Pub Date : 2023-07-20DOI: 10.1007/s00799-023-00372-3
Elias Entrup, A. Eppelin, R. Ewerth, Josephine Hartwig, Marco Tullney, Michael Wohlgemuth, Anett Hoppe
{"title":"Comparing different search methods for the open access journal recommendation tool B!SON","authors":"Elias Entrup, A. Eppelin, R. Ewerth, Josephine Hartwig, Marco Tullney, Michael Wohlgemuth, Anett Hoppe","doi":"10.1007/s00799-023-00372-3","DOIUrl":"https://doi.org/10.1007/s00799-023-00372-3","url":null,"abstract":"","PeriodicalId":44974,"journal":{"name":"International Journal on Digital Libraries","volume":"65 2 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81405201","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}
Pub Date : 2023-07-04DOI: 10.1007/s00799-023-00375-0
P. Bharti, Tirthankar Ghosal, Mayank Agarwal, Asif Ekbal
{"title":"PEERRec: An AI-based approach to automatically generate recommendations and predict decisions in peer review","authors":"P. Bharti, Tirthankar Ghosal, Mayank Agarwal, Asif Ekbal","doi":"10.1007/s00799-023-00375-0","DOIUrl":"https://doi.org/10.1007/s00799-023-00375-0","url":null,"abstract":"","PeriodicalId":44974,"journal":{"name":"International Journal on Digital Libraries","volume":"6 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80162340","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}
Pub Date : 2023-06-19DOI: 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
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 .
{"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}
Pub Date : 2023-06-15DOI: 10.1007/s00799-023-00366-1
Salomon Kabongo, Jennifer D’Souza, Sören Auer
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.
{"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}
Pub Date : 2023-06-13DOI: 10.1007/s00799-023-00368-z
H. Kroll, Jan Pirklbauer, Florian Plötzky, Wolf-Tilo Balke
{"title":"A detailed library perspective on nearly unsupervised information extraction workflows in digital libraries","authors":"H. Kroll, Jan Pirklbauer, Florian Plötzky, Wolf-Tilo Balke","doi":"10.1007/s00799-023-00368-z","DOIUrl":"https://doi.org/10.1007/s00799-023-00368-z","url":null,"abstract":"","PeriodicalId":44974,"journal":{"name":"International Journal on Digital Libraries","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80224094","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}
Pub Date : 2023-06-01DOI: 10.1007/s00799-023-00371-4
Stephen M. Griffin
{"title":"Special Issue: Epigraphy and Paleography: Bringing Records from the Distant Past to the Present","authors":"Stephen M. Griffin","doi":"10.1007/s00799-023-00371-4","DOIUrl":"https://doi.org/10.1007/s00799-023-00371-4","url":null,"abstract":"","PeriodicalId":44974,"journal":{"name":"International Journal on Digital Libraries","volume":"4 1","pages":"77 - 85"},"PeriodicalIF":1.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90851631","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}
Pub Date : 2023-06-01DOI: 10.1007/s00799-023-00362-5
W. Seales, Christy Chapman
{"title":"From stone to silicon: technical advances in epigraphy","authors":"W. Seales, Christy Chapman","doi":"10.1007/s00799-023-00362-5","DOIUrl":"https://doi.org/10.1007/s00799-023-00362-5","url":null,"abstract":"","PeriodicalId":44974,"journal":{"name":"International Journal on Digital Libraries","volume":"24 1","pages":"129 - 138"},"PeriodicalIF":1.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85951241","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}
Pub Date : 2023-05-24DOI: 10.1007/s00799-023-00364-3
Tamara Heck, C. Keller, Marc Rittberger
{"title":"Coverage and similarity of bibliographic databases to find most relevant literature for systematic reviews in education","authors":"Tamara Heck, C. Keller, Marc Rittberger","doi":"10.1007/s00799-023-00364-3","DOIUrl":"https://doi.org/10.1007/s00799-023-00364-3","url":null,"abstract":"","PeriodicalId":44974,"journal":{"name":"International Journal on Digital Libraries","volume":"2 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73428461","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}
Pub Date : 2023-05-19DOI: 10.1007/s00799-023-00365-2
G. Crane, Alison Babeu, Lisa M. Cerrato, Amelia Parrish, Carolina Penagos, Farnoosh Shamsian, James Tauber, Jake Wegner
{"title":"Correction: Beyond translation: engaging with foreign languages in a digital library","authors":"G. Crane, Alison Babeu, Lisa M. Cerrato, Amelia Parrish, Carolina Penagos, Farnoosh Shamsian, James Tauber, Jake Wegner","doi":"10.1007/s00799-023-00365-2","DOIUrl":"https://doi.org/10.1007/s00799-023-00365-2","url":null,"abstract":"","PeriodicalId":44974,"journal":{"name":"International Journal on Digital Libraries","volume":"4 1","pages":"177 - 177"},"PeriodicalIF":1.5,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85600207","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}
Pub Date : 2023-05-11DOI: 10.1007/s00799-023-00355-4
Zhewei Xu, M. Iwaihara
{"title":"Self-training involving semantic-space finetuning for semi-supervised multi-label document classification","authors":"Zhewei Xu, M. Iwaihara","doi":"10.1007/s00799-023-00355-4","DOIUrl":"https://doi.org/10.1007/s00799-023-00355-4","url":null,"abstract":"","PeriodicalId":44974,"journal":{"name":"International Journal on Digital Libraries","volume":"94 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82970342","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}
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