FUZZY POSITIONING MODELING OF NATURAL LANGUAGE LOCATION DESCRIPTION
Keywords: Uncertainty, Spatial relationships, Fuzzy sets, Membership function, Positioning locations, Natural language
Abstract. The development of cognitive technology and natural language understanding has made the interaction between humans and machines more mature. Natural language interactive location services through language or text are more in line with human cognitive habits, and it is the future development direction of location service techniques. Different from accurate GPS location, the uncertainty of human cognition causes the natural language position description is of uncertainty. How to take the uncertainty of position description into account to model it, and further establish a positioning computing framework that supports the expression of uncertainty, is a difficult problem in this field. At present, quantitative spatial relationship models can be well applied to navigation and positioning. However, these models cannot be directly transferred to deal with qualitative spatial relationships, such as positioning based on natural language descriptions. An effective solution is to establish a method to transform the qualitative natural language positioning into a quantitative one. To build this transformation, we proposed a fuzzy positioning model based on fussy mathematics theory and methods. The proposed model was validated using indoor and outdoor positioning experiments. The experiments showed that it could achieve a high positioning accuracy both indoor and outdoor.