Neuro-Symbolic AI for Deep Analysis of Social Media Big Data

dc.contributor.authorKhandelwal, Vedant
dc.contributor.authorGaur, Manas
dc.contributor.authorKursuncu, Ugur
dc.contributor.authorShalin, Valerie
dc.contributor.authorSheth, Amit P.
dc.date.accessioned2025-03-11T14:42:45Z
dc.date.available2025-03-11T14:42:45Z
dc.date.issued2024-09-13
dc.description2024 IEEE International Conference on Big Data, 2024
dc.description.abstractThis 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.
dc.description.urihttps://scholarcommons.sc.edu/csce_facpub/317/
dc.format.extent4 pages
dc.genreconference papers and proceedings
dc.genrepreprints
dc.identifierdoi:10.13016/m2m7bb-uy7n
dc.identifier.urihttp://hdl.handle.net/11603/37769
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectUMBC Ebiquity Research Group
dc.titleNeuro-Symbolic AI for Deep Analysis of Social Media Big Data
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-5411-2230

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