2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)最新文献_第2页

Comparing Personalized PageRank and Activation Spreading in Wikipedia Diagram-Based Search 基于维基百科图的搜索中个性化PageRank和激活传播的比较

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00016

Hisham Benotman, D. Maier

{"title":"Comparing Personalized PageRank and Activation Spreading in Wikipedia Diagram-Based Search","authors":"Hisham Benotman, D. Maier","doi":"10.1109/JCDL52503.2021.00016","DOIUrl":"https://doi.org/10.1109/JCDL52503.2021.00016","url":null,"abstract":"Diagram Navigation (DN) is based on using existing diagrams for a domain as maps to navigate and query a collection from different perspectives. With a relatively small number of manual connections, such as ones between diagram concepts and related documents, a domain expert can integrate their perspective of a domain (depicted in a diagram) into the navigation system of a collection. DN utilizes the abundance of internal connections in a collection, such as Wikipedia hyperlinks to access the entire collection. In a Diagram-to-Content (D2C) query, an end user selects a diagram concept to retrieve a ranked list of related collection documents. In a Content-to-Diagram (C2D) query, DN highlights related concepts in a diagram based on document(s) selected by the user. To increase D2C ranking performance, we study and tune Personalized PageRank and an energy-spreading algorithm. We report key differences in how the algorithms rank D2C queries. We show that the tested algorithms are affected differently by Wikipedia graph structures, such as categories and hyperlinks from article templates. We also show that diagrams not only can provide overviews, but they also positively bias the ranking of D2C queries.","PeriodicalId":112400,"journal":{"name":"2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124763576","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

Weak Supervision for Scientific Document Relevance Tagging 科学文献相关标注监管薄弱

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00060

Drahomira Herrmannova, Chathika Gunaratne, V. Walker, Andrew A. Rooney, Robert M. Patton, Mary Wolfe, Charles Schmitt

引用次数: 0

Estimating Contemporary Relevance of Past News 评估过去新闻的当代相关性

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00019

M. Sato, A. Jatowt, Yijun Duan, Ricardo Campos, Masatoshi Yoshikawa

{"title":"Estimating Contemporary Relevance of Past News","authors":"M. Sato, A. Jatowt, Yijun Duan, Ricardo Campos, Masatoshi Yoshikawa","doi":"10.1109/JCDL52503.2021.00019","DOIUrl":"https://doi.org/10.1109/JCDL52503.2021.00019","url":null,"abstract":"Our society generates massive amounts of digital data, significant portion of which is being archived and made accessible to the public for the current and future use. In addition, historical born-analog documents are being increasingly digitized and included in document archives which are available online. Professionals who use document archives tend to know what they wish to search for. Yet, if the results are to be useful and attractive for ordinary users they need to contain content which is interesting and familiar. However, the state-of-the-art retrieval methods for document archives basically apply same techniques as search engines for synchronic document collections. In this paper, we introduce a novel concept of estimating the relation of archival documents to the present times, called contemporary relevance. Contemporary relevance can be used for improving access to archival document collections so that users have higher probability of finding interesting or useful content. We then propose an effective method for computing contemporary relevance degrees of news articles using Learning to Rank with a range of diverse features, and we successfully test it on the New York Times Annotated document collection. Our proposal offers a novel paradigm of information access to archival document collections by incorporating the context of contemporary time.","PeriodicalId":112400,"journal":{"name":"2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)","volume":"95 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116359706","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: 2

TASSY—A Text Annotation Survey System TASSY-A文本标注调查系统

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00052

K. Sinha, Norman Meuschke, Bela Gipp

引用次数: 11

Incorporating Fairness in Paper Recommendation 论文推荐的公平性

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00050

Reem Alsaffar, Susan Gauch, Mohammed Alqahtani, Omar Salman

{"title":"Incorporating Fairness in Paper Recommendation","authors":"Reem Alsaffar, Susan Gauch, Mohammed Alqahtani, Omar Salman","doi":"10.1109/JCDL52503.2021.00050","DOIUrl":"https://doi.org/10.1109/JCDL52503.2021.00050","url":null,"abstract":"Although many conferences use double-blind reviewing to increase fairness, studies show that bias still occurs. Our research focuses on developing fair algorithms that correct for these biases and select papers from a more demographically diverse group of authors. To increase author diversity and achieve demographic parity, we use multidimensional author profiles with Boolean feature values, i.e., gender, ethnicity, career stage, university rank, and geolocation. Based on these profiles, we present two algorithms that explicitly consider demographic diversity and paper quality during paper recommendation. To evaluate our approaches, we compare the resulting set of conference papers with the actual accepted papers in the conference, measuring the diversity gain, utility savings, and F-measure for each method. Our best method, Multi-Faceted Diversity, produces a set of papers whose authors achieve 95% similarity to the demographics of the pool across multiple dimensions, increasing the selected papers' authors by 46% with only a 2.48% drop in utility. Tasks within academia, such as conference papers, journal papers, grant and proposal reviews, could benefit from applying this approach.","PeriodicalId":112400,"journal":{"name":"2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128330464","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

