Skip to Main Content
This paper proposes a low-level cognitive process model as a part of context-aware systems that can recognize situations in real space as well as provide suitable information or services to users based on the recognized situations. Generally context-aware systems need to deal with sensory data (non-symbolic processing) as well as language resources such as Web (symbolic processing). In this paper, we employ symbol grounding perspective and propose a neural network-based models for converting sensory data into symbolic representation. In addition, this paper exhibits an evidence for a basic question “why do we take a symbol grounding perspective?” in the view point of accessibility of symbolic representation inside systems.