Document Type
Presentation
Abstract
This tutorial introduces a neuro-symbolic AI framework to analyze big data from social media platforms. Integrating human-curated knowledge through symbolic AI with the pattern recognition capabilities of neural networks enhances the adaptability and efficiency of traditional neural network approaches. Knowledge-guided zero-shot learning techniques enable swift adaption to new linguistic contexts and emerging events [6]. Participants will explore how to design, develop, and utilize these models in specific domains, such as public health surveillance, that require dynamic adaptation to new terminologies. This session The tutorial aims to equip attendees with practical skills and a deep understanding of how to apply neuro-symbolic AI to manage and analyze large-scale social media datasets effectively.
Publication Info
Preprint version 2024 IEEE International Conference on Big Data, 2024.
Rights
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
© 2024 Vedant Khandelwal, Manas Gaur, Ugur Kursuncu, Valerie Shalin, and Amit Sheth. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This tutorial is available through the University of South Carolina Scholar Commons for academic and non-commercial use.
APA Citation
Khandelwal, V., Gaur, M., Kursuncu, U., Shalin, V., & Sheth, A. (2024, December). Neuro-Symbolic AI for Deep Analysis of Social Media Big Data (Tutorial). In IEEE International Conference on Big Data.
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