Knowledge Graphs

Fensel, Dieter; Şimşek, Umutcan; Angele, Kevin; Huaman, Elwin; Kärle, Elias; Panasiuk, Oleksandra; Toma, Ioan; Umbrich, Jürgen; Wahler, Alexander

doi:10.1007/978-3-030-37439-6

Cited by 152 publications

(56 citation statements)

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“…Recently, knowledge graphs have been rapidly applied by businesses. Google and Bing's search engines are representative examples of applying knowledge graphs [16], while Google Home and Amazon Alexa use knowledge graphs in virtual assistant services [17].…”

Section: Related Workmentioning

confidence: 99%

Developing a Product Knowledge Graph of Consumer Electronics to Manage Sustainable Product Information

Kim

2021

Sustainability

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Transformational computing paradigms, such as artificial intelligence, home automation, and the Internet of Things, are being rapidly applied to consumer electronics products, thus aiding in the development of integrated and innovative features. Hence, ubiquitous computing and electronic devices are increasingly becoming essential to everyday life. In this context, a wide gulf often exists between the capabilities and technical features of consumer electronic devices and the consumers’ understanding of such devices and ability to operate them correctly and effectively. This study proposes a machine-readable knowledge model representing technical terms in product specifications along with a product knowledge graph to discover semantic relationships among various products. Formal concept analysis is applied to conceptually analyze the specification terms of heterogeneous electronic products and design a hierarchical knowledge structure of extracted concepts, to elaborate the proposed knowledge model.

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Section: Related Workmentioning

confidence: 99%

Developing a Product Knowledge Graph of Consumer Electronics to Manage Sustainable Product Information

Kim

2021

Sustainability

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Section: Introductionmentioning

confidence: 99%

“…Such a graph displayed in a visualisation tool gives an interactive overview of the nodes and connections between the concepts across the papers and allows one to move around and focus on what is interesting to the researcher. 8 The aim of this short report is to demonstrate the feasibility of using a network graph approach for rapid navigation of the COVID-19 literature in a publicly available format and to present an openly available tool for exploring a COVID-19 knowledge data set.…”

Section: Introductionmentioning

confidence: 99%

Network graph representation of COVID-19 scientific publications to aid knowledge discovery

Cernile

Heritage

Sebire

et al. 2021

BMJ Health Care Inform

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IntroductionNumerous scientific journal articles related to COVID-19 have been rapidly published, making navigation and understanding of relationships difficult.MethodsA graph network was constructed from the publicly available COVID-19 Open Research Dataset (CORD-19) of COVID-19-related publications using an engine leveraging medical knowledge bases to identify discrete medical concepts and an open-source tool (Gephi) to visualise the network.ResultsThe network shows connections between diseases, medications and procedures identified from the title and abstract of 195 958 COVID-19-related publications (CORD-19 Dataset). Connections between terms with few publications, those unconnected to the main network and those irrelevant were not displayed. Nodes were coloured by knowledge base and the size of the node related to the number of publications containing the term. The data set and visualisations were made publicly accessible via a webtool.ConclusionKnowledge management approaches (text mining and graph networks) can effectively allow rapid navigation and exploration of entity inter-relationships to improve understanding of diseases such as COVID-19.

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“…Such a graph displayed in a visualization tool gives an interactive overview of the nodes and connections between the concepts across the papers and allows one to move around and focus on what is interesting to the researcher. [4] The aim of this short report is to demonstrate the feasibility of using a network graph approach for rapid navigation of the COVID-19 literature in a publicly available format and to present an openly available tool for exploring a COVID-19 knowledge dataset.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network Graph Representation of COVID-19 Scientific Publications to Aid Knowledge Discovery

Cernile

Sebire

Gordon

et al. 2020

Preprint

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Introduction: Numerous scientific journal articles have been rapidly published related to COVID-19 making navigation and understanding of relationships difficult. Methods: A graph network was constructed from the publicly available CORD-19 database of COVID-19-related publications using an engine leveraging medical knowledgebases to identify discrete medical concepts and an open source tool (Gephi) used to visualise the network. Results: The network shows connections between disease, medication and procedures identified from title and abstracts of 195,958 COVID-19 related publications (CORD-19 Dataset). Connections between terms with few publications, those unconnected to the main network and those irrelevant were not displayed. Nodes were coloured by knowledgebase and node size related to the number of publications containing the term. The dataset and visualisations made publicly accessible via a webtool. Conclusion: Knowledge management approaches (text mining and graph networks) can effectively allow rapid navigation and exploration of entity interrelationships to improve understanding of diseases such as COVID-19.

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Knowledge Graphs

Cited by 152 publications

References 0 publications

Developing a Product Knowledge Graph of Consumer Electronics to Manage Sustainable Product Information

Developing a Product Knowledge Graph of Consumer Electronics to Manage Sustainable Product Information

Network graph representation of COVID-19 scientific publications to aid knowledge discovery

Network Graph Representation of COVID-19 Scientific Publications to Aid Knowledge Discovery

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