Call for npj Artificial Intelligence Themed Collection Papers
Neurosymbolic AI
Guest Editors:
Rui Mao, Nanyang Technological University, Singapore
Erik Cambria, Nanyang Technological University, Singapore
Björn W. Schuller, Technical University of Munich, Germany
Corresponding Guest Editor:
Rui Mao
Background
Neurosymbolic AI is an emerging approach in the field of artificial intelligence that combines the strengths of symbolic reasoning (e.g., deep neural networks and large language models) and symbolic reasoning (e.g., semantic networks and knowledge graphs). This hybrid method seeks to address some of the limitations of traditional AI systems by integrating the learning capabilities of neural networks with the structured reasoning capabilities of knowledge representations. It represents a promising direction for AI research and development, aiming to unify the strengths of neuro-driven learning and symbolic reasoning to create more powerful and interpretable AI systems.
Topics
This Collection welcomes the following topics, including but not limited to:
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Explainable AI
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Interpretable Machine Learning
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Commonsense Reasoning
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Natural Language Understanding and Generation
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Sentic Computing
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Cognitive Inspired Reasoning (e.g., System 1/System 2 integration)
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Multimodal Neurosymbolic Integration (e.g., vision language models)
Paper Submission
Please follow the steps detailed on this page to prepare your manuscript for submission. Submissions are handled via the Springer Nature SNAPP system. On the first page of the online submission system, under “Select article type:” select the option of your article type. Then when filling out the manuscript information, select this Collection from the Collection list on the “Details” tab. Authors should express their interest in the Collection in their cover letter.
Important Dates
Submission deadline: 12 September 2025
About the Guest Editors
RUI MAO is a Research Scientist, Lead Investigator at Nanyang Technological University, Singapore. He received his Ph.D. in Computing Science from the University of Aberdeen. His research interests include neurosymbolic AI, affective computing and cognitive computing. He and his founded company have developed the first neural network search engine (https://wensousou.com) for searching ancient Chinese poems by using modern language, and the MetaPro system (https://metapro.ruimao.tech) for linguistic and conceptual metaphor understanding. MetaPro has been employed for cognitive analysis in diverse domains. He served as Area Chair in COLING and EMNLP and Associate Editor in Expert Systems, IEEE Transactions on Affective Computing, Information Fusion and Neurocomputing.
ERIK CAMBRIA is a Professor at Nanyang Technological University, where he also holds the appointment of Provost Chair in Computer Science and Engineering, and Founder of several AI companies, such as SenticNet (https://business.sentic.net), offering B2B sentiment analysis services, and finaXai (https://finaxai.com), providing fully explainable financial insights. Prior to moving to Singapore, he worked at Microsoft Research Asia (Beijing) and HP Labs India (Bangalore), after earning his PhD through a joint program between the University of Stirling (UK) and MIT Media Lab (USA). Today, his research focuses on neurosymbolic AI for interpretable, trustworthy, and explainable affective computing in domains like social media monitoring, financial forecasting, and AI for social good. He is ranked in Clarivate's Highly Cited Researchers List of World's Top 1% Scientists, is recipient of many awards, e.g., IEEE Outstanding Early Career, was listed among the AI's 10 to Watch, and was featured in Forbes as one of the 5 People Building Our AI Future. He is an IEEE Fellow, serves as an Associate Editor for several leading AI journals, and actively participates in international conferences as a keynote speaker, program chair, and senior program committee member.
BJÖRN W. SCHULLER combines computer science with modern health care and medicine. The main focus lies in the acquisition, analysis, and interpretation of biosignals including in daily life, such as those generated in monitoring heart activity, metabolism, or neuronal activities. Additionally, acoustic, visual, and a variety of other parameters are also evaluated. The goal is prevention, diagnosis, as well as decision support and intervention through efficient, transparent, and trustworthy methods of current Artificial Intelligence. Prof. Schuller received his diploma (1999), doctoral degree (2006), and habilitation (2012), all in EE/IT from TUM where he became Full Professor of Health Informatics in 2023. Since 2013 he is also with Imperial College London now as Professor of Artificial Intelligence. Further, he is the co-founding CEO and current CSO of audEERING. Previous major stays include Full Professor at the University of Augsburg (2017-2023) and University of Passau (2014-2017), and Researcher at Joanneum Research in Graz (2012) and the CNRS near Paris (2009-2010).