang, Haihong Yang, Xiang Zhuang, Shumin Deng, Wen Zhang, Ming Qin, Zhuo Chen, Xiaohui Fan, Huajun Chen

To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning.

Contrastive Learning Molecular Property Prediction +1

Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding

1 code implementation27 Oct 2021 Mingyang Chen, Wen Zhang, Yushan Zhu, Hongting Zhou, Zonggang Yuan, Changliang Xu, Huajun Chen

In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce entity embeddings.

Entity Embeddings Inductive Relation Prediction +6

Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs

2 code implementations4 Oct 2021 Fang Wu, Qiang Zhang, Dragomir Radev, Jiyu Cui, Wen Zhang, Huabin Xing, Ningyu Zhang, Huajun Chen

To address such issues, we formulate heterogeneous molecular graphs (HMGs), and introduce Molformer to exploit both molecular motifs and 3D geometry.

graph construction Translation

Learning to Ask for Data-Efficient Event Argument Extraction

no code implementations1 Oct 2021 Hongbin Ye, Ningyu Zhang, Zhen Bi, Shumin Deng, Chuanqi Tan, Hui Chen, Fei Huang, Huajun Chen

Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles.

Explaining Knowledge Graph Embedding via Latent Rule Learning

no code implementations29 Sep 2021 Wen Zhang, Mingyang Chen, Zezhong Xu, Yushan Zhu, Huajun Chen

KGExplainer is a multi-hop reasoner learning latent rules for link prediction and is encouraged to behave similarly to KGEs during prediction through knowledge distillation.

Knowledge Distillation Knowledge Graph Embedding +3

LightNER: A Lightweight Generative Framework with Prompt-guided Attention for Low-resource NER

1 code implementation31 Aug 2021 Xiang Chen, Ningyu Zhang, Lei LI, Xin Xie, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen

Most existing NER methods rely on extensive labeled data for model training, which struggles in the low-resource scenarios with limited training data.

Few-Shot Learning Language Modelling +2

Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners

1 code implementation ICLR 2022 Ningyu Zhang, Luoqiu Li, Xiang Chen, Shumin Deng, Zhen Bi, Chuanqi Tan, Fei Huang, Huajun Chen

Large-scale pre-trained language models have contributed significantly to natural language processing by demonstrating remarkable abilities as few-shot learners.

Language Modelling

Document-level Relation Extraction as Semantic Segmentation

2 code implementations7 Jun 2021 Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Chuanqi Tan, Mosha Chen, Fei Huang, Luo Si, Huajun Chen

Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples.

Relation Extraction Semantic Segmentation

OntoED: Low-resource Event Detection with Ontology Embedding

1 code implementation ACL 2021 Shumin Deng, Ningyu Zhang, Luoqiu Li, Hui Chen, Huaixiao Tou, Mosha Chen, Fei Huang, Huajun Chen

Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types.

Event Detection

Billion-scale Pre-trained E-commerce Product Knowledge Graph Model

no code implementations2 May 2021 Wen Zhang, Chi-Man Wong, Ganqiang Ye, Bo Wen, Wei zhang, Huajun Chen

As a backbone for online shopping platforms, we built a billion-scale e-commerce product knowledge graph for various item knowledge services such as item recommendation.

Knowledge Graphs

Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph

no code implementations30 Apr 2021 Chi-Man Wong, Fan Feng, Wen Zhang, Chi-Man Vong, Hui Chen, Yichi Zhang, Peng He, Huan Chen, Kun Zhao, Huajun Chen

We first construct a billion-scale conversation knowledge graph (CKG) from information about users, items and conversations, and then pretrain CKG by introducing knowledge graph embedding method and graph convolution network to encode semantic and structural information respectively. To make the CTR prediction model sensible of current state of users and the relationship between dialogues and items, we introduce user-state and dialogue-interaction representations based on pre-trained CKG and propose K-DCN. In K-DCN, we fuse the user-state representation, dialogue-interaction representation and other normal feature representations via deep cross network, which will give the rank of candidate items to be recommended. We experimentally prove that our proposal significantly outperforms baselines and show it's real application in Alime.

Click-Through Rate Prediction Knowledge Graph Embedding +1

Interventional Aspect-Based Sentiment Analysis

no code implementations20 Apr 2021 Zhen Bi, Ningyu Zhang, Ganqiang Ye, Haiyang Yu, Xi Chen, Huajun Chen

Recent neural-based aspect-based sentiment analysis approaches, though achieving promising improvement on benchmark datasets, have reported suffering from poor robustness when encountering confounder such as non-target aspects.

