Search Results for author: Luoyi Fu

Found 20 papers, 9 papers with code

RFBFN: A Relation-First Blank Filling Network for Joint Relational Triple Extraction

1 code implementation ACL 2022 Zhe Li, Luoyi Fu, Xinbing Wang, Haisong Zhang, Chenghu Zhou

However, most existing works either ignore the semantic information of relations or predict subjects and objects sequentially.


Characterizing the Influence of Topology on Graph Learning Tasks

no code implementations11 Apr 2024 Kailong Wu, Yule Xie, Jiaxin Ding, Yuxiang Ren, Luoyi Fu, Xinbing Wang, Chenghu Zhou

Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations.

Graph Learning Stochastic Block Model

AceMap: Knowledge Discovery through Academic Graph

no code implementations5 Mar 2024 Xinbing Wang, Luoyi Fu, Xiaoying Gan, Ying Wen, Guanjie Zheng, Jiaxin Ding, Liyao Xiang, Nanyang Ye, Meng Jin, Shiyu Liang, Bin Lu, Haiwen Wang, Yi Xu, Cheng Deng, Shao Zhang, Huquan Kang, Xingli Wang, Qi Li, Zhixin Guo, Jiexing Qi, Pan Liu, Yuyang Ren, Lyuwen Wu, Jungang Yang, Jianping Zhou, Chenghu Zhou

While existing scientific search engines excel at delivering search results based on relational databases, they often neglect the analysis of collaborations between scientific entities and the evolution of ideas, as well as the in-depth analysis of content within scientific publications.

Knowledge Graphs Reading Comprehension

Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus

1 code implementation22 Nov 2023 Tianhang Zhang, Lin Qiu, Qipeng Guo, Cheng Deng, Yue Zhang, Zheng Zhang, Chenghu Zhou, Xinbing Wang, Luoyi Fu

Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields.

Hallucination Retrieval

Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation

no code implementations29 Aug 2023 Yi Xu, Junjie Ou, Hui Xu, Luoyi Fu, Lei Zhou, Xinbing Wang, Chenghu Zhou

To this end, we investigate the limits of historical information for temporal knowledge graph extrapolation and propose a new event forecasting model called Contrastive Event Network (CENET) based on a novel training framework of historical contrastive learning.

Contrastive Learning Knowledge Graphs

Graph Out-of-Distribution Generalization with Controllable Data Augmentation

no code implementations16 Aug 2023 Bin Lu, Xiaoying Gan, Ze Zhao, Shiyu Liang, Luoyi Fu, Xinbing Wang, Chenghu Zhou

The spurious correlations over hybrid distribution deviation degrade the performance of previous GNN methods and show large instability among different datasets.

Data Augmentation Graph Classification +2

Exploring and Verbalizing Academic Ideas by Concept Co-occurrence

1 code implementation4 Jun 2023 Yi Xu, Shuqian Sheng, Bo Xue, Luoyi Fu, Xinbing Wang, Chenghu Zhou

The results demonstrate that our system has broad prospects and can assist researchers in expediting the process of discovering new ideas.

Language Modelling Link Prediction

Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph

no code implementations14 Apr 2023 Cheng Deng, Jiaxin Ding, Luoyi Fu, Weinan Zhang, Xinbing Wang, Chenghu Zhou

In this work, we propose Covidia, COVID-19 interdisciplinary academic knowledge graph to bridge the gap between knowledge of COVID-19 on different domains.

Classification Contrastive Learning +2

FMGNN: Fused Manifold Graph Neural Network

no code implementations3 Apr 2023 Cheng Deng, Fan Xu, Jiaxing Ding, Luoyi Fu, Weinan Zhang, Xinbing Wang

Graph representation learning has been widely studied and demonstrated effectiveness in various graph tasks.

Graph Representation Learning Link Prediction +1

PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model

2 code implementations2 Apr 2023 Cheng Deng, Bo Tong, Luoyi Fu, Jiaxin Ding, Dexing Cao, Xinbing Wang, Chenghu Zhou

In the research of end-to-end dialogue systems, using real-world knowledge to generate natural, fluent, and human-like utterances with correct answers is crucial.

Knowledge Graphs Language Modelling +1

Temporal Knowledge Graph Reasoning with Historical Contrastive Learning

1 code implementation20 Nov 2022 Yi Xu, Junjie Ou, Hui Xu, Luoyi Fu

Simultaneously, it trains representations of queries to investigate whether the current moment depends more on historical or non-historical events by launching contrastive learning.

Contrastive Learning

INFINITY: A Simple Yet Effective Unsupervised Framework for Graph-Text Mutual Conversion

no code implementations22 Sep 2022 Yi Xu, Luoyi Fu, Zhouhan Lin, Jiexing Qi, Xinbing Wang

As a fully unsupervised framework, INFINITY is empirically verified to outperform state-of-the-art baselines for G2T and T2G tasks.

Knowledge Graphs

Disentangled Graph Contrastive Learning for Review-based Recommendation

no code implementations4 Sep 2022 Yuyang Ren, Haonan Zhang, Qi Li, Luoyi Fu, Jiaxin Ding, Xinde Cao, Xinbing Wang, Chenghu Zhou

In review-based recommendation methods, review data is considered as auxiliary information that can improve the quality of learned user/item or interaction representations for the user rating prediction task.

Contrastive Learning Recommendation Systems

Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer

1 code implementation27 May 2022 Bin Lu, Xiaoying Gan, Weinan Zhang, Huaxiu Yao, Luoyi Fu, Xinbing Wang

To address this challenge, cross-city knowledge transfer has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities.

Few-Shot Learning Graph Learning +2

Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation

1 code implementation27 May 2022 Bin Lu, Xiaoying Gan, Lina Yang, Weinan Zhang, Luoyi Fu, Xinbing Wang

Instead of replacing and retraining the fully connected neural network classifer, Geometer predicts the label of a node by finding the nearest class prototype.

Few-Shot Class-Incremental Learning Incremental Learning +2

High-Order Relation Construction and Mining for Graph Matching

no code implementations9 Oct 2020 Hui Xu, Liyao Xiang, Youmin Le, Xiaoying Gan, Yuting Jia, Luoyi Fu, Xinbing Wang

Iterated line graphs are introduced for the first time to describe such high-order information, based on which we present a new graph matching method, called High-order Graph Matching Network (HGMN), to learn not only the local structural correspondence, but also the hyperedge relations across graphs.

Graph Matching Relation +1

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