A Human-Inspired Model to Represent Uncertain Knowledge in the Semantic Web

Abstract

One of the most evident and well-known limitations of the Semantic Web technology is its lack of capability to deal with uncertain knowledge. As uncertainty is often part of the knowledge itself or can be inducted by external factors, such a limitation may be a serious barrier for some practical applications. A number of approaches have been proposed to extend the capabilities in terms of uncertainty representation; some of them are just theoretical or not compatible with the current semantic technology; others focus exclusively on data spaces in which uncertainty is or can be quantified. Human-inspired models have been adopted in the context of different disciplines and domains (e.g. robotics and human-machine interaction) and could be a novel, still largely unexplored, pathway to represent uncertain knowledge in the Semantic Web. Human-inspired models are expected to address uncertainties in a way similar to the human one. Within this paper, we (i) briefly point out the limitations of the Semantic Web technology in terms of uncertainty representation, (ii) discuss the potentialities of human-inspired solutions to represent uncertain knowledge in the Semantic Web, (iii) present a human-inspired model and (iv) a reference architecture for implementations in the context of the legacy technology.