A map ontology driven approach to natural language traffic information processing and services

Authors:
Hongwei Qi;Yuguang Liu;Huifeng Liu;Xiaowei Liu;Yabo Wang;Toshikazu Fukushima;Yufei Zheng;Haitao Wang;Qiangze Feng;Han Lu;Shi Wang;Cungen Cao
Affiliations:
NEC Laboratories, China, Beijing, China;NEC Laboratories, China, Beijing, China;NEC Laboratories, China, Beijing, China;NEC Laboratories, China, Beijing, China;NEC Laboratories, China, Beijing, China;NEC Laboratories, China, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
Year:
2006

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Abstract

This 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%.