OpenResearch: Collaborative Management of Scholarly Communication Metadata

Vahdati, Sahar; Arndt, Natanael; Auer, Sören; Lange, Christoph

doi:10.1007/978-3-319-49004-5_50

Cited by 23 publications

(11 citation statements)

References 9 publications

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“…Abordagens de gestão colaborativa vêm sendo estudadas em diversas áreas do conhecimento, como na administração (French & Hollmann, 1975;Ma & Hu, 2020), na pesquisa (Shani et al, 2012;Vahdati et al, 2016), na gestão de recursos naturais (Parkins, 2008;Schelhas et al, 2001;Smith, 2015), no setor público (Alam et al, 2014;Jia-feng & Wen-xing, 2010;McGuire, 2006;Morris & Miller-Stevens, 2015), e na medicina (Caruso & Payne, 1990;Heinzelman, 1994;Katon et al, 1995).…”

Section: Abordagens De Gestão Colaborativaunclassified

Análise Bibliométrica da Gestão Colaborativa

Villarim

2021

CSJ

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Em um contexto em que a soma dos conhecimentos individuais já não é suficiente para responder à complexidade dos problemas e mudanças, modelos de gestão que propõem a utilização de ativos intangíveis do conhecimento como capital para desenvolvimento sustentável e alcance de vantagens competitivas, como a gestão colaborativa, têm ganhado atenção mundial. Para contribuir com o avanço deste campo de pesquisa, desenvolveu-se este estudo com o objetivo de traçar um panorama da produção científica sobre gestão colaborativa por meio de análise bibliométrica. Um total de 1564 publicações recuperadas das bases de dados Scopus, Web of Science e EBSCOhost foram analisadas. Os resultados demonstram um crescimento de publicações sobre o tema. Adicionalmente, nas análises de coautoria e coocorrência de palavras-chave, foram identificados grupos de autores, notadamente chineses, norte-americanos e europeus, que pesquisam a gestão colaborativa em distintas áreas do conhecimento, a exemplo das ciências ambientais, sociais, tecnológicas e da saúde, bem como em diferentes níveis de análise. A partir dos resultados encontrados, foram deixadas aqui sugestões para estudos futuros.

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Section: Abordagens De Gestão Colaborativaunclassified

Análise Bibliométrica da Gestão Colaborativa

Villarim

2021

CSJ

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“…Figure 1 shows the ontology that is used for the creation of these knowledge graphs. In order to define the terms, the OpenResearch [20] ontology is reused.…”

Section: A Scholarly Knowledge Graphmentioning

confidence: 99%

Embedding-Based Recommendations on Scholarly Knowledge Graphs

et al. 2020

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The increasing availability of scholarly metadata in the form of Knowledge Graphs (KG) offers opportunities for studying the structure of scholarly communication and evolution of science. Such KGs build the foundation for knowledge-driven tasks e.g., link discovery, prediction and entity classification which allow to provide recommendation services. Knowledge graph embedding (KGE) models have been investigated for such knowledge-driven tasks in different application domains. One of the applications of KGE models is to provide link predictions, which can also be viewed as a foundation for recommendation service, e.g. high confidence "co-author" links in a scholarly knowledge graph can be seen as suggested collaborations. In this paper, KGEs are reconciled with a specific loss function (Soft Margin) and examined with respect to their performance for co-authorship link prediction task on scholarly KGs. The results show a significant improvement in the accuracy of the experimented KGE models on the considered scholarly KGs using this specific loss. TransE with Soft Margin (TransE-SM) obtains a score of 79.5% Hits@10 for co-authorship link prediction task while the original TransE obtains 77.2%, on the same task. In terms of accuracy and Hits@10, TransE-SM also outperforms other state-of-the-art embedding models such as ComplEx, ConvE and RotatE in this setting. The predicted coauthorship links have been validated by evaluating profile of scholars.

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“…At the time of writing, OR contains crowd-sourced metadata about more than 66,000 conferences, +1000 workshops, and +370 event series. OR is based on SemanticMediaWiki (SMW) and supports researchers in collecting, organizing and disseminating information about scholarly events, tools, projects, people and organizations in a structured way (Vahdati et al 2016). Information is represented in wiki pages with semantic annotations, with the possibility to be queried via a SPARQL endpoint as well as inline queries embedded into wiki pages.…”

Section: Data Gatheringmentioning

confidence: 99%

Scholarly event characteristics in four fields of science: a metrics-based analysis

et al. 2020

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One of the key channels of scholarly knowledge exchange are scholarly events such as conferences, workshops, symposiums, etc.; such events are especially important and popular in Computer Science, Engineering, and Natural Sciences. However, scholars encounter problems in finding relevant information about upcoming events and statistics on their historic evolution. In order to obtain a better understanding of scholarly event characteristics in four fields of science, we analyzed the metadata of scholarly events of four major fields of science, namely Computer Science, Physics, Engineering, and Mathematics using Scholarly Events Quality Assessment suite, a suite of ten metrics. In particular, we analyzed renowned scholarly events belonging to five sub-fields within Computer Science, namely World Wide Web, Computer Vision, Software Engineering, Data Management, as well as Security and Privacy. This analysis is based on a systematic approach using descriptive statistics as well as exploratory data analysis. The findings are on the one hand interesting to observe the general evolution and success factors of scholarly events; on the other hand, they allow (prospective) event organizers, publishers, and committee members to assess the progress of their event over time and compare it to other events in the same field; and finally, they help researchers to make more informed decisions when selecting suitable venues for presenting their work. Based on these findings, a set of recommendations has been concluded to different stakeholders, involving event organizers, potential authors, proceedings publishers, and sponsors. Our comprehensive dataset of scholarly events of the aforementioned fields is openly available in a semantic format and maintained collaboratively at OpenResearch.org.

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OpenResearch: Collaborative Management of Scholarly Communication Metadata

Cited by 23 publications

References 9 publications

Análise Bibliométrica da Gestão Colaborativa

Análise Bibliométrica da Gestão Colaborativa

Embedding-Based Recommendations on Scholarly Knowledge Graphs

Scholarly event characteristics in four fields of science: a metrics-based analysis

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