Abstract
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.
The full text of this article is available as a PDF.
Download PDF- Liu, R., Gao, M., Wang, L., Wang, X., Xiang, Y., Zhang, A., Xia, J., Chen, Y., & Chen, S. (2022). Interactive extended reality techniques in information visualization. IEEE Transactions on Human-Machine Systems, 52(6), 1338–1351. https://doi.org/10.1109/THMS.2022.3211317
- Saad, M., Zhang, Y., Tian, J., & Jia, J. (2023). A graph database for life cycle inventory using Neo4j. Journal of Cleaner Production, 393, 136344. https://doi.org/10.1016/j.jclepro.2023.136344
- Bharti, S., Lo, D. C.-T., & Shi, Y. (2024). Enhancing contextual understanding in knowledge graphs: Integration of quantum natural language processing with Neo4j LLM knowledge graph. 2024 IEEE International Conference on Big Data (BigData), 8628–8630. https://doi.org/10.1109/BigData62323.2024.10826002
- Sima, H., Gao, M., & Liu, L. (2025). A real-time semantic segmentation network leveraging spatial and contextual features for enhanced scene understanding. Intelligent Systems with Applications, 27, 200542. https://doi.org/10.1016/j.iswa.2025.200542
- He, Z., Yu, J., & Guo, B. (2022). Execution time prediction for Cypher queries in the Neo4j database using a learning approach. Symmetry, 14(1), 55. https://doi.org/10.3390/sym14010055
- Wang, S., Li, W., & Gu, Z. (2023). GEOGRAPHVIZ: Geographically constrained 3D force-directed graph for knowledge graph visualization. Transactions in GIS, 27(4), 931–948. https://doi.org/10.1111/tgis.13053
- Chen, Y., & Xing, X. (2022). Constructing dynamic knowledge graph based on ontology modeling and Neo4j graph database. 2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD), 522–525. https://doi.org/10.1109/ICAIBD55127.2022.9820199
- Ye, L., Yimeng, Y., Wei, C., & Chuanning, L. (2024). Construction and application of user check-in spatiotemporal knowledge graph based on Neo4j. Procedia Computer Science, 242, 609–616. https://doi.org/10.1016/j.procs.2024.08.117
- Cerjan, M., Rabuzin, K., & Šestak, M. (2024). Implementing domains in Neo4j. International Journal of Intelligent Information and Database Systems, 16(3), 258–285. https://doi.org/10.1504/IJIIDS.2024.137683
- Tian, L., Zhou, X., Wu, Y.-P., Zhou, W.-T., Zhang, J.-H., & Zhang, T.-S. (2022). Knowledge graph and knowledge reasoning: A systematic review. Journal of Electronic Science and Technology, 20(2), 100159. https://doi.org/10.1016/j.jnlest.2022.100159
- Ehlers, H., Brich, N., Krone, M., Nöllenburg, M., Yu, J., Natsukawa, H., Yuan, X., & Wu, H.-Y. (2025). An introduction to and survey of biological network visualization. Computers & Graphics, 126, 104115. https://doi.org/10.1016/j.cag.2024.104115
- Huseynli, A., & Akcayol, M. A. (2021). Bloom filter based graph database CRUD optimization for stream data. 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 1056–1061. https://doi.org/10.1109/IDAACS53288.2021.9661054
- Li, H., Appleby, G., Brumar, C. D., Chang, R., & Suh, A. (2024). Knowledge graphs in practice: Characterizing their users, challenges, and visualization opportunities. IEEE Transactions on Visualization and Computer Graphics, 30(1), 584–594. https://doi.org/10.1109/TVCG.2023.3326904
- Drakopoulos, G., Gourgaris, P., & Kanavos, A. (2020). Graph communities in Neo4j: Four algorithms at work. Evolving Systems, 11(3), 397–407. https://doi.org/10.1007/s12530-018-9244-x
- Mathew, A. B. (2018). Efficient query retrieval from social data in Neo4j using Lindex. KSII Transactions on Internet and Information Systems, 12(5), 2211–2232. https://itiis.org/digital-library/manuscript/2015
Looking up citing articles…
Copyright © 2026 LIS TODAY