Cone-KG: A semantic knowledge graph with news content and social context for studying covid-19 news articles on social media
Source of Publication
2020 7th International Conference on Social Network Analysis, Management and Security, SNAMS 2020
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.
COVID-19 News, Knowledge Graphs, Semantic Graphs, Semantic Web, Social Data Analysis, Social Media
Al-Obeidat, Feras; Adedugbe, Oluwasegun; Hani, Anoud Bani; Benkhelifa, Elhadj; and Majdalawieh, Munir, "Cone-KG: A semantic knowledge graph with news content and social context for studying covid-19 news articles on social media" (2020). All Works. 4191.
Indexed in Scopus
Open Access Type
Bronze: This publication is openly available on the publisher’s website but without an open license