Affiliations: [a] Librarian, Circuit Bench of Calcutta High Court at Jalpaiguri, Jalpaiguri – 735 131, West Bengal, India. (E): [email protected]
| [b] M.Phil Scholar, Department of Library and Information Science, University of Burdwan, Burdwan – 713 104, West Bengal, India. (E): [email protected]
| [c] Assistant Professor, Department of Library and Information Science, University of Burdwan, Burdwan – 713 104, West Bengal, India. (E): [email protected]
Abstract: The purpose of the study discussed in the present article is to demonstrate how to leverage the resource description framework to integrate static and dynamic data sources to speed-up data integration, unification, analytics, and sharing in any database. People who use the knowledge graph approach to make apps, integrate data, and examine it depend on its ability to connect scattered data and match complex patterns of long links in the data set. This research looked at several methods for creating database knowledge graphs. The strategy and technical mechanism are observed on command prompts of Ubuntu Operating System. This project runs using machine learning and template-based technology. Other approaches allowed for a semi-automated data integrity assessment. In graph databases, data is stored as graphs. This level of maturity enables failback and clustering in real-time. The information is stored in columns and rows rather than tables. As a result, complex relationships may be swiftly encoded and detected. To understand the significance of this research, we must examine it from a practical standpoint to create a database’s knowledge graph. A Knowledge Graph, which determines the authority of a given topic, is one strategy of presenting a subject directly to the user based on real-time sharing of data.
Keywords: Knowledge graph, Linked open data, Ontology, Database, RDF, Thesaurus
