A Framework for Paper Submission Recommendation System

Cuong, Dinh V.; Nguyen, Dac H.; Huynh, Son T.; Huynh, Phong T.; Gurrin, Cathal; Dao, Minh-Son; Dang-Nguyen, Duc-Tien; Nguyen, Binh T.

doi:10.1145/3372278.3391929

Cited by 4 publications

(1 citation statement)

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“…Knowledge graphs lay a solid foundation for a plethora of applications which can exploit such semantically enriched graph structures. Among the possible applications are enhanced document retrieval techniques [13], automated content comparisons [14], reasoning engines, autonomous research systems [15], mathematical proof assistants [16] and paper recommendation systems [17,18].…”

Section: Motivationmentioning

confidence: 99%

SciKGTeX - A LATEX Package to Semantically Annotate Contributions in Scientific Publications

Bless,

Baimuratov,

Karras

2023

2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

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The continuously increasing output of published research makes the work of researchers harder as it becomes impossible to keep track of and compare the most recent advances in a field. Scientific knowledge graphs have been proposed as a solution to structure the content of research publications in a machine-readable way and enable more efficient, computer-assisted workflows for many research activities. Crowdsourcing approaches are used frequently to build and maintain such scientific knowledge graphs. Researchers are motivated to contribute to these crowdsourcing efforts as they want their work to be included in the knowledge graphs and benefit from applications built on top of them. To contribute to scientific knowledge graphs, researchers need simple and easy-to-use solutions to generate new knowledge graph elements and establish the practice of semantic representations in scientific communication.In this thesis, I present SciKGTeX, a L A T E X package to semantically annotate scientific contributions at the time of document creation. The L A T E X package allows authors of scientific publications to mark the main contributions such as the background, research problem, method, results and conclusion of their work directly in L A T E X source files. The package then automatically embeds them as metadata into the generated PDF document. In addition to the package, I document a user evaluation with 26 participants which I conducted to assess the usability and feasibility of the solution.The analysis of the evaluation results shows that SciKGTeX is highly usable with a score of 79 out of 100 on the System Usability Scale. Furthermore, the study showed that the functionalities of the package can be picked up very quickly by the study participants which only needed 7 minutes on average to annotate the main contributions on a sample abstract of a published paper. SciKGTeX demonstrates a new way to generate structured metadata for the key contributions of research publications and embed them into PDF files at the time of document creation. nische Informationsbibliothek (TIB) in Hannover for allowing me to work together with his group in the context of the ORKG project. Many of the colleagues at TIB have given me great feedback on SciKGTeX and helped me to achieve my evaluation goals. I hope the outcome of this work will ultimately contribute to their vision of an open scientific knowledge graph.Lastly, I thank all the anonymous participants in the user evaluation of this work for their time and feedback. A specific thanks goes to Dr. Umutcan Şimşek for letting me present my thesis in his Seminar and gaining valuable insights as well as a few more evaluation participants.

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Section: Motivationmentioning

confidence: 99%

SciKGTeX - A LATEX Package to Semantically Annotate Contributions in Scientific Publications

Bless,

Baimuratov,

Karras

2023

2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

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Comparing different search methods for the open access journal recommendation tool B!SON

et al. 2023

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Finding a suitable open access journal to publish academic work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of predatory publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. A systematic requirements analysis was conducted in the form of a survey. The developed tool suggests open access journals based on title, abstract and references provided by the user. The recommendations are built on open data, publisher-independent and work across domains and languages. Transparency is provided by its open source nature, an open application programming interface (API) and by specifying which matches the shown recommendations are based on. The recommendation quality has been evaluated using two different evaluation techniques, including several new recommendation methods. We were able to improve the results from our previous paper with a pre-trained transformer model. The beta version of the tool received positive feedback from the community and in several test sessions. We developed a recommendation system for open access journals to help researchers find a suitable journal. The open tool has been extensively tested, and we found possible improvements for our current recommendation technique. Development by two German academic libraries ensures the longevity and sustainability of the system.

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