Search Results for author: Huajun Chen

Found 126 papers, 84 papers with code

Large Knowledge Model: Perspectives and Challenges

no code implementations5 Dec 2023 Huajun Chen

Humankind's understanding of the world is fundamentally linked to our perception and cognition, with \emph{human languages} serving as one of the major carriers of \emph{world knowledge}.

Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering

1 code implementation11 Nov 2023 Yichi Zhang, Zhuo Chen, Yin Fang, Lei Cheng, Yanxi Lu, Fangming Li, Wen Zhang, Huajun Chen

Besides, we design a new alignment objective to align the LLM preference with human preference, aiming to train a better LLM for real-scenario domain-specific QA to generate reliable and user-friendly answers.

Knowledge Graphs Question Answering

Unveiling the Siren's Song: Towards Reliable Fact-Conflicting Hallucination Detection

1 code implementation18 Oct 2023 Xiang Chen, Duanzheng Song, Honghao Gui, Chengxi Wang, Ningyu Zhang, Fei Huang, Chengfei Lv, Dan Zhang, Huajun Chen

Large Language Models (LLMs), such as ChatGPT/GPT-4, have garnered widespread attention owing to their myriad of practical applications, yet their adoption has been constrained by issues of fact-conflicting hallucinations across web platforms.

Making Large Language Models Perform Better in Knowledge Graph Completion

1 code implementation10 Oct 2023 Yichi Zhang, Zhuo Chen, Wen Zhang, Huajun Chen

In this paper, we discuss how to incorporate the helpful KG structural information into the LLMs, aiming to achieve structrual-aware reasoning in the LLMs.

Knowledge Graph Completion Language Modelling +1

InstructProtein: Aligning Human and Protein Language via Knowledge Instruction

no code implementations5 Oct 2023 Zeyuan Wang, Qiang Zhang, Keyan Ding, Ming Qin, Xiang Zhuang, Xiaotong Li, Huajun Chen

To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein languages: (i) taking a protein sequence as input to predict its textual function description and (ii) using natural language to prompt protein sequence generation.

Knowledge Graphs Protein Function Prediction +1

OceanGPT: A Large Language Model for Ocean Science Tasks

1 code implementation3 Oct 2023 Zhen Bi, Ningyu Zhang, Yida Xue, Yixin Ou, Daxiong Ji, Guozhou Zheng, Huajun Chen

Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface.

Language Modelling Large Language Model

Unveiling the Pitfalls of Knowledge Editing for Large Language Models

1 code implementation3 Oct 2023 Zhoubo Li, Ningyu Zhang, Yunzhi Yao, Mengru Wang, Xi Chen, Huajun Chen

This paper pioneers the investigation into the potential pitfalls associated with knowledge editing for LLMs.

Editing Personality for LLMs

1 code implementation3 Oct 2023 Shengyu Mao, Ningyu Zhang, Xiaohan Wang, Mengru Wang, Yunzhi Yao, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen

This task seeks to adjust the models' responses to opinion-related questions on specified topics since an individual's personality often manifests in the form of their expressed opinions, thereby showcasing different personality traits.

Agents: An Open-source Framework for Autonomous Language Agents

1 code implementation14 Sep 2023 Wangchunshu Zhou, Yuchen Eleanor Jiang, Long Li, Jialong Wu, Tiannan Wang, Shi Qiu, Jintian Zhang, Jing Chen, Ruipu Wu, Shuai Wang, Shiding Zhu, Jiyu Chen, Wentao Zhang, Ningyu Zhang, Huajun Chen, Peng Cui, Mrinmaya Sachan

Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces.

When Do Program-of-Thoughts Work for Reasoning?

1 code implementation29 Aug 2023 Zhen Bi, Ningyu Zhang, Yinuo Jiang, Shumin Deng, Guozhou Zheng, Huajun Chen

Although there are effective methods like program-of-thought prompting for LLMs which uses programming language to tackle complex reasoning tasks, the specific impact of code data on the improvement of reasoning capabilities remains under-explored.

