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Towards building a scholarly big data platform: Challenges, lessons and opportunities

Zhaohui Wu, Jian Wu, Madian Khabsa, Kyle Williams, Hung-Hsuan Chen, Wenyi Huang, Suppawong Tuarob, Sagnik Ray Choudhury, Alexander Ororbia, Prasenjit Mitra, C. Lee Giles
2014 IEEE/ACM Joint Conference on Digital Libraries  
We introduce a big data platform that provides various services for harvesting scholarly information and enabling efficient scholarly applications.  ...  We also introduce a set of scholarly applications built on top of this platform including citation recommendation and collaborator discovery.  ...  For global recommendation, RefSeer internally computes from the text a topical composition based on topic modeling [19] .  ... 
doi:10.1109/jcdl.2014.6970157 dblp:conf/jcdl/WuWKWCHTCOMG14 fatcat:tjulib72yfcbbcoufyxcf37bwe

Serving the readers of scholarly documents: A grand challenge for the introspective digital library

Min-Yen Kan
2015 2015 International Conference on Big Data and Smart Computing (BIGCOMP)  
Hearst, “Citances: Citation scholarly big data platform: Challenges, lessons and opportunities,” in sentences for semantic analysis of bioscience text,” in Proceedings of Digital Libraries  ...  Kan, “Scholarly paper recommendation via user’s recent research interests,” in Proceedings of the 10th annual joint [25] A. Abu-Jbara, J. Ezra, and D.  ... 
doi:10.1109/35021bigcomp.2015.7072807 dblp:conf/bigcomp/Kan15 fatcat:rmtqrpteojgpbfexkal5frqdya

Scientific Article Recommendation: Exploiting Common Author Relations and Historical Preferences

Feng Xia, Haifeng Liu, Ivan Lee, Longbing Cao
2016 IEEE Transactions on Big Data  
Scientific article recommender systems are playing an increasingly important role for researchers in retrieving scientific articles of interest in the coming era of big scholarly data.  ...  However, different researchers may have their own features and there might be corresponding methods for them resulting in better recommendations.  ...  Academic recommender systems aim to solve the information overload problem in big scholarly data such as finding relevant research paper, relevant publication venue, etc.  ... 
doi:10.1109/tbdata.2016.2555318 fatcat:5h5kucrbkbhlnewg4ft2l7cevu

A Review of Microsoft Academic Services for Science of Science Studies

Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Darrin Eide, Yuxiao Dong, Junjie Qian, Anshul Kanakia, Alvin Chen, Richard Rogahn
2019 Frontiers in Big Data  
the factoids into a knowledge graph, and (3) a reinforcement learning approach to assessing scholarly importance for entities participating in scholarly communications, called the saliency, that serves  ...  These elements enhance the capabilities of MAS in supporting the studies of science of science based on the GOTO principle, i.e., good and open data with transparent and objective methodologies.  ...  venues and even topics are non-trivial.  ... 
doi:10.3389/fdata.2019.00045 pmid:33693368 pmcid:PMC7931949 fatcat:dieitheynfcltbyebhq4rh6su4

An Interactive Scholarly Collaborative Network Based on Academic Relationships and Research Collaborations

Abrar A. Almuhanna, Wael M. S. Yafooz, Abdullah Alsaeedi
2022 Applied Sciences  
Furthermore, an enhanced approach for identifying expert authors by extracting evidence of expertise has been proposed based on the topic-modeling principle.  ...  Three experiments have been conducted on the collected data; they demonstrated that the most influential factor for accurately recommending a collaborator was the topic's distribution, which had an accuracy  ...  Consequently, recommending academic collaborators based on scholarly big data is becoming increasingly relevant.  ... 
doi:10.3390/app12020915 fatcat:pbxvuwy5pbdjde7bszdge3c5eu

Cross-language context-aware citation recommendation in scientific articles

Xuewei Tang, Xiaojun Wan, Xun Zhang
2014 Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval - SIGIR '14  
We thank the anonymous reviewers for their helpful comments.  ...  [5] apply multiple graphs model on the document recommendation problem. Their model jointly combines multiple graphs including citation, author and venue information. Torres et al.  ...  [11] propose a scholarly paper recommendation algorithm based on the citation graph and random-walker properties.  ... 
doi:10.1145/2600428.2609564 dblp:conf/sigir/TangWZ14 fatcat:zs62mkcrkvef7bjz7ih7vixjmi

Prediction methods and applications in the science of science: A survey

Jie Hou, Hanxiao Pan, Teng Guo, Ivan Lee, Xiangjie Kong, Feng Xia
2019 Computer Science Review  
Second, we review data-driven prediction based on paper citation count, and investigate research issues in this area.  ...  The development of data analytics technologies and the readily available scholarly data enable the exploration of data-driven prediction, which plays a pivotal role in finding the trend of scientific impact  ...  In this work, they discovered cross-cutting problems can be solved by using a Hybrid Graph Model, and the information of citation is useful for scholarly recommendation.  ... 
doi:10.1016/j.cosrev.2019.100197 fatcat:tokwu3it5zas7c7uu3urasi3au

Automated scholarly paper review: Possibility and challenges [article]

