Interpretable ontology meta-matching in the biomedical domain using Mamdani fuzzy inference

Ontology meta-matching techniques have been consolidated as one of the best approaches to face the problem of discovering semantic relationships between knowledge models that belong to the same domain but have been developed independently. After more than a decade of research, the community has reached a stage of maturity characterized by increasingly better results and aspects such as the robustness and scalability of solutions have been solved. However, the resulting models remain practically intelligible to a human operator. In this work, we present a novel approach based on Mamdani fuzzy inference exploiting a model very close to natural language. This fact has a double objective: to achieve results with high degrees of accuracy but at the same time to guarantee the interpretability of the resulting models. After validating our proposal with several ontological models popular in the biomedical field, we can conclude that the results obtained are promising.

Automated summary

Ontology meta-matching techniques have been established as effective in discovering semantic relationships between independently developed knowledge models. While the resulting models may be difficult for humans to interpret, a novel approach based on Mamdani fuzzy inference aims to improve interpretability by making the models more similar to natural language. Validation with popular ontological models in the biomedical field has shown promising results.