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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | KNOWLEDGE GRAPH CONSTRUCTION FROM UNSTRUCTED DATA |
| Authors Name: | Srinija Moogala , Varshitha Bekkam , Priyanka Gadhari , Sriram Udutha |
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IJRTI_204930
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| Published Paper Id: | IJRTI2507035 |
| Published In: | Volume 10 Issue 7, July-2025 |
| DOI: | |
| Abstract: | In this project, a knowledge graph is constructed from structured JSON data that is generated from unstructured text inputs. The process begins with users uploading raw text, which is systematically transformed into a standardized JSON format to organize the information into entities and properties. Once structured, this JSON data is further processed using natural language processing (NLP) techniques to extract meaningful connections, identify relationships between different entities, and build a semantic understanding of the data.The generated JSON acts as the backbone for constructing an interactive knowledge graph. Each node in the graph represents an extracted entity or concept, while the edges define the semantic relationships among them. To make the system more powerful and user- friendly, an NLP-driven query interface is integrated, allowing users to perform natural-language searches and explore specific areas of the knowledge graph based on their queries. For instance, users can input questions like "Show related topics for Artificial Intelligence," and the graph dynamically highlights the relevant nodes and connections, providing an intuitive and visual method of navigating complex information structures.The ultimate goal of the project is to enable users to seamlessly traverse large datasets, uncover hidden relationships, and gain insights that would be difficult to identify through traditional linear data formats. By combining structured JSON conversion, dynamic graph generation, and intelligent NLP querying, the project successfully bridges the gap between raw text and knowledge visualization, demonstrating a highly effective method for knowledge discovery and management. This approach finds applications across various fields such as research analysis, academic knowledge representation, educational tools, and corporate data management, where understanding and visualizing large sets of information is crucial. |
| Keywords: | Knowledge Graph, JSON Conversion, Text Processing, Natural Language Processing (NLP), Entity Extraction, Semantic Relationships, Query Interface. |
| Cite Article: | "KNOWLEDGE GRAPH CONSTRUCTION FROM UNSTRUCTED DATA", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.a300-a305, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507035.pdf |
| Downloads: | 000396 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2507035
Registration ID:204930
Published In: Volume 10 Issue 7, July-2025
DOI (Digital Object Identifier):
Page No: a300-a305 Country: Hyderabad/Medchal, Telangana, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2507035 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2507035 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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