Inspection-oriented coding service based on machine learning and semantics mining

被引:2
作者
Li, Yinsheng [1 ]
Ma, Zhanxin
Me, Wei
Laing, Chris
机构
[1] Fudan Univ, Software Sch, Shanghai 200433, Peoples R China
[2] Shanghai Int Airport Entry Exit Inspect & Quarant, Shanghai 201202, Peoples R China
[3] Northumbria Univ, Sch Comp Engn & Informat Sci, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England
关键词
HS codes; semantics mining; knowledge database; web services;
D O I
10.1016/j.eswa.2006.01.019
中图分类号
TP18 [人工智能理论];
学科分类号
081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];
摘要
HS codes have been adopted by the majority of countries as being the basis for import and export inspection and the generation of trade statistics. Customs authorities and international traders need a HS code query tool to make their processing efficient and automatic. Since HS codes are identified at 5-7 levels of classification, then any intelligent coding service will need to combine a knowledge database, with the techniques of data mining, machine learning and semantics reasoning. In this paper, the authors propose a comprehensive solution for such a coding service. The architecture, related techniques, technical solution and implementation considerations for the proposed system have been provided. Several of the proposed functions and implementation techniques have been developed and deployed by the Shanghai International Airport Entry-Exit Inspection and Quarantine Bureau. The coding service has been published as a Web service, and has the potential to be widely used by authorities and international traders around the world. The proposed system may also be appropriate for other applications that relate to code or classification processes, such as RFID-based or product ontology based applications. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码:835 / 848
页数:14
相关论文
共 26 条
[1]
Agrawal R., 1993, SIGMOD Record, V22, P207, DOI 10.1145/170036.170072
[2]
Apte C., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, P23
[3]
BONNIE DJ, 2000, LARGE SCALE CONSTRUC
[4]
A region-based fuzzy feature matching approach to content-based image retrieval [J].
Chen, YX ;
Wang, JZ .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2002, 24 (09) :1252-1267
[5]
COHEN WW, 1996, ADV INDUCTIVE LOGIC, P124
[6]
GRACE DW, 2005, BOOSTING NAMED ENTIT
[7]
GRACE MC, 2005, CREATING BILINGUAL O
[8]
GREVENSTETTE JJ, 1990, MACH LEARN, V4, P137
[9]
HAN J, 2000, P 2000 ACM SIGMOD IN, P1, DOI DOI 10.1145/342009.335372
[10]
HAYES P, 1990, 2 ANN C INN APPL ART