Temporal knowledge-based explanations for inductive reasoning: a mHealth case example
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1272Keywords:
Knowledge Representation, Explicability, mHealthAbstract
Objective: Investigate the generation of explanations for inductive systems using a unified ontology that represents the health status of mobile users. This ontology serves as a priori knowledge, facilitating the generation of explanations. Method: We examined 24 Mobile health (mHealth) apps to develop this ontology, emphasizing extensions that consider temporal aspects. Such aspects are usually neglected in health representations, given the limitation of ontologies in modelling ternary temporal relations. After that, we applied different configurations of an inductive algorithm that receives this ontology as input, generating explanations for their inductive outcomes. Results: Experiments show that the temporal model structure affects the readability of explanations. Moreover, experiments emphasize the tradeoff between accuracy and generalization power. Conclusion: Temporal extensions improve the expressiveness of explanations since temporal relations and concepts are explored to better contextualize temporal-based facts associated with inductive outcomes.
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