{"title":"A semantic and service-based approach for adaptive mutli-structured data curation in data lakehouses","authors":"Firas Zouari, Chirine Ghedira-Guegan, Khouloud Boukadi, Nadia Kabachi","doi":"10.1007/s11280-023-01218-3","DOIUrl":"https://doi.org/10.1007/s11280-023-01218-3","url":null,"abstract":"","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":"2019 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135636720","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}
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.
{"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":"18 1","pages":"0"},"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}
{"title":"CoSP: co-selection pick for a global explainability of black box machine learning models","authors":"Dou El Kefel Mansouri, Seif-Eddine Benkabou, Khaoula Meddahi, Allel Hadjali, Amin Mesmoudi, Khalid Benabdeslem, Souleyman Chaib","doi":"10.1007/s11280-023-01213-8","DOIUrl":"https://doi.org/10.1007/s11280-023-01213-8","url":null,"abstract":"","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135884253","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}
Pub Date : 2023-10-10DOI: 10.1007/s11280-023-01210-x
Longcan Wu, Daling Wang, Shi Feng, Xiangmin Zhou, Yifei Zhang, Ge Yu
{"title":"Graph neural network for recommendation in complex and quaternion spaces","authors":"Longcan Wu, Daling Wang, Shi Feng, Xiangmin Zhou, Yifei Zhang, Ge Yu","doi":"10.1007/s11280-023-01210-x","DOIUrl":"https://doi.org/10.1007/s11280-023-01210-x","url":null,"abstract":"","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136294659","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}
{"title":"Securing recommender system via cooperative training","authors":"Qingyang Wang, Chenwang Wu, Defu Lian, Enhong Chen","doi":"10.1007/s11280-023-01214-7","DOIUrl":"https://doi.org/10.1007/s11280-023-01214-7","url":null,"abstract":"","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135592119","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}
Pub Date : 2023-09-26DOI: 10.1007/s11280-023-01208-5
Zongda Wu, Huawen Liu, Jian Xie, Guandong Xu, Gang Li, Chenglang Lu
{"title":"An effective method for the protection of user health topic privacy for health information services","authors":"Zongda Wu, Huawen Liu, Jian Xie, Guandong Xu, Gang Li, Chenglang Lu","doi":"10.1007/s11280-023-01208-5","DOIUrl":"https://doi.org/10.1007/s11280-023-01208-5","url":null,"abstract":"","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134957982","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}
Pub Date : 2023-09-20DOI: 10.1007/s11280-023-01207-6
Zemin Chao, Hong Gao, Dongjing Miao, Hongzhi Wang
{"title":"Discovering time series motifs of all lengths using dynamic time warping","authors":"Zemin Chao, Hong Gao, Dongjing Miao, Hongzhi Wang","doi":"10.1007/s11280-023-01207-6","DOIUrl":"https://doi.org/10.1007/s11280-023-01207-6","url":null,"abstract":"","PeriodicalId":49356,"journal":{"name":"World Wide Web-Internet and Web Information Systems","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136309340","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}
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 ( $$alpha $$ α , $$beta $$ β )-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.
{"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":"32 1","pages":"0"},"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}
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