Methods for Integrating Information in Data, Language Models, and Knowledge Graphs for Neurosymbolic Learning and Reasoning
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".
Deadline for manuscript submissions: 30 April 2025 | Viewed by 982
Special Issue Editors
Interests: artificial intelligence; machine learning; social good
Special Issue Information
Dear Colleagues,
Neurosymbolic learning and reasoning systems aim to unify the strengths of symbolic reasoning and neural networks. This area of research focuses on integrating various forms of information—from unstructured data and language models to structured knowledge graphs—into a cohesive framework that enhances learning and reasoning capabilities. The core challenge lies in developing methods that effectively combine these diverse information sources, enabling declarative and procedural knowledge to be incorporated into neural architectures or vice versa. Key areas of exploration include the embedding of knowledge graphs within neural networks, synergizing symbolic reasoning with language models, and building unified representations of unstructured data and structured knowledge to enhance neurosymbolic learning methods.
Prof. Dr. Kaushik Roy
Prof. Dr. Valerie L. Shalin
Guest Editors
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Keywords
- language models
- knowledge graphs
- neurosymbolic AI
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