Abstract: The exponential growth of digital information presents critical challenges for efficient data management, as conventional manual curation methods remain slow, error-prone, and unable to adapt to evolving data streams. This paper presents InfoCore: AI-Driven Entity Deduplication and Event Categorization, an automated framework that leverages artificial intelligence to identify and remove redundant news articles while classifying them by event. Focusing on the political news domain, the system integrates Natural Language Processing, machine learning, and clustering techniques to enhance information retrieval and reduce redundancy. News content is collected via the Newspaper3k library and processed through tokenization, normalization, and entity extraction. Transformer-based models enable named entity recognition, while LLaMA-based large language models, TensorFlow, and PyTorch support text classification and event categorization. Empirical evaluation demonstrates InfoCore’s capacity to detect duplicates and achieve precise event classification with high scalability. The paper contributes a domain-independent architecture for automated data curation and a replicable workflow that improves efficiency and accuracy in large-scale information systems. The results highlight InfoCore’s potential to advance data management practices and inform the design of intelligent, scalable frameworks for handling unstructured digital content.

Rohail Qamar, Raheela Asif, Abdul Karim Kazi, Muhammad Ali and Muhammad Mustafa. “InfoCore: AI Driven Named Entity Deduplication and Event Categorization”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161156

@article{Qamar2025,
title = {InfoCore: AI Driven Named Entity Deduplication and Event Categorization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161156},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161156},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Rohail Qamar and Raheela Asif and Abdul Karim Kazi and Muhammad Ali and Muhammad Mustafa}
}