Abstract: Curiosity-adaptive ambient semantic intelligence systems and methods provide an ontology-driven graph database comprising machine-processable semantic representations of intellectual resources in the form of Curiosity Attracting Pointers (CAPs). The system also utilizes formal semantics that enable the system to automatically add new CAPs through reasoners. The users score their CAP entries based on various parameters. Curiosity traits are derived through the analysis of selectivity patterns of the users. User-scoring and curiosity value of resources are used by a calculation module to assign an overall Curiosity Satisfaction Value, which enables the personalization of output graphs generated by the system for a given user.

Abstract: Curiosity-adaptive ambient semantic intelligence systems and methods provide an ontology-driven graph database comprising machine-processable semantic representations of intellectual resources in the form of Curiosity Attracting Pointers (CAPs). The system also utilizes formal semantics that enable the system to automatically add new CAPs through reasoners. The users score their CAP entries based on various parameters. Curiosity traits are derived through the analysis of selectivity patterns of the users. User-scoring and curiosity value of resources are used by a calculation module to assign an overall Curiosity Satisfaction Value, which enables the personalization of output graphs generated by the system for a given user.