Neo4J Bloom: Illuminating Knowledge Graphs for Smarter Data Discovery
DOI:
https://doi.org/10.48165/lis.3020.12.01.04Keywords:
Knowledge Graph, Neo4j Bloom, Information Visualization, Iinformation Retrieval, Graph Databases, Data DiscoveryAbstract
The paper aims to investigate user-friendly information visualization across various databases and text. It explores scientific information access systems and bibliometrics visualization interfaces to increase semantic facet similarities to big data management in digital library environments. The workflow proposes the integration of domain-specific data into a Neo4j database, followed by the use of Bloom to perform dynamic context-sensitive visualization based on LLM, GenAI, and GraphRAG. Text demonstrates Bloom's natural language processing features, design, and convenient interface. The system has also explored offline installation and configuration for knowledge visualization in libraries and information centers. The system provides a sophisticated and innovative integrated prototyping framework, Neo4j Bloom, capable of being a powerful, codeless tool that can be used to explore Neo4j graph data in an interactive and visual manner. Bloom design is well-thought, and it promotes teamwork due to simplification of the complex query operations. Smarter data discovery promotes cross-functional team collaboration, effectively democratizing advanced graph analytics by opening it to a wider audience and making it easier to share. Transparent access to knowledge graphs makes it an invaluable tool for both analysts and decision-makers that utilize bibliometric data visualization. The originality of the integrated framework for implementing interactive visualizations in practical knowledge graph applications and discusses best practices aimed at maximizing Bloom's effectiveness in supporting data-driven scenarios for increasing meaningful insights through intuition. It is straightforward to grasp the performance considerations and deployment challenges linked to such information retrieval systems.
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