Jialiang Lin, Jiaxin Song, Zhangping Zhou, Xiaodong Shi
2021 arXiv   pre-print
Peer review is a widely accepted mechanism for research evaluation, playing a pivotal role in scholarly publishing.  ...  The major difficulties in its realization lie in imperfect document parsing and representation, inadequate data, defected human-computer interaction and flawed deep logical reasoning.  ...  Special and heartfelt gratitude goes to the first author's wife Fenmei Zhou, for her understanding and love. Her unwavering support and continuous encouragement enable this research to be possible.  ... 
arXiv:2111.07533v1 fatcat:keaq4bivrffuzjvpnziye3pgou

Openaire2020 D10.2 - Clustering Algorithms

Omiros Metaxas, Theodoros Giannakopoulos
2016 Zenodo  
This deliverable describes clustering algorithms for scholarly content, their implementation and performance, and reports on the outcomes.  ...  F+Trees that: 1) is more readable and extensible, 2) is usually faster in real world big datasets especially in multi-view settings, and 3) shares model related data structures across threads (contrary  ...  We use the following two metrics for automatically evaluating a model's fitting and topic quality: £ Model fitting (LL): To evaluate model fitting and convergence for the training data, we use the log  ... 
doi:10.5281/zenodo.1257349 fatcat:urryukf52barnfmymb6noquzq4

Low-Rank and Sparse Matrix Factorization for Scientific Paper Recommendation in Heterogeneous Network

2018 IEEE Access  
In this paper, we propose a novel low-rank and sparse matrix factorizationbased paper recommendation (LSMFPRec) method for authors.  ...  The effectiveness of the proposed LSMFPRec is demonstrated by the recommendation evaluation conducted on the AAN and CiteULike data sets.  ...  Moreover, it is easy to design distributed factorization algorithms for LSMFPRec, which would make LSMFPRec scalable for big data modeling.  ... 
doi:10.1109/access.2018.2865115 fatcat:jw7a766thnesbosxl3q2ree2ei

Random Walks on the Reputation Graph

Sabir Ribas, Berthier Ribeiro-Neto, Rodrygo L.T. Santos, Edmundo de Souza e Silva, Alberto Ueda, Nivio Ziviani
2015 Proceedings of the 2015 International Conference on Theory of Information Retrieval - ICTIR '15  
Thanks for everything, may God bless you in heaven.  ...  Karine, thanks for being a good friend and supporting Sávio in his journey. It is great having you in the family. Lili, thanks for always helping us carefully and teach us math.  ...  For instance, Deng et al. [2012] proposed a joint regularization framework to enhance expertise retrieval in academia by modeling heterogeneous networks as regularization constraints on top of a documentcentric  ... 
doi:10.1145/2808194.2809462 dblp:conf/ictir/RibasRSSUZ15 fatcat:3u3ry2ijl5do3p2gbkagmgg2rm

OpenCitations, an infrastructure organization for open scholarship

Silvio Peroni, David Shotton
2020 Quantitative Science Studies  
bibliographic and citation data made available in RDF under a Creative Commons public domain dedication; and the OpenCitations Indexes of open citation data, of which the first and largest is COCI, the  ...  These include the OpenCitations Data Model; the SPAR (Semantic Publishing and Referencing) Ontologies; OpenCitations' open software of generic applicability for searching, browsing, and providing REST  ...  use of the OCDM and provide a publication venue for the citation data that these projects are liberating from the scholarly literature.  ... 
doi:10.1162/qss_a_00023 fatcat:irg2q5dn65hrrmqtyka755k46a

Theory and practice of data citation

Gianmaria Silvello
2017 Journal of the Association for Information Science and Technology  
Many works in recent years have discussed data citation from different viewpoints: illustrating why data citation is needed, defining the principles and outlining recommendations for data citation systems  ...  , and providing computational methods for addressing specific issues of data citation.  ...  the XML hierarchical model, or the RDF graph model.  ... 
doi:10.1002/asi.23917 fatcat:isdji7rhibgldbj6efop3j7geu

TOSNet: A Topic-based Optimal Subnetwork Identification in Academic Networks

Hayat D. Bedru, Wenhong Zhao, Mubarak Alrashoud, Amr Tolba, He Guo, Feng Xia
2020 IEEE Access  
Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks.  ...  The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research  ...  For instance, if a scholar wants to gather collaborators who have experiences in "Big Scholarly Data", there is no support in the existing methods that could help identify appropriate candidates for a  ... 
doi:10.1109/access.2020.3034997 fatcat:2iu5gnfpr5bvxanqf5a4sih3wa

RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training [article]

Ziyue Qiao, Yanjie Fu, Pengyang Wang, Meng Xiao, Zhiyuan Ning, Yi Du, Yuanchun Zhou
2021 arXiv   pre-print
The model can accomplish multiple downstream tasks via a few fine-tuning steps. In this paper, we propose a multi-task self-supervised learning-based researcher data pre-training model named RPT.  ...  Specifically, we divide the researchers' data into semantic document sets and community graph.  ...  Analysis and mining based on big data technology have been implemented on these data and the analyses of researchers have been a hot topic.  ... 
arXiv:2110.07336v1 fatcat:4h7odty3hrct7cmz56ohvo7sxe
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