Abstract
Among challenges for eXplainable Artificial Intelligence (XAI) is explanation generation. In this paper we put the stress on this issue by focusing on a semantic representation of the content of an explanation that could be common to any kind of XAI. We investigate knowledge representations, and discuss the benefits of conceptual graph structures for being a basis to represent explanations in AI.
Cite
CITATION STYLE
Baaj, I., Poli, J. P., & Ouerdane, W. (2019). Some insights towards a unified semantic representation of explanation for explainable artificial intelligence (XAI). In NL4XAI 2019 - 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence, Proceedings of the Workshop (pp. 14–19). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w19-8404
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