Date of this Version
Winter 9-15-2026
Document Type
Article
Citation
APA Citation
Abstract
Knowledge graphs with graph databases such as Neo4j provide tremendous capabilities for semantic mapping and discovery in Library and Information Science. This paper proposes a method to construct a domain-specific knowledge graph from journal articles in LIS. Researchers are interested in the semantic connection between authors, topics, citations, and concepts. Using Neo4j, the team implements entity extraction, ontology modeling, relationship mapping, and advanced querying techniques will enable better information retrieval and knowledge discovery. A realistic knowledge graph is created from about 27825 current LIS articles from 100 LIS journals from 2020 to 2026. Interactive Neo4j dashboards are used to visualize the graph. Findings highlight new trends in research and relationships that have not been previously used in this field. Knowledge graphs surpass traditional databases in the fields of semantic search, recommendation systems, and bibliometric analysis. The present article shows the great power of knowledge graphs based on Neo4j, which can be used in digital libraries, scholarly communication, and intelligent information services.
