International Journal on Digital Libraries最新文献_第4页

Comparing different search methods for the open access journal recommendation tool B!SON 开放获取期刊推荐工具B!儿子

IF 1.5

International Journal on Digital Libraries Pub Date : 2023-07-20 DOI: 10.1007/s00799-023-00372-3

Elias Entrup, A. Eppelin, R. Ewerth, Josephine Hartwig, Marco Tullney, Michael Wohlgemuth, Anett Hoppe

引用次数: 0

PEERRec: An AI-based approach to automatically generate recommendations and predict decisions in peer review PEERRec:一种基于人工智能的方法，在同行评审中自动生成建议和预测决策

IF 1.5

International Journal on Digital Libraries Pub Date : 2023-07-04 DOI: 10.1007/s00799-023-00375-0

P. Bharti, Tirthankar Ghosal, Mayank Agarwal, Asif Ekbal

引用次数: 1

AgAsk: an agent to help answer farmer’s questions from scientific documents AgAsk:帮助农民从科学文献中回答问题的代理

International Journal on Digital Libraries Pub Date : 2023-06-19 DOI: 10.1007/s00799-023-00369-y

Bevan Koopman, Ahmed Mourad, Hang Li, Anton van der Vegt, Shengyao Zhuang, Simon Gibson, Yash Dang, David Lawrence, Guido Zuccon

{"title":"AgAsk: an agent to help answer farmer’s questions from scientific documents","authors":"Bevan Koopman, Ahmed Mourad, Hang Li, Anton van der Vegt, Shengyao Zhuang, Simon Gibson, Yash Dang, David Lawrence, Guido Zuccon","doi":"10.1007/s00799-023-00369-y","DOIUrl":"https://doi.org/10.1007/s00799-023-00369-y","url":null,"abstract":"Abstract Decisions in agriculture are increasingly data-driven. However, valuable agricultural knowledge is often locked away in free-text reports, manuals and journal articles. Specialised search systems are needed that can mine agricultural information to provide relevant answers to users’ questions. This paper presents AgAsk—an agent able to answer natural language agriculture questions by mining scientific documents. We carefully survey and analyse farmers’ information needs. On the basis of these needs, we release an information retrieval test collection comprising real questions, a large collection of scientific documents split in passages, and ground truth relevance assessments indicating which passages are relevant to each question. We implement and evaluate a number of information retrieval models to answer farmers questions, including two state-of-the-art neural ranking models. We show that neural rankers are highly effective at matching passages to questions in this context. Finally, we propose a deployment architecture for AgAsk that includes a client based on the Telegram messaging platform and retrieval model deployed on commodity hardware. The test collection we provide is intended to stimulate more research in methods to match natural language to answers in scientific documents. While the retrieval models were evaluated in the agriculture domain, they are generalisable and of interest to others working on similar problems. The test collection is available at: https://github.com/ielab/agvaluate .","PeriodicalId":44974,"journal":{"name":"International Journal on Digital Libraries","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135336431","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

ORKG-Leaderboards: a systematic workflow for mining leaderboards as a knowledge graph ORKG-Leaderboards:将排行榜作为知识图进行挖掘的系统化工作流程

International Journal on Digital Libraries Pub Date : 2023-06-15 DOI: 10.1007/s00799-023-00366-1

Salomon Kabongo, Jennifer D’Souza, Sören Auer

{"title":"ORKG-Leaderboards: a systematic workflow for mining leaderboards as a knowledge graph","authors":"Salomon Kabongo, Jennifer D’Souza, Sören Auer","doi":"10.1007/s00799-023-00366-1","DOIUrl":"https://doi.org/10.1007/s00799-023-00366-1","url":null,"abstract":"Abstract The purpose of this work is to describe the orkg -Leaderboard software designed to extract leaderboards defined as task–dataset–metric tuples automatically from large collections of empirical research papers in artificial intelligence (AI). The software can support both the main workflows of scholarly publishing, viz. as LaTeX files or as PDF files. Furthermore, the system is integrated with the open research knowledge graph (ORKG) platform, which fosters the machine-actionable publishing of scholarly findings. Thus, the systemsss output, when integrated within the ORKG’s supported Semantic Web infrastructure of representing machine-actionable ‘resources’ on the Web, enables: (1) broadly, the integration of empirical results of researchers across the world, thus enabling transparency in empirical research with the potential to also being complete contingent on the underlying data source(s) of publications; and (2) specifically, enables researchers to track the progress in AI with an overview of the state-of-the-art across the most common AI tasks and their corresponding datasets via dynamic ORKG frontend views leveraging tables and visualization charts over the machine-actionable data. Our best model achieves performances above 90% F1 on the leaderboard extraction task, thus proving orkg -Leaderboards a practically viable tool for real-world usage. Going forward, in a sense, orkg -Leaderboards transforms the leaderboard extraction task to an automated digitalization task, which has been, for a long time in the community, a crowdsourced endeavor.","PeriodicalId":44974,"journal":{"name":"International Journal on Digital Libraries","volume":"36 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134981699","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

A detailed library perspective on nearly unsupervised information extraction workflows in digital libraries 数字图书馆中几乎无监督的信息提取工作流程的详细图书馆视角

IF 1.5

International Journal on Digital Libraries Pub Date : 2023-06-13 DOI: 10.1007/s00799-023-00368-z

H. Kroll, Jan Pirklbauer, Florian Plötzky, Wolf-Tilo Balke

引用次数: 0

Special Issue: Epigraphy and Paleography: Bringing Records from the Distant Past to the Present 特刊:铭文和古文字:从遥远的过去到现在带来的记录

IF 1.5

International Journal on Digital Libraries Pub Date : 2023-06-01 DOI: 10.1007/s00799-023-00371-4

Stephen M. Griffin

引用次数: 0

From stone to silicon: technical advances in epigraphy 从石头到硅:金石学的技术进步

IF 1.5

International Journal on Digital Libraries Pub Date : 2023-06-01 DOI: 10.1007/s00799-023-00362-5

W. Seales, Christy Chapman

引用次数: 0

IF 1.5

International Journal on Digital Libraries Pub Date : 2023-05-24 DOI: 10.1007/s00799-023-00364-3

Tamara Heck, C. Keller, Marc Rittberger

引用次数: 0

Correction: Beyond translation: engaging with foreign languages in a digital library 更正:超越翻译:在数字图书馆中接触外语

IF 1.5

International Journal on Digital Libraries Pub Date : 2023-05-19 DOI: 10.1007/s00799-023-00365-2

G. Crane, Alison Babeu, Lisa M. Cerrato, Amelia Parrish, Carolina Penagos, Farnoosh Shamsian, James Tauber, Jake Wegner

引用次数: 0

Self-training involving semantic-space finetuning for semi-supervised multi-label document classification 基于语义空间调优的半监督多标签文档分类自训练

IF 1.5

International Journal on Digital Libraries Pub Date : 2023-05-11 DOI: 10.1007/s00799-023-00355-4

Zhewei Xu, M. Iwaihara

引用次数: 0