Code Generation Mathematical Reasoning

A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning

1 code implementation15 Aug 2023 Long Jin, Zhen Yao, Mingyang Chen, Huajun Chen, Wen Zhang

Though KGE models' capabilities are analyzed over different relational patterns in theory and a rough connection between better relational patterns modeling and better performance of KGC has been built, a comprehensive quantitative analysis on KGE models over relational patterns remains absent so it is uncertain how the theoretical support of KGE to a relational pattern contributes to the performance of triples associated to such a relational pattern.

Knowledge Graph Completion Knowledge Graph Embedding

EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models

1 code implementation14 Aug 2023 Peng Wang, Ningyu Zhang, Xin Xie, Yunzhi Yao, Bozhong Tian, Mengru Wang, Zekun Xi, Siyuan Cheng, Kangwei Liu, Guozhou Zheng, Huajun Chen

Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to the outdated/noisy data.

Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment

1 code implementation30 Jul 2023 Zhuo Chen, Lingbing Guo, Yin Fang, Yichi Zhang, Jiaoyan Chen, Jeff Z. Pan, Yangning Li, Huajun Chen, Wen Zhang

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 associated visual information.

 Ranked #1 on Multi-modal Entity Alignment on UMVM-oea-d-w-v2 (using extra training data)

Benchmarking Knowledge Graph Embeddings +2

Graph Sampling-based Meta-Learning for Molecular Property Prediction

1 code implementation29 Jun 2023 Xiang Zhuang, Qiang Zhang, Bin Wu, Keyan Ding, Yin Fang, Huajun Chen

To effectively utilize many-to-many correlations of molecules and properties, we propose a Graph Sampling-based Meta-learning (GS-Meta) framework for few-shot molecular property prediction.

Graph Sampling Meta-Learning +2

Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models

1 code implementation13 Jun 2023 Yin Fang, Xiaozhuan Liang, Ningyu Zhang, Kangwei Liu, Rui Huang, Zhuo Chen, Xiaohui Fan, Huajun Chen

Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields.

Catalytic activity prediction Chemical-Disease Interaction Extraction +14

Newton-Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems

1 code implementation24 May 2023 Lingbing Guo, Weiqing Wang, Zhuo Chen, Ningyu Zhang, Zequn Sun, Yixuan Lai, Qiang Zhang, Huajun Chen

Reasoning system dynamics is one of the most important analytical approaches for many scientific studies.

Revisit and Outstrip Entity Alignment: A Perspective of Generative Models

2 code implementations24 May 2023 Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Huajun Chen

We then reveal that their incomplete objective limits the capacity on both entity alignment and entity synthesis (i. e., generating new entities).

Entity Alignment

LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities

1 code implementation22 May 2023 Yuqi Zhu, Xiaohan Wang, Jing Chen, Shuofei Qiao, Yixin Ou, Yunzhi Yao, Shumin Deng, Huajun Chen, Ningyu Zhang

This paper presents an exhaustive quantitative and qualitative evaluation of Large Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning.

Event Extraction graph construction +2

Editing Large Language Models: Problems, Methods, and Opportunities

2 code implementations22 May 2023 Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang

Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context.

Model Editing

Schema-adaptable Knowledge Graph Construction

1 code implementation15 May 2023 Hongbin Ye, Honghao Gui, Xin Xu, Xi Chen, Huajun Chen, Ningyu Zhang

This necessitates a system that can handle evolving schema automatically to extract information for KGC.

graph construction UIE

Knowledge Rumination for Pre-trained Language Models

1 code implementation15 May 2023 Yunzhi Yao, Peng Wang, Shengyu Mao, Chuanqi Tan, Fei Huang, Huajun Chen, Ningyu Zhang

Previous studies have revealed that vanilla pre-trained language models (PLMs) lack the capacity to handle knowledge-intensive NLP tasks alone; thus, several works have attempted to integrate external knowledge into PLMs.

Language Modelling

Continual Multimodal Knowledge Graph Construction

1 code implementation15 May 2023 Xiang Chen, Ningyu Zhang, Jintian Zhang, Xiaohan Wang, Tongtong Wu, Xi Chen, Yongheng Wang, Huajun Chen

Multimodal Knowledge Graph Construction (MKGC) involves creating structured representations of entities and relations using multiple modalities, such as text and images.

Continual Learning graph construction +1

NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning

1 code implementation28 Apr 2023 Wen Zhang, Zhen Yao, Mingyang Chen, Zhiwei Huang, Huajun Chen

Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities.

