Discovery of potential collaboration networks from open knowledge sources

Chicaiza, Janneth; Piedra, Nelson; Lopez-Vargas, Jorge; Tovar, Edmundo

doi:10.1109/educon.2018.8363382

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…From this perspective, the knowledge discovery process, an important KE technique, transforms data into information by interpretation, derives new information from existing information by elaboration, and acquires new knowledge by learning. It is noteworthy that selected studies use techniques such as Data Mining (Li, Peng & Li, 2015;Sumba et al, 2016;Chicaiza et al, 2018) and SNA (Lopes, Silva & Oliveira, 2011;Brandão et al, 2013;Vacca et al, 2015).…”

Section: Knowledge Discoverymentioning

confidence: 99%

“…ClusterArticles 01(Sumba et al, 2016;Ma et al, 2017;Santarém Segundo, Coneglian & Lucas, 2017;Chicaiza et al, 2018) 02(Velardi, Cucchiarelli & Petit, 2007;Triperina, Sgouropoulou & Tsolakidis, 2013;Li, Peng & Li, 2015;Vacca et al, 2015;Zamiri et al, 2019) 03…”

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confidence: 99%

“… Chicaiza et al (2018) presented a conceptual view for analyzing RCNs, in which researchers could use the model developed to identify critical areas of investment in research organizations, to find peers interested in common or related issues, or simply to learn about the topics researched by people from a particular sector. The model sought to facilitate interoperability and data sharing between different scientific applications or services.…”

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confidence: 99%

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Ontology-Based Artifacts for Research Collaborative Networks

Lima

Varvákis

2022

Anais Do Congresso Internacional De Conhecimento E Inovação – Ciki

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The identification and understanding of patterns in Research Collaborative Networks has become increasingly important, however, the absence of a systemic vision about the process of codification, management and dissemination of knowledge implies a reduction in the capacity to institutionalize the actions of these networks. The purpose of this article was to analyze how ontology-based artifacts have been used to represent Research Collaborative Networks. For this, an integrative literature review was conducted. The analysis of this field shows that these artifacts can provide more assertive access to relevant information and knowledge. They have become increasingly important as the use of knowledge graphs and machine learning has grown. Context is a critical feature for the data that are implicit in the analysis to really be intelligence indicators.

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Section: Knowledge Discoverymentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Ontology-Based Artifacts for Research Collaborative Networks

Lima

Varvákis

2022

Anais Do Congresso Internacional De Conhecimento E Inovação – Ciki

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“…However, the generation of collaboration networks of Ecuadorian researchers is analysed in Piedra et al [14], where the authors classified a subset of Computer Science researchers (obtained from Scopus) into research topics through an inference process based on the DBpedia concepts. Next, the authors of this work expanded this idea to Computer Science researchers from the Andean region countries in a more recent paper [15]. Nonetheless, all the aforementioned approaches focus on individual data sources; therefore; they do not provide an entire view of the research status in Ecuador.…”

Section: Related Workmentioning

confidence: 99%

REDI: Towards knowledge graph-powered scholarly information management and research networking

Vivar

Segarra

Villazón-Terrazas

et al. 2020

Journal of Information Science

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Academic data management has become an increasingly challenging task as research evolves over time. Essential tasks such as information retrieval and research networking have turned into extremely difficult operations due to an ever-growing number of researchers and scientific articles. Numerous initiatives have emerged in the IT environments to address this issue, especially focused on web technologies. Although those approaches have individually provided solutions for diverse problems, they still can not offer integrated knowledge bases nor flexibility to exploit adequately this information. In this article, we present REDI, a Linked Data-powered framework for academic knowledge management and research networking, which introduces a new perspective of integration. REDI combines information from multiple sources into a consolidated knowledge base through state-of-the-art procedures and leverages semantic web standards to represent the information. Moreover, REDI takes advantage of such knowledge for data visualisation and analysis, which ultimately improves and simplifies many activities including research networking.

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Scholarly recommendation systems: a literature survey

Zhang,

Patra,

Yaseen

et al. 2023

Knowl Inf Syst

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A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain.

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Discovery of potential collaboration networks from open knowledge sources

Cited by 4 publications

References 11 publications

Ontology-Based Artifacts for Research Collaborative Networks

Ontology-Based Artifacts for Research Collaborative Networks

REDI: Towards knowledge graph-powered scholarly information management and research networking

Scholarly recommendation systems: a literature survey

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