What is semantic gap? Why it exists in AI
Any ML model assumes a particular representation of the data, and connects bits of information only within this representation.
For example a linear regression model assumes a linear relation between the features and the target variable. A Naive Bayes model assumes that the features are independent of each other. And these are only the most obvious kind of simplifications made by ML models.
Naturally this results in different models representing data differently. Any semantic interpretation based on some model outcome is potentially biased by the assumptions made by the model.