A Cognitive Approach to Modelling Semantic Sensor Web Solutions

Abstract

Semantic sensor solutions are characterized by a lack of consensus on what features make sensor networks semantic, and what services a semantic layer should provide. Although authors emphasize the fact that humans outperform software in managing inconsistent knowledge and unreliable sensor data, no attempt has been made so far to construct a model of semantic sensor networks inspired by human cognition. The aim of the present paper is to investigate whether the structure and organisation of concepts and meaning in the human mind (as proposed by cognitive linguists and psycholinguists) can serve as a model for constructing ontologies and knowledge representations for the semantic sensor web (hereafter SSW). We also aim to show how multimodal sensory data can be integrated with these representations based on contemporary findings in human perception. We suggest that SSW solutions based on cognitive mechanisms and psychologically plausible knowledge representations overcome the challenges that handling of fuzzy data and inconsistent information generates at present.