Graph Representation Learning Knowledge Graphs

CodeKGC: Code Language Model for Generative Knowledge Graph Construction

2 code implementations18 Apr 2023 Zhen Bi, Jing Chen, Yinuo Jiang, Feiyu Xiong, Wei Guo, Huajun Chen, Ningyu Zhang

However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks.

Code Completion graph construction +1

Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer

1 code implementation3 Mar 2023 Wen Zhang, Yushan Zhu, Mingyang Chen, Yuxia Geng, Yufeng Huang, Yajing Xu, Wenting Song, Huajun Chen

Through experiments, we justify that the pretrained KGTransformer could be used off the shelf as a general and effective KRF module across KG-related tasks.

Image Classification Knowledge Graphs +3

Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding

1 code implementation3 Feb 2023 Mingyang Chen, Wen Zhang, Zhen Yao, Yushan Zhu, Yang Gao, Jeff Z. Pan, Huajun Chen

In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities.

Entity Embeddings Knowledge Graph Embedding +3

Domain-Agnostic Molecular Generation with Self-feedback

1 code implementation26 Jan 2023 Yin Fang, Ningyu Zhang, Zhuo Chen, Lingbing Guo, Xiaohui Fan, Huajun Chen

However, despite the potential of language models in molecule generation, they face numerous challenges such as the generation of syntactically or chemically flawed molecules, narrow domain focus, and limitations in creating diverse and directionally feasible molecules due to a dearth of annotated data or external molecular databases.

Language Modelling Molecular Docking +1

One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER

2 code implementations25 Jan 2023 Xiang Chen, Lei LI, Shuofei Qiao, Ningyu Zhang, Chuanqi Tan, Yong Jiang, Fei Huang, Huajun Chen

Previous typical solutions mainly obtain a NER model by pre-trained language models (PLMs) with data from a rich-resource domain and adapt it to the target domain.

NER Text Generation

Editing Language Model-based Knowledge Graph Embeddings

2 code implementations25 Jan 2023 Siyuan Cheng, Ningyu Zhang, Bozhong Tian, Xi Chen, Qingbing Liu, Huajun Chen

To address this issue, we propose a new task of editing language model-based KG embeddings in this paper.

Knowledge Graph Embeddings Language Modelling

Analogical Inference Enhanced Knowledge Graph Embedding

1 code implementation3 Jan 2023 Zhen Yao, Wen Zhang, Mingyang Chen, Yufeng Huang, Yi Yang, Huajun Chen

And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding.

Knowledge Graph Embedding Knowledge Graphs +1

MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid

1 code implementation29 Dec 2022 Zhuo Chen, Jiaoyan Chen, Wen Zhang, Lingbing Guo, Yin Fang, Yufeng Huang, Yichi Zhang, Yuxia Geng, Jeff Z. Pan, Wenting Song, Huajun Chen

Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images.

 Ranked #1 on Entity Alignment on FBYG15k (using extra training data)

Knowledge Graphs Multi-modal Entity Alignment

Reasoning with Language Model Prompting: A Survey

2 code implementations19 Dec 2022 Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen

Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc.

Arithmetic Reasoning Common Sense Reasoning +4

Generative Knowledge Graph Construction: A Review

1 code implementation23 Oct 2022 Hongbin Ye, Ningyu Zhang, Hui Chen, Huajun Chen

Our contributions are threefold: (1) We present a detailed, complete taxonomy for the generative KGC methods; (2) We provide a theoretical and empirical analysis of the generative KGC methods; (3) We propose several research directions that can be developed in the future.

graph construction Knowledge Graphs

Tele-Knowledge Pre-training for Fault Analysis

1 code implementation20 Oct 2022 Zhuo Chen, Wen Zhang, Yufeng Huang, Mingyang Chen, Yuxia Geng, Hongtao Yu, Zhen Bi, Yichi Zhang, Zhen Yao, Wenting Song, Xinliang Wu, Yi Yang, Mingyi Chen, Zhaoyang Lian, YingYing Li, Lei Cheng, Huajun Chen

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 normally found in both machine log data and product documents.

