The Data-Driven Decision Making (D3M) Lab is pleased to announce that graduate student Sravani Malla received Best Paper recognition for her research titled “Enhancing Knowledge Base Completion for Long-tail Entities through Zero-shot Learning” at the 8th International Conference on Innovative Computing and Communication (ICICC 2025). The paper was subsequently published in the conference proceedings and is available through Springer Nature.
Publication Information
Title: Enhancing Knowledge Base Completion for Long-tail Entities through Zero-shot Learning
Conference: 8th International Conference on Innovative Computing and Communication (ICICC 2025)
Recognition: Best Paper Award
Publication: Springer Nature Conference Proceedings
DOI: https://doi.org/10.1007/978-981-96-7137-3_37
Knowledge graphs play a critical role in modern artificial intelligence applications, powering search engines, recommendation systems, question-answering platforms, and large language models. However, many knowledge base completion methods struggle when dealing with long-tail entities, entities that appear infrequently in training data and are therefore often overlooked by traditional machine learning approaches.
To address this challenge, Sravani’s research introduced a novel two-stage framework that leverages zero-shot learning for Knowledge Base Completion (KBC). The proposed approach enables AI systems to make more accurate predictions for underrepresented entities and previously unseen relationships, helping improve the completeness and reliability of knowledge graphs.
The project was conducted through collaboration among Sravani Malla, Dr. Hien Nguyen, Dr. Sayed Mohsin Reza, and Dr. Thien Khai Tran. The work reflects the D3M Lab’s continued commitment to advancing research in artificial intelligence, machine learning, knowledge representation, and natural language processing.
Receiving the Best Paper Award at an international conference is a significant accomplishment and recognizes both the technical contribution and the broader impact of the research on the AI community.
Dr. Sayed Reza congratulates Sravani Malla on this outstanding achievement and recognizes the dedication, innovation, and scholarly excellence demonstrated throughout the research process.