Abstract: Time-series data and domain knowledge rules governing interpretation of the time-series data in building a semiotic model of a user are accessed. An experience event is extracted from the time-series data based on the rules. A triadic data structure is generated based on the event to include an object element, a sign element, and an interpretant element. The interpretant element is determined based on an observation associated with the rules. An object of interest that appears in the environment of the user during the experience event is identified based on the time-series data. The object element of the triadic data structure is determined based on the object of interest. The triadic data structure is added to the semiotic model as a hypothesis. Once validated, the triadic data structure represents an interpretive habit of the user.