On the Utilization of Structural and Textual Information of a Scientific Knowledge Graph to Discover Future Research Collaborations: A Link Prediction Perspective

Giarelis, Nikolaos; Kanakaris, Nikos; Karacapilidis, Nikos

doi:10.1007/978-3-030-61527-7_29

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…During the peer review process, we were made aware of a paper by Giarelis et al [ 36 ] that is more rigorous in its performance evaluation than the papers included in this review. It was not found in our search, as it is not indexed on PubMed, and our Google and Google Scholar searches were limited to the first 50 results.…”

Section: Discussionmentioning

confidence: 99%

Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape

Chatterjee

Nardi

Oberije

et al. 2021

JPM

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Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was “covid-19 knowledge graph”. In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research.

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Section: Discussionmentioning

confidence: 99%

Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape

Chatterjee

Nardi

Oberije

et al. 2021

JPM

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“…Among the established work on link prediction, the studies closely relate to the work we demonstrate here are "Machine Learning within a Graph Database: A Case Study on Link Prediction for Scholarly Data" [9] and "On the utilization of structural and textual information of a scientific knowledge graph to discover future research collaborations: a link prediction perspective" [10].…”

Section: Related Workmentioning

confidence: 99%

“…In "On the Utilization of Structural and Textual Information of a Scientific Knowledge Graph to Discover Future Research Collaborations: A Link Prediction Perspective" [10], the prime target was to utilize the limitation of structured and unstructured data in graphs. To resolve the issue, both structured and unstructured textual data were represented in a scientific knowledge graph.…”

Section: Related Workmentioning

confidence: 99%

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A Comparative Study between Graph Database and Traditional Approach to forecast Coauthor Link Prediction based on Machine Learning Models

Huq¹,

Jennifer²,

Partho³

et al. 2022

Preprint

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In the modern world where research is taking a huge leap, the collaboration network between authors is also expanding, increasing the probability of different authors coming together to work on the same project, same research paper making them co-authors. In coauthorship, link prediction is used to anticipate new interactions between its members that are likely to occur in the future. Researchers have concentrated their efforts on studying and suggesting methods for providing effective reviews for authors who can collaborate on a scientific endeavor. In order to provide a precise link prediction, a graph database approach is proposed in this paper using nodes to determine most possible co-authors in future. In order to forecast the connections, we preprocessed the data set for the maximum relative contents. A supervised learning approach is used to execute the solution, which includes random forest classifier and logistic regression. The first findings of our technique reveal that the total of two author node’s research collaboration indices has the greatest influence on the performance of supervised link prediction than that of the traditional approach, which stimulates us to conduct further study on employing such a forecast.

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“…Algunos trabajos proponen modelos para predecir futuras colaboraciones en investigación y realizan experimentaciones con la base de datos CORD-19 [90], [91]. En otra dirección, se plantean modelos para el análisis de patrones temporales de redes de colaboración científica [92] o relaciones entre campos de conocimiento [93].…”

Section: Artículos Más Citadosunclassified

Tendencias de investigación en los estudios de colaboración científica sobre el COVID-19

Pérez-Martelo,

Jaime,

Mejía-Quiroga

et al. 2023

Rev. UIS ing.

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Este artículo tiene como propósito identificar las tendencias de investigación en los estudios de colaboración científica sobre el COVID-19, y la presencia que han tenido en esas contribuciones distintos países, enfoques, áreas de conocimiento y tópicos. Se utilizan métodos bibliométricos, de análisis de redes sociales y de contenido con un corpus de 63 documentos indexados en Scopus, publicados entre enero del 2020 y junio del 2021. Se encuentra que los países con un mayor número de publicaciones son el Reino Unido y Estados Unidos, seguidos de China y Canadá. En cuanto a las revistas, resaltan las del área de medicina. Se evidencia que las Ciencias Sociales están poco representadas en los contextos de colaboración analizados, y predomina la bibliometría como enfoque metodológico. Los estudios sobre prácticas de colaboración tienen menor presencia, y se concentran principalmente en análisis de experiencias y retos de cooperación desde las áreas de la salud para enfrentar la pandemia.

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On the Utilization of Structural and Textual Information of a Scientific Knowledge Graph to Discover Future Research Collaborations: A Link Prediction Perspective

Cited by 11 publications

References 22 publications

Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape

Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape

A Comparative Study between Graph Database and Traditional Approach to forecast Coauthor Link Prediction based on Machine Learning Models

Tendencias de investigación en los estudios de colaboración científica sobre el COVID-19

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