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Transparent, explainable reasoning pathways for knowledge representation

Establish transparent, explainable reasoning pathways in Neuro-Symbolic knowledge representation that make intermediate symbolic operations and neural contributions traceable and understandable to users.

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Background

While the review highlights progress in representing complex relationships and integrating personalized knowledge, the authors emphasize that achieving fully transparent and explainable reasoning pathways remains an open question, especially for robust, human-like knowledge representation.

References

Open research questions remain around how Neuro-Symbolic AI can enhance the dynamic interpretation and manipulation of symbols, develop meta-cognitive abilities to monitor and adjust reasoning processes, and ensure transparent, explainable reasoning pathways for more human-like, adaptable, and robust knowledge representation.

Neuro-Symbolic AI in 2024: A Systematic Review (2501.05435 - Colelough et al., 9 Jan 2025) in Section 4.1 Knowledge Representation