Aspect-Based Sentiment Analysis

KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction

1 code implementation15 Apr 2021 Xiang Chen, Ningyu Zhang, Xin Xie, Shumin Deng, Yunzhi Yao, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen

To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt).

Ranked #3 on Dialog Relation Extraction on DialogRE (F1 (v1) metric)

Dialog Relation Extraction Language Modelling +2

Disentangled Contrastive Learning for Learning Robust Textual Representations

1 code implementation11 Apr 2021 Xiang Chen, Xin Xie, Zhen Bi, Hongbin Ye, Shumin Deng, Ningyu Zhang, Huajun Chen

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-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs.

Contrastive Learning

Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction

2 code implementations1 Apr 2021 Luoqiu Li, Xiang Chen, Zhen Bi, Xin Xie, Shumin Deng, Ningyu Zhang, Chuanqi Tan, Mosha Chen, Huajun Chen

Recent neural-based relation extraction approaches, though achieving promising improvement on benchmark datasets, have reported their vulnerability towards adversarial attacks.

Relation Extraction

Knowledge-aware Contrastive Molecular Graph Learning

no code implementations24 Mar 2021 Yin Fang, Haihong Yang, Xiang Zhuang, Xin Shao, Xiaohui Fan, Huajun Chen

Leveraging domain knowledge including fingerprints and functional groups in molecular representation learning is crucial for chemical property prediction and drug discovery.

Contrastive Learning Drug Discovery +3

Knowledge-aware Zero-Shot Learning: Survey and Perspective

1 code implementation26 Feb 2021 Jiaoyan Chen, Yuxia Geng, Zhuo Chen, Ian Horrocks, Jeff Z. Pan, Huajun Chen

Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a. k. a.

Zero-Shot Learning

OntoZSL: Ontology-enhanced Zero-shot Learning

1 code implementation15 Feb 2021 Yuxia Geng, Jiaoyan Chen, Zhuo Chen, Jeff Z. Pan, Zhiquan Ye, Zonggang Yuan, Yantao Jia, Huajun Chen

The key of implementing ZSL is to leverage the prior knowledge of classes which builds the semantic relationship between classes and enables the transfer of the learned models (e. g., features) from training classes (i. e., seen classes) to unseen classes.

Image Classification Knowledge Graph Completion +2

Towards Robust Textual Representations with Disentangled Contrastive Learning

no code implementations1 Jan 2021 Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Yantao Jia, Zonggang Yuan, Huajun Chen

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-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs.

Contrastive Learning

Towards Principled Representation Learning for Entity Alignment

no code implementations1 Jan 2021 Lingbing Guo, Zequn Sun, Mingyang Chen, Wei Hu, Huajun Chen

In this paper, we define a typical paradigm abstracted from the existing methods, and analyze how the representation discrepancy between two potentially-aligned entities is implicitly bounded by a predefined margin in the scoring function for embedding learning.

Entity Alignment Machine Translation +1

Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction

no code implementations COLING 2020 Haiyang Yu, Ningyu Zhang, Shumin Deng, Hongbin Ye, Wei zhang, Huajun Chen

Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings.

FedE: Embedding Knowledge Graphs in Federated Setting

2 code implementations24 Oct 2020 Mingyang Chen, Wen Zhang, Zonggang Yuan, Yantao Jia, Huajun Chen

Knowledge graphs (KGs) consisting of triples are always incomplete, so it's important to do Knowledge Graph Completion (KGC) by predicting missing triples.

Knowledge Graph Completion Knowledge Graph Embedding +1

The Devil is the Classifier: Investigating Long Tail Relation Classification with Decoupling Analysis

1 code implementation15 Sep 2020 Haiyang Yu, Ningyu Zhang, Shumin Deng, Zonggang Yuan, Yantao Jia, Huajun Chen

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.

General Classification Relation Classification

On Robustness and Bias Analysis of BERT-based Relation Extraction

1 code implementation14 Sep 2020 Luoqiu Li, Xiang Chen, Hongbin Ye, Zhen Bi, Shumin Deng, Ningyu Zhang, Huajun Chen

Fine-tuning pre-trained models have achieved impressive performance on standard natural language processing benchmarks.

Relation Extraction

DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning

no code implementations13 Sep 2020 Yushan Zhu, Wen Zhang, Mingyang Chen, Hui Chen, Xu Cheng, Wei zhang, Huajun Chen

In DualDE, we propose a soft label evaluation mechanism to adaptively assign different soft label and hard label weights to different triples, and a two-stage distillation approach to improve the student's acceptance of the teacher.

Knowledge Distillation Knowledge Graph Embedding +2

Ontology-guided Semantic Composition for Zero-Shot Learning

1 code implementation30 Jun 2020 Jiaoyan Chen, Freddy Lecue, Yuxia Geng, Jeff Z. Pan, Huajun Chen

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 relationship with some side information.

