2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL)最新文献_第6页

Coagmento 2.0: A system for capturing individual and group information seeking behavior 一个捕捉个人和群体信息寻求行为的系统

2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) Pub Date : 2016-06-19 DOI: 10.1145/2910896.2925447

M. Mitsui, C. Shah

引用次数: 7

Personal video collection management behavior 个人视频采集管理行为

2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) Pub Date : 2016-06-19 DOI: 10.1145/2910896.2925440

S. Cunningham, D. Nichols, Judy Bowen

引用次数: 1

Extracting academic genealogy trees from the networked digital library of theses and dissertations 从论文和学位论文的网络化数字图书馆中提取学术谱系树

2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) Pub Date : 2016-06-19 DOI: 10.1145/2910896.2910916

W. Dores, Fabrício Benevenuto, Alberto H. F. Laender

引用次数: 13

A mathematical information retrieval system based on RankBoost 基于RankBoost的数学信息检索系统

2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) Pub Date : 2016-06-19 DOI: 10.1145/2910896.2925460

Ke Yuan, Liangcai Gao, Yuehan Wang, Xiaohan Yi, Zhi Tang

引用次数: 11

Querylog-based assessment of retrievability bias in a large newspaper corpus 基于查询日志的大型报纸语料库可检索性偏差评估

2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) Pub Date : 2016-06-19 DOI: 10.1145/2910896.2910907

Myriam C. Traub, Thaer Samar, J. V. Ossenbruggen, Jiyin He, A. D. Vries, L. Hardman

{"title":"Querylog-based assessment of retrievability bias in a large newspaper corpus","authors":"Myriam C. Traub, Thaer Samar, J. V. Ossenbruggen, Jiyin He, A. D. Vries, L. Hardman","doi":"10.1145/2910896.2910907","DOIUrl":"https://doi.org/10.1145/2910896.2910907","url":null,"abstract":"Bias in the retrieval of documents can directly influence the information access of a digital library. In the worst case, systematic favoritism for a certain type of document can render other parts of the collection invisible to users. This potential bias can be evaluated by measuring the retrievability for all documents in a collection. Previous evaluations have been performed on TREC collections using simulated query sets. The question remains, however, how representative this approach is of more realistic settings. To address this question, we investigate the effectiveness of the retrievability measure using a large digitized newspaper corpus, featuring two characteristics that distinguishes our experiments from previous studies: (1) compared to TREC collections, our collection contains noise originating from OCR processing, historical spelling and use of language; and (2) instead of simulated queries, the collection comes with real user query logs including click data. First, we assess the retrievability bias imposed on the newspaper collection by different IR models. We assess the retrievability measure and confirm its ability to capture the retrievability bias in our setup. Second, we show how simulated queries differ from real user queries regarding term frequency and prevalence of named entities, and how this affects the retrievability results.","PeriodicalId":109613,"journal":{"name":"2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115898046","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: 23

Low-cost semantic enhancement to digital library metadata and indexing: Simple yet effective strategies 数字图书馆元数据和索引的低成本语义增强:简单而有效的策略

2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) Pub Date : 2016-06-19 DOI: 10.1145/2910896.2910910

A. Hinze, D. Bainbridge, S. Cunningham, J. S. Downie

引用次数: 6

Routing memento requests using binary classifiers 使用二进制分类器路由纪念品请求

2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) Pub Date : 2016-06-19 DOI: 10.1145/2910896.2910899

Nicolas J. Bornand, Lyudmila Balakireva, H. Sompel

{"title":"Routing memento requests using binary classifiers","authors":"Nicolas J. Bornand, Lyudmila Balakireva, H. Sompel","doi":"10.1145/2910896.2910899","DOIUrl":"https://doi.org/10.1145/2910896.2910899","url":null,"abstract":"The Memento protocol provides a uniform approach to query individual web archives. Soon after its emergence, Memento Aggregator infrastructure was introduced that supports querying across multiple archives simultaneously. An Aggregator generates a response by issuing the respective Memento request against each of the distributed archives it covers. As the number of archives grows, it becomes increasingly challenging to deliver aggregate responses while keeping response times and computational costs under control. Ad-hoc heuristic approaches have been introduced to address this challenge and research has been conducted aimed at optimizing query routing based on archive profiles. In this paper, we explore the use of binary, archive-specific classifiers generated on the basis of the content cached by an Aggregator, to determine whether or not to query an archive for a given URI. Our results turn out to be readily applicable and can help to significantly decrease both the number of requests and the overall response times without compromising on recall. We find, among others, that classifiers can reduce the average number of requests by 77% compared to a brute force approach on all archives, and the overall response time by 42% while maintaining a recall of 0.847.","PeriodicalId":109613,"journal":{"name":"2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128529426","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

Interplanetary Wayback: The permanent web archive 星际之路:永久的网络档案

2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) Pub Date : 2016-06-19 DOI: 10.1145/2910896.2925467

Sawood Alam, Mat Kelly, Michael L. Nelson

引用次数: 22

Early prediction of scholar popularity 学者人气的早期预测

2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) Pub Date : 2016-06-19 DOI: 10.1145/2910896.2910905

Masoumeh Nezhadbiglari, Marcos André Gonçalves, J. Almeida

{"title":"Early prediction of scholar popularity","authors":"Masoumeh Nezhadbiglari, Marcos André Gonçalves, J. Almeida","doi":"10.1145/2910896.2910905","DOIUrl":"https://doi.org/10.1145/2910896.2910905","url":null,"abstract":"Prediction of scholar popularity has become an important research topic for a number of reasons. In this paper, we tackle the problem of predicting the popularity trend of scholars by concentrating on making predictions both as earlier and accurate as possible. In order to perform the prediction task, we first extract the popularity trends of scholars from a training set. To that end, we apply a time series clustering algorithm called K-Spectral Clustering (K-SC) to identify the popularity trends as cluster centroids. We then predict trends for scholars in a test set by solving a classification problem. Specifically, we first compute a set of measures for individual scholars based on the distance between earlier points in her particular popularity curve and the identified centroids. We then combine those distance measures with a set of academic features (e.g., number of publications, number of venues, etc) collected during the same monitoring period, and use them as input to a classification method. One aspect that distinguishes our method from other approaches is that the monitoring period, during which we gather information on each scholar popularity and academic features, is determined on a per scholar basis, as part of our approach. Using total citation count as measure of scientific popularity, we evaluate our solution on the popularity time series of more than 500,000 Computer Science scholars, gathered from Microsoft Azure Mar-ketplace1. The experimental results show that the our prediction method outperforms other alternative prediction methods. We also show how to apply our method jointly with regression models to improve the prediction of scholar popularity values (e.g., number of citations) at a given future time.","PeriodicalId":109613,"journal":{"name":"2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122056486","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: 17

Can academic conferences promote research collaboration? 学术会议能促进研究合作吗?

2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) Pub Date : 2016-06-19 DOI: 10.1145/2910896.2925446

Xiaoyan Su, Wei Wang, Shuo Yu, Chenxin Zhang, T. M. Bekele, Feng Xia

引用次数: 10