A Map Ontology Driven Approach to Natural Language Traffic Information Processing and Services

Abstract

AbstractThis paper proposes a map ontology driven approach to natural language traffic information processing, and also describes its evaluation results. Traffic congestion is considered a major urban problem whose solution has long been sought for by engineers and researchers. Recently, the idea of gathering traffic information from mobile users via short message service appears promising. However, the traffic information is difficult to process to achieve a high accuracy because of its direct, indirect and connotative expressions. The proposed map ontology consists of a set of concepts, attributes, relations and constraints on them. The map ontology plays two key roles: 1) a basis for natural language traffic information analysis, and 2) a basis for user query analysis. In this paper we present the major information processing modules and services for mobile users. Experimental results show that the proposed method can improve the traffic information processing accuracy to 93%–95%.KeywordsMobile UserShort Message ServiceUser QuerySemantic KnowledgeTraffic InformationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.