Image Classification Question Answering +3

Neural Entity Summarization with Joint Encoding and Weak Supervision

1 code implementation1 May 2020 Junyou Li, Gong Cheng, Qingxia Liu, Wen Zhang, Evgeny Kharlamov, Kalpa Gunaratna, Huajun Chen

In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts.

Generative Adversarial Zero-shot Learning via Knowledge Graphs

no code implementations7 Apr 2020 Yuxia Geng, Jiaoyan Chen, Zhuo Chen, Zhiquan Ye, Zonggang Yuan, Yantao Jia, Huajun Chen

However, the side information of classes used now is limited to text descriptions and attribute annotations, which are in short of semantics of the classes.

Image Classification Knowledge Graphs +1

Relation Adversarial Network for Low Resource Knowledge Graph Completion

no code implementations8 Nov 2019 Ningyu Zhang, Shumin Deng, Zhanlin Sun, Jiaoayan Chen, Wei zhang, Huajun Chen

Specifically, the framework takes advantage of a relation discriminator to distinguish between samples from different relations, and help learn relation-invariant features more transferable from source relations to target relations.

Knowledge Graph Completion Link Prediction +2

Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection

1 code implementation25 Oct 2019 Shumin Deng, Ningyu Zhang, Jiaojian Kang, Yichi Zhang, Wei zhang, Huajun Chen

Differing from vanilla prototypical networks simply computing event prototypes by averaging, which only consume event mentions once, our model is more robust and is capable of distilling contextual information from event mentions for multiple times due to the multi-hop mechanism of DMNs.

Event Detection Event Extraction +1

Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs

1 code implementation IJCNLP 2019 Mingyang Chen, Wen Zhang, Wei zhang, Qiang Chen, Huajun Chen

Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples.

Knowledge Graphs Link Prediction +1

Transfer Learning for Relation Extraction via Relation-Gated Adversarial Learning

no code implementations22 Aug 2019 Ningyu Zhang, Shumin Deng, Zhanlin Sun, Jiaoyan Chen, Wei zhang, Huajun Chen

However, the human annotation is expensive, while human-crafted patterns suffer from semantic drift and distant supervision samples are usually noisy.

Partial Domain Adaptation Relation Extraction +1

Augmenting Transfer Learning with Semantic Reasoning

no code implementations31 May 2019 Freddy Lecue, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen

We exploit their semantics to augment transfer learning by dealing with when to transfer with semantic measurements and what to transfer with semantic embeddings.

Transfer Learning

Learning to Decompose Compound Questions with Reinforcement Learning

no code implementations ICLR 2019 Haihong Yang, Han Wang, Shuang Guo, Wei zhang, Huajun Chen

Our model consists of two parts: (i) a novel learning-to-decompose agent that learns a policy to decompose a compound question into simple questions and (ii) three independent simple-question answerers that classify the corresponding relations for each simple question.

Question Answering reinforcement-learning

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

no code implementations21 Mar 2019 Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei zhang, Abraham Bernstein, Huajun Chen

We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently.

Entity Embeddings Knowledge Graphs +1

Interaction Embeddings for Prediction and Explanation in Knowledge Graphs

no code implementations12 Mar 2019 Wen Zhang, Bibek Paudel, Wei zhang, Abraham Bernstein, Huajun Chen

Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications.

Knowledge Graph Embedding Knowledge Graphs +1

Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract]

no code implementations20 Jan 2019 Yuxia Geng, Jiaoyan Chen, Ernesto Jimenez-Ruiz, Huajun Chen

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 research attentions.

Transfer Learning Zero-Shot Learning

Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding

no code implementations EMNLP 2018 Guanying Wang, Wen Zhang, Ruoxu Wang, Yalin Zhou, Xi Chen, Wei zhang, Hai Zhu, Huajun Chen

This paper proposes a label-free distant supervision method, which makes no use of the relation labels under this inadequate assumption, but only uses the prior knowledge derived from the KG to supervise the learning of the classifier directly and softly.

Knowledge Graph Embedding Relation Extraction +1

Knowledge-based Transfer Learning Explanation

1 code implementation22 Jul 2018 Jiaoyan Chen, Freddy Lecue, Jeff Z. Pan, Ian Horrocks, Huajun Chen

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, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i. e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain.

Decision Making Transfer Learning

Learning from Ontology Streams with Semantic Concept Drift

no code implementations24 Apr 2017 Freddy Lecue, Jiaoyan Chen, Jeff Pan, Huajun Chen

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records.

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