World Wide Web-Internet and Web Information Systems最新文献

A semantic and service-based approach for adaptive mutli-structured data curation in data lakehouses 基于语义和服务的自适应多结构数据管理方法

3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-11-06 DOI: 10.1007/s11280-023-01218-3

Firas Zouari, Chirine Ghedira-Guegan, Khouloud Boukadi, Nadia Kabachi

引用次数: 0

KC-GEE: knowledge-based conditioning for generative event extraction KC-GEE:基于知识的条件作用生成事件提取

3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-10-25 DOI: 10.1007/s11280-023-01216-5

Tongtong Wu, Fatemeh Shiri, Jingqi Kang, Guilin Qi, Gholamreza Haffari, Yuan-Fang Li

{"title":"KC-GEE: knowledge-based conditioning for generative event extraction","authors":"Tongtong Wu, Fatemeh Shiri, Jingqi Kang, Guilin Qi, Gholamreza Haffari, Yuan-Fang Li","doi":"10.1007/s11280-023-01216-5","DOIUrl":"https://doi.org/10.1007/s11280-023-01216-5","url":null,"abstract":"Abstract Event extraction is an important, but challenging task. Many existing techniques decompose it into event and argument detection/classification subtasks, which are complex structured prediction problems. Generation-based extraction techniques lessen the complexity of the problem formulation and are able to leverage the reasoning capabilities of large pretrained language models. However, they still suffer from poor zero-shot generalizability and are ineffective in handling long contexts such as documents. We propose a generative event extraction model, KC-GEE, that addresses these limitations. A key contribution of KC-GEE is a novel knowledge-based conditioning technique that injects the schema of candidate event types as the prefix into each layer of an encoder-decoder language model. This enables effective zero-shot learning and improves supervised learning. Our experiments on two benchmark datasets demonstrate the strong performance of our KC-GEE model. It achieves particularly strong results in the challenging document-level extraction task and in the zero-shot learning setting, outperforming state-of-the-art models by up to 5.4 absolute F1 points.","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135217078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

CoSP: co-selection pick for a global explainability of black box machine learning models CoSP:黑盒机器学习模型的全局可解释性的共同选择选择

3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-10-18 DOI: 10.1007/s11280-023-01213-8

Dou El Kefel Mansouri, Seif-Eddine Benkabou, Khaoula Meddahi, Allel Hadjali, Amin Mesmoudi, Khalid Benabdeslem, Souleyman Chaib

引用次数: 0

Graph neural network for recommendation in complex and quaternion spaces 复和四元数空间中的推荐图神经网络

3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-10-10 DOI: 10.1007/s11280-023-01210-x

Longcan Wu, Daling Wang, Shi Feng, Xiangmin Zhou, Yifei Zhang, Ge Yu

引用次数: 0

Securing recommender system via cooperative training 通过合作培训确保推荐系统的安全

3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-10-04 DOI: 10.1007/s11280-023-01214-7

Qingyang Wang, Chenwang Wu, Defu Lian, Enhong Chen

引用次数: 0

Low-cost crossed probing path planning for network failure localization 网络故障定位的低成本交叉探测路径规划

3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-10-03 DOI: 10.1007/s11280-023-01206-7

Hongyun Gao, Laiping Zhao, Sheng Chen, Keqiu Li

引用次数: 0

A semi-supervised framework for concept-based hierarchical document clustering 基于概念的分层文档聚类的半监督框架

3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-10-02 DOI: 10.1007/s11280-023-01209-4

Seyed Mojtaba Sadjadi, Hoda Mashayekhi, Hamid Hassanpour

引用次数: 0

An effective method for the protection of user health topic privacy for health information services 健康信息服务用户健康主题隐私保护的有效方法

3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-09-26 DOI: 10.1007/s11280-023-01208-5

Zongda Wu, Huawen Liu, Jian Xie, Guandong Xu, Gang Li, Chenglang Lu

引用次数: 0

Discovering time series motifs of all lengths using dynamic time warping 使用动态时间翘曲发现所有长度的时间序列主题

3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-09-20 DOI: 10.1007/s11280-023-01207-6

Zemin Chao, Hong Gao, Dongjing Miao, Hongzhi Wang

引用次数: 0

FPGN: follower prediction framework for infectious disease prevention FPGN:传染病预防跟随者预测框架

3区计算机科学

World Wide Web-Internet and Web Information Systems Pub Date : 2023-09-16 DOI: 10.1007/s11280-023-01205-8

Jianke Yu, Xianhang Zhang, Hanchen Wang, Xiaoyang Wang, Wenjie Zhang, Ying Zhang

{"title":"FPGN: follower prediction framework for infectious disease prevention","authors":"Jianke Yu, Xianhang Zhang, Hanchen Wang, Xiaoyang Wang, Wenjie Zhang, Ying Zhang","doi":"10.1007/s11280-023-01205-8","DOIUrl":"https://doi.org/10.1007/s11280-023-01205-8","url":null,"abstract":"Abstract In recent years, how to prevent the widespread transmission of infectious diseases in communities has been a research hot spot. Tracing close contact with infected individuals is one of the most severe problems. In this work, we present a model called Follower Prediction Graph Network (FPGN) to identify high-risk visitors, which is known as follower prediction. The model is designed to identify visitors who may be infected with a disease by tracking their activities at the exact location of infected visitors. FPGN is inspired by the state-of-the-art temporal graph edge prediction algorithm TGN and draws on the shortcomings of existing algorithms. It utilizes graph structure information based on ( $$alpha $$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mi>α</mml:mi> </mml:math> , $$beta $$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mi>β</mml:mi> </mml:math> )-core, time interval statistics by using the statistics of timestamp information, and a GAT-based prediction module to achieve high accuracy in follower prediction. Extensive experiments are conducted on two real datasets, demonstrating the progress of FPGN. The experimental results show that FPGN can achieve the highest results compared with other SOTA baselines. Its AP scores are higher than 0.46, and its AUC scores are higher than 0.62.","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135308649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0