Authors : G. Newton; M. Shajith Revanth; P. Minish; K. Tharuni Reddy

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/mr3jm44h

Scribd : https://tinyurl.com/3mz7wu6t

DOI : https://doi.org/10.38124/ijisrt/25may2182

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Abstract : This project presents a client-side, knowledge graph system that dynamically extracts and visualizes semantic relationships from unstructured natural language input. Unlike traditional keyword-based methods, this system uses lightweight Natural Language Processing (NLP) to interpret the contextual meaning of user queries. Unlike traditional keyword-based methods, this system uses lightweight Natural Language Processing (NLP) to interpret the contextual meaning of user queries. It identifies key entities and their relationships through in-browser logic and parsing, transforming them into nodes and edges rendered instantly as a knowledge graph. Built with React, TypeScript (TSX), and ReactFlow, the interface offers an intuitive experience for exploring semantic structures without relying on any backend or external database. This fully browser-based architecture ensures fast, private, and responsive interaction. The system is well- suited for applications such as semantic search, concept discovery, educational tools, and interactive data exploration—enabling users to better understand and navigate the relationships embedded in text.

Keywords : Knowledge Graph, NLP, Semantic Parsing, Entity Extraction, Relationship Mapping, React, Typescript, Client-Side Processing, Graph Visualization, Unstructured Text, Dynamic UI, Contextual Analysis, Backend-Free Architecture.

References :

1. M. Reimers, J. Dodge, J. Gilmer, M. D. Hoffman, and M. Dredze, "Sentence-level representations for document classification," Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, USA, 2019, pp. 700–707, doi: 10.18653/v1/N19-1070.
2. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, 2019, pp. 4171–4186, doi: 10.48550/arXiv.1810.04805.
3. N. Reimers and I. Gurevych, "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks," Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, 2019, pp. 3982–3992, doi: 10.48550/arXiv.1908.10084.
4. C. C. Aggarwal and C. X. Zhai, "A Survey of Text Clustering Algorithms," in Mining Text Data, Boston, MA: Springer, 2012, pp. 77–128, doi: 10.1007/978-1-4614-3223-4_4.
5. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, 1996, pp. 226– 231.
6. J. Han, J. Pei, and M. Kamdar, Data Mining: Concepts and Techniques, 4th ed., Elsevier, 2022, pp. 493–508, ISBN: 978-0-12-818148-7.
7. J. Liu, X. Shen, W. Pan, and B. Liu, "Document clustering via topic modeling using BERT embeddings," Proceedings of the 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE), Beijing, 2020, pp. 188–192, doi: 10.1109/ICAICE51518.2020.00047.
8. W. X. Zhao, Y. Guo, and Y. He, "A Comparative Study of Deep Learning Models for Semantic Document Clustering," Information Sciences, vol. 576, pp. 55–72, 2021, doi: 10.1016/j.ins.2021.07.055.
9. S. K. Singh and R. Sharma, "Semantic clustering using transformer embeddings for document organization," Journal of Machine Learning and Data Mining, vol. 10, no. 3, pp. 145–160, 2023.

[10].    T. Wang and L. Zhang, "Enhancing document clustering performance with contextual embeddings and density-based algorithms," International Journal of Data Science, vol. 8, no. 1, pp. 30–42, 2022.