Title

Cone-KG: A semantic knowledge graph with news content and social context for studying covid-19 news articles on social media

Document Type

Conference Proceeding

Source of Publication

2020 7th International Conference on Social Network Analysis, Management and Security, SNAMS 2020

Publication Date

12-14-2020

Abstract

Semantic knowledge graphs provide very significant benefits for structuring and analysing huge amounts of aggregated data across diverse heterogeneous sources. Beyond quick and efficient data query and analysis, they facilitate inference from data and generation of insights for several purposes. With the multi-faceted global challenges posed by the COVID-19 pandemic, this research focused on the use of a semantic knowledge graph to model, structure and store COVID-related news articles centrally and semantically towards knowledge discovery, knowledge acquisition and advanced data analytics for understanding varying metrics relating to the virus towards curbing its spread. The semantic knowledge graph provides a platform for researchers, data analysts and data scientists across societal sectors to investigate and recommend strategies towards addressing the challenges it poses to the global society.

ISBN

9780738111803

Disciplines

Computer Sciences

Keywords

COVID-19 News, Knowledge Graphs, Semantic Graphs, Semantic Web, Social Data Analysis, Social Media

Scopus ID

85100870803

Indexed in Scopus

yes

Open Access

no

Open Access Type

Bronze: This publication is openly available on the publisher’s website but without an open license

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