Pub Date : 2023-01-16DOI: 10.1007/s41019-022-00201-8
Yanlin Yang, Zhonglin Ye, Haixing Zhao, Lei Meng
{"title":"A Novel Link Prediction Framework Based on Gravitational Field","authors":"Yanlin Yang, Zhonglin Ye, Haixing Zhao, Lei Meng","doi":"10.1007/s41019-022-00201-8","DOIUrl":"https://doi.org/10.1007/s41019-022-00201-8","url":null,"abstract":"","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"44 1","pages":"47 - 60"},"PeriodicalIF":4.2,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87560620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2023-01-01DOI: 10.1007/s41019-022-00203-6
Zenghua Liao, Zongqiang Yang, Peixin Huang, Ning Pang, Xiang Zhao
In recent years, Coronavirus disease 2019 (COVID-19) has become a global epidemic, and some efforts have been devoted to tracking and controlling its spread. Extracting structured knowledge from involved epidemic case reports can inform the surveillance system, which is important for controlling the spread of outbreaks. Therefore, in this paper, we focus on the task of Chinese epidemic event extraction (EE), which is defined as the detection of epidemic-related events and corresponding arguments in the texts of epidemic case reports. To facilitate the research of this task, we first define the epidemic-related event types and argument roles. Then we manually annotate a Chinese COVID-19 epidemic dataset, named COVID-19 Case Report (CCR). We also propose a novel hierarchical EE architecture, named multi-model fusion-based hierarchical event extraction (MFHEE). In MFHEE, we introduce a multi-model fusion strategy to tackle the issue of recognition bias of previous EE models. The experimental results on CCR dataset show that our method can effectively extract epidemic events and outperforms other baselines on this dataset. The comparative experiments results on other generic datasets show that our method has good scalability and portability. The ablation studies also show that the proposed hierarchical structure and multi-model fusion strategy contribute to the precision of our model.
Supplementary information: The online version contains supplementary material available at 10.1007/s41019-022-00203-6.
{"title":"Multi-Model Fusion-Based Hierarchical Extraction for Chinese Epidemic Event.","authors":"Zenghua Liao, Zongqiang Yang, Peixin Huang, Ning Pang, Xiang Zhao","doi":"10.1007/s41019-022-00203-6","DOIUrl":"https://doi.org/10.1007/s41019-022-00203-6","url":null,"abstract":"<p><p>In recent years, Coronavirus disease 2019 (COVID-19) has become a global epidemic, and some efforts have been devoted to tracking and controlling its spread. Extracting structured knowledge from involved epidemic case reports can inform the surveillance system, which is important for controlling the spread of outbreaks. Therefore, in this paper, we focus on the task of Chinese epidemic event extraction (EE), which is defined as the detection of epidemic-related events and corresponding arguments in the texts of epidemic case reports. To facilitate the research of this task, we first define the epidemic-related event types and argument roles. Then we manually annotate a Chinese COVID-19 epidemic dataset, named COVID-19 Case Report (CCR). We also propose a novel hierarchical EE architecture, named <i>m</i>ulti-model <i>f</i>usion-based <i>h</i>ierarchical <i>e</i>vent <i>e</i>xtraction (MFHEE). In MFHEE, we introduce a multi-model fusion strategy to tackle the issue of recognition bias of previous EE models. The experimental results on CCR dataset show that our method can effectively extract epidemic events and outperforms other baselines on this dataset. The comparative experiments results on other generic datasets show that our method has good scalability and portability. The ablation studies also show that the proposed hierarchical structure and multi-model fusion strategy contribute to the precision of our model.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s41019-022-00203-6.</p>","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"8 1","pages":"73-83"},"PeriodicalIF":4.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9807097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9377525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The success of blockchain technology in cryptocurrencies reveals its potential in the data management field. Recently, there is a trend in the database community to integrate blockchains and traditional databases to obtain security, efficiency, and privacy from the two distinctive but related systems. In this survey, we discuss the use of blockchain technology in the data management field and focus on the fusion system of blockchains and databases. We first classify existing blockchain-related data management technologies by their locations on the blockchain-database spectrum. Based on the taxonomy, we discuss three types of fusion systems and analyze their design spaces and trade-offs. Then, by further investigating the typical systems and techniques of each type of fusion system and comparing the solutions, we provide insights of each fusion model. Finally, we outline the unsolved challenges and promising directions in this field and believe that fusion systems will take a more important role in data management tasks. We hope this survey can help both academia and industry to better understand the advantages and limitations of blockchain-related data management systems and develop fusion systems that meet various requirements in practice.
{"title":"A Survey on the Integration of Blockchains and Databases.","authors":"Changhao Zhu, Junzhe Li, Ziyue Zhong, Cong Yue, Meihui Zhang","doi":"10.1007/s41019-023-00212-z","DOIUrl":"10.1007/s41019-023-00212-z","url":null,"abstract":"<p><p>The success of blockchain technology in cryptocurrencies reveals its potential in the data management field. Recently, there is a trend in the database community to integrate blockchains and traditional databases to obtain security, efficiency, and privacy from the two distinctive but related systems. In this survey, we discuss the use of blockchain technology in the data management field and focus on the fusion system of blockchains and databases. We first classify existing blockchain-related data management technologies by their locations on the blockchain-database spectrum. Based on the taxonomy, we discuss three types of fusion systems and analyze their design spaces and trade-offs. Then, by further investigating the typical systems and techniques of each type of fusion system and comparing the solutions, we provide insights of each fusion model. Finally, we outline the unsolved challenges and promising directions in this field and believe that fusion systems will take a more important role in data management tasks. We hope this survey can help both academia and industry to better understand the advantages and limitations of blockchain-related data management systems and develop fusion systems that meet various requirements in practice.</p>","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"8 2","pages":"196-219"},"PeriodicalIF":4.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10124707/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9484493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-28DOI: 10.1007/s41019-022-00199-z
Haiting Zhong, W. He, Li-zhen Cui, Lei Liu, Zhongmin Yan, Kun Zhao
{"title":"Joint Attention Networks with Inherent and Contextual Preference-Awareness for Successive POI Recommendation","authors":"Haiting Zhong, W. He, Li-zhen Cui, Lei Liu, Zhongmin Yan, Kun Zhao","doi":"10.1007/s41019-022-00199-z","DOIUrl":"https://doi.org/10.1007/s41019-022-00199-z","url":null,"abstract":"","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"8 1","pages":"370 - 382"},"PeriodicalIF":4.2,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88717608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号