Language Modelling

Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction

1 code implementation19 Oct 2022 Yunzhi Yao, Shengyu Mao, Ningyu Zhang, Xiang Chen, Shumin Deng, Xi Chen, Huajun Chen

With the development of pre-trained language models, many prompt-based approaches to data-efficient knowledge graph construction have been proposed and achieved impressive performance.

Event Extraction graph construction +2

Neural-Symbolic Entangled Framework for Complex Query Answering

no code implementations19 Sep 2022 Zezhong Xu, Wen Zhang, Peng Ye, Hui Chen, Huajun Chen

In this work, we propose a Neural and Symbolic Entangled framework (ENeSy) for complex query answering, which enables the neural and symbolic reasoning to enhance each other to alleviate the cascading error and KG incompleteness.

Complex Query Answering Link Prediction

DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning

1 code implementation4 Jul 2022 Zhuo Chen, Yufeng Huang, Jiaoyan Chen, Yuxia Geng, Wen Zhang, Yin Fang, Jeff Z. Pan, Huajun Chen

Specifically, we (1) developed a cross-modal semantic grounding network to investigate the model's capability of disentangling semantic attributes from the images; (2) applied an attribute-level contrastive learning strategy to further enhance the model's discrimination on fine-grained visual characteristics against the attribute co-occurrence and imbalance; (3) proposed a multi-task learning policy for considering multi-model objectives.

Contrastive Learning Image Classification +3

Disentangled Ontology Embedding for Zero-shot Learning

1 code implementation8 Jun 2022 Yuxia Geng, Jiaoyan Chen, Wen Zhang, Yajing Xu, Zhuo Chen, Jeff Z. Pan, Yufeng Huang, Feiyu Xiong, Huajun Chen

In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects.

Image Classification Ontology Embedding +2

Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning

2 code implementations29 May 2022 Xiang Chen, Lei LI, Ningyu Zhang, Xiaozhuan Liang, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen

Specifically, vanilla prompt learning may struggle to utilize atypical instances by rote during fully-supervised training or overfit shallow patterns with low-shot data.

Few-Shot Text Classification Memorization +5

Multi-modal Protein Knowledge Graph Construction and Applications

no code implementations27 May 2022 Siyuan Cheng, Xiaozhuan Liang, Zhen Bi, Huajun Chen, Ningyu Zhang

Existing data-centric methods for protein science generally cannot sufficiently capture and leverage biology knowledge, which may be crucial for many protein tasks.

graph construction

Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce

1 code implementation22 May 2022 Yincen Qu, Ningyu Zhang, Hui Chen, Zelin Dai, Zezhong Xu, Chengming Wang, Xiaoyu Wang, Qiang Chen, Huajun Chen

In addition to formulating the new task, we also release a new Benchmark dataset of Salience Evaluation in E-commerce (BSEE) and hope to promote related research on commonsense knowledge salience evaluation.

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 Named Entity Recognition +2

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 Link Prediction +4

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

1 code implementation4 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.

Few-Shot Learning Memorization +2

Contrastive Demonstration Tuning for Pre-trained Language Models

1 code implementation9 Apr 2022 Xiaozhuan Liang, Ningyu Zhang, Siyuan Cheng, Zhenru Zhang, Chuanqi Tan, Huajun Chen

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

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 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 +1

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 +2

Kformer: Knowledge Injection in Transformer Feed-Forward Layers

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

In this work, we propose a simple model, Kformer, which takes advantage of the knowledge stored in PTMs and external knowledge via knowledge injection in Transformer FFN layers.

Language Modelling Question Answering

Zero-shot and Few-shot 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

Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision.

Data Augmentation Few-Shot Learning +10

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 +3

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

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.

Event Argument Extraction

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

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

4 code implementations 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 Prompt Engineering

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.

Document-level Relation Extraction Segmentation +1

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 Aspect-Based Sentiment Analysis (ABSA) +1

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 #5 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

1 code implementation1 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 +5

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.

BIG-bench Machine Learning 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 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

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

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 Embedding Knowledge Graph Embeddings

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.

counterfactual 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 Ontology Embedding +4

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.

Link Prediction Partial Domain Adaptation +1

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 +1

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.

BIG-bench Machine Learning Decision Making +1

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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