2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)最新文献_第8页

What Did It Look Like: A service for creating website timelapses using the Memento framework 它是什么样子的:一个使用Memento框架创建网站时间轴的服务

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-04-28 DOI: 10.1109/JCDL52503.2021.00061

Dhruv Patel, Alexander C. Nwala, Michael L. Nelson, Michele C. Weigle

引用次数: 0

It's All About The Cards: Sharing on Social Media Encouraged HTML Metadata Growth 社交媒体上的分享鼓励HTML元数据的增长

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-04-08 DOI: 10.1109/JCDL52503.2021.00023

Shawn M. Jones, Valentina Neblitt-Jones, Michele C. Weiglet, Martin Klein, Michael L. Nelson

{"title":"It's All About The Cards: Sharing on Social Media Encouraged HTML Metadata Growth","authors":"Shawn M. Jones, Valentina Neblitt-Jones, Michele C. Weiglet, Martin Klein, Michael L. Nelson","doi":"10.1109/JCDL52503.2021.00023","DOIUrl":"https://doi.org/10.1109/JCDL52503.2021.00023","url":null,"abstract":"In a perfect world, all articles consistently contain sufficient metadata to describe the resource. We know this is not the reality, so we are motivated to investigate the evolution of the metadata that is present when authors and publishers supply their own. Because applying metadata takes time, we recognize that each news article author has a limited metadata budget with which to spend their time and effort. How are they spending this budget? What are the top metadata categories in use? How did they grow over time? What purpose do they serve? We also recognize that not all metadata fields are used equally. What is the growth of individual fields over time? Which fields experienced the fastest adoption? In this paper, we review 227,724 archived HTML news articles from 29 outlets captured by the Internet Archive between 1998 and 2016. Upon reviewing the metadata fields in each article, we discovered that 2010 began a metadata renaissance as publishers embraced metadata for improved search engine ranking, search engine tracking, social media tracking, and social media sharing. When analyzing individual fields, we find that one application of metadata stands out above all others: social cards - the cards generated by platforms like Twitter when one shares a URL. Once a metadata standard was established for cards in 2010, its fields were adopted by 20% of articles in the first year and reached more than 95% adoption by 2016. This rate of adoption surpasses efforts like Schema.org and Dublin Core by a fair margin. When confronted with these results on how news publishers spend their metadata budget, we must conclude that it is all about the cards.","PeriodicalId":112400,"journal":{"name":"2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115464115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection 神经语言模型是好的剽窃者吗?神经释义检测的基准

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-03-19 DOI: 10.1109/JCDL52503.2021.00065

Jan Philip Wahle, Terry Ruas, Norman Meuschke, Bela Gipp

引用次数: 28

S2AND: A Benchmark and Evaluation System for Author Name Disambiguation and:作者姓名消歧的基准与评价体系

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-03-12 DOI: 10.1109/JCDL52503.2021.00029

Shivashankar Subramanian, Daniel King, Doug Downey, Sergey Feldman

{"title":"S2AND: A Benchmark and Evaluation System for Author Name Disambiguation","authors":"Shivashankar Subramanian, Daniel King, Doug Downey, Sergey Feldman","doi":"10.1109/JCDL52503.2021.00029","DOIUrl":"https://doi.org/10.1109/JCDL52503.2021.00029","url":null,"abstract":"Author Name Disambiguation (AND) is the task of resolving which author mentions in a bibliographic database refer to the same real-world person, and is a critical ingredient of digital library applications such as search and citation analysis. While many AND algorithms have been proposed, comparing them is difficult because they often employ distinct features and are evaluated on different datasets. In response to this challenge, we present S2AND, a unified benchmark dataset for AND on scholarly papers, as well as an open-source reference model implementation. Our dataset harmonizes eight disparate AND datasets into a uniform format, with a single rich feature set drawn from the Semantic Scholar (S2) database. Our evaluation suite for S2AND reports performance split by facets like publication year and number of papers, allowing researchers to track both global performance and measures of fairness across facet values. Our experiments show that because previous datasets tend to cover idiosyncratic and biased slices of the literature, algorithms trained to perform well on one on them may generalize poorly to others. By contrast, we show how training on a union of datasets in S2AND results in more robust models that perform well even on datasets unseen in training. The resulting AND model also substantially improves over the production algorithm in S2, reducing error by over 50% in terms of B3 F1. We release our unified dataset, model code, trained models, and evaluation suite to the research community.11https://github.com/allenai/S2AND/","PeriodicalId":112400,"journal":{"name":"2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123174169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

References of References: How Far is the Knowledge Ancestry 参考文献的参考文献:知识祖先有多远

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-01-21 DOI: 10.1109/JCDL52503.2021.00079

Chao Min, Yi Bu, Tao Han

{"title":"References of References: How Far is the Knowledge Ancestry","authors":"Chao Min, Yi Bu, Tao Han","doi":"10.1109/JCDL52503.2021.00079","DOIUrl":"https://doi.org/10.1109/JCDL52503.2021.00079","url":null,"abstract":"Scientometrics studies have extended from direct citations to high-order citations, as simple citation count is found to tell only part of the story regarding scientific impact. This extension is deemed to be beneficial in scenarios like research evaluation, science history modelling, and information retrieval. In contrast to citations of citations (forward citation generations), references of references (backward citation generations) as another side of high-order citations, is relatively less explored. We adopt a series of metrics for measuring the unfolding of backward citations of a focal paper, tracing back to its knowledge ancestors generation by generation. Two sub-fields in Physics are subject to such analysis on a large-scale citation network. Preliminary results show that (1) most papers in our dataset can be traced to their knowledge ancestry; (2) the size distribution of backward citation generations presents a decreasing-and-then-increasing shape; and (3) citations more than one generation away are still relevant to the focal paper, from either a forward or backward perspective; yet, backward citation generations are higher in topic relevance to the paper of interest. Furthermore, the backward citation generations shed lights for literature recommendation, science evaluation, and sociology of science studies.","PeriodicalId":112400,"journal":{"name":"2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114788949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Introduction to Digital Libraries 数字图书馆概论

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2016-06-19 DOI: 10.1145/2910896.2925429

E. Fox, Yinlin Chen

引用次数: 2