Visualizing Feature-based Similarity for Research Paper Recommendation 基于特征的研究论文推荐相似度可视化

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00033

Corinna Breitinger, Harald Reiterer

{"title":"Visualizing Feature-based Similarity for Research Paper Recommendation","authors":"Corinna Breitinger, Harald Reiterer","doi":"10.1109/JCDL52503.2021.00033","DOIUrl":"https://doi.org/10.1109/JCDL52503.2021.00033","url":null,"abstract":"Research paper recommender systems are widely used by academics to discover and explore the most relevant publications on a topic. While existing recommendation interfaces present researchers with a ranked list of publications based on a global relevance score, they fail to visualize the full range of non-textual features uniquely present in academic publications: citations, figures, charts, or images, and mathematical formulae or expressions. Especially for STEM literature, examining such non-textual features efficiently can provide utility to researchers interested in answering specialized research questions or information needs. If research paper search and recommender systems are to consider the similarity of such features as one facet of a content-based similarity assessment for academic literature, new methods for visualizing these non-textual features are needed. In this paper, we review the state-of-the-art in visualizing feature-based similarity in documents. We subsequently propose a set of user-customizable visualization approaches tailored to STEM literature and the research paper recommendation context. Results from a study with 10 expert users show that the interactive visualization interface we propose for the exploration of non-textual features in publications can effectively address specialized information retrieval tasks, which cannot be addressed by existing research paper search or recommendation interfaces.","PeriodicalId":112400,"journal":{"name":"2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)","volume":"415 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122992934","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: 0

Crowdsourced Linked Data Question Answering with AQUACOLD 众包关联数据问题回答与水产养殖

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00043

Nick Collis, Ingo Frommholz

引用次数: 0

A New Methodology to Bring Out Typical Users Interactions in Digital Libraries 数字图书馆典型用户交互的新方法

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00013

Marwa Trabelsi, Cyrille Suire, Jacques Morcos, R. Champagnat

{"title":"A New Methodology to Bring Out Typical Users Interactions in Digital Libraries","authors":"Marwa Trabelsi, Cyrille Suire, Jacques Morcos, R. Champagnat","doi":"10.1109/JCDL52503.2021.00013","DOIUrl":"https://doi.org/10.1109/JCDL52503.2021.00013","url":null,"abstract":"With the growing amount of digital publications, digital libraries (DLs) attract a variety of users for diverse tasks. A practical need to investigate how users interact with digital library (DL) portals is greatly increasing. Modeling users' interaction in DLs is interestingly required in order to optimize the use of different DL functionalities and to ease the accessibility to stored resources. The aim of this work is to take advantage of Process Mining techniques to model DL user's journeys. To the best of our knowledge, no other research work applied PM to real DLs users journeys. Discovered models can therefore be used in forthcoming work to present a set of recommendations to DL users. However, the large number of generated logs leads to complicated models that are not generic for all users and do not allow achieving all their objectives. For this reason, we propose in this paper a new methodology of grouping users' interactions prior to modeling. We compare our proposed approach to two state-of-the-art methods over a synthetic resource manually annotated used for validation and a real-life user interaction history (event logs) provided by the national library of France. The experimental part shows that our method outperforms existing methods in both clustering and modeling users over the synthetic dataset and generates interesting models on real-world data.","PeriodicalId":112400,"journal":{"name":"2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117274775","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

Surfacing Collective Harms in Privacy Sensitive Data 隐私敏感数据中的集体危害

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00032

Nicholas M. Weber

引用次数: 0

HistoInformatics2021: The 6th International Workshop on Computational History HistoInformatics2021:第六届计算历史国际研讨会

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI: 10.1109/JCDL52503.2021.00072

Yasunobu Sumikawa, Ryohei Ikejiri, A. Doucet, Eva Pfanzelter, Mohammed Hasanuzzaman, G. Dias, Ian Milligan, A. Jatowt

引用次数: 0