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On the Analysis of Large Integrated Knowledge Graphs for Economics, Banking, and Finance
2022 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2022 ; 3135, 2022.
Article in English | Scopus | ID: covidwho-1871933
ABSTRACT
Knowledge graphs are being used for the detection of money laundering, insurance fraud, and other suspicious activities. Some recent work demonstrated how knowledge graphs are being used to study the impact of the COVID-19 outbreak on the economy. The fact that knowledge graphs are being used in more and more interdisciplinary problems calls for a reliable source of interdisciplinary knowledge. In this paper, we study the integration of knowledge graphs in the domains of economics, banking, and finance. Our integrated knowledge graph has over 610K nodes and 1.7 million edges. By performing statistical and graph-theoretical analysis, we demonstrate how the integration results in more entities with richer information. Its quality was examined by analyzing the subgraphs of the identity links and (pseudo-)transitive relations. Finally, we study the sources of error, and their refinement and discuss the benefit of our integrated graph. © 2022 Copyright for this paper by its authors.
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Collection: Databases of international organizations Database: Scopus Language: English Journal: ICDT Joint Conference, EDBT Year: 2022 Document Type: Article

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Collection: Databases of international organizations Database: Scopus Language: English Journal: ICDT Joint Conference, EDBT Year: 2022 Document Type: Article