An ontological engineering approach for automating inspection and quarantine at airports

Customs and quarantine departments are applying information systems to automate their inspection processes and improve their inspection efficiency and accuracy. The product codes from the Harmonized System (HS codes) are the essential elements of the system's integration, automation and intelligence. The identified HS codes are well-accepted and precise product references used by customs authorities, to match applicable policies to the products being inspected and taxed. Domain ontology for importing and exporting industry can be used to acquire HS codes for given products, and is a prerequisite for an integrated and intelligent automated inspection system. The authors have proposed and implemented an importing and exporting domain ontology. The ontology is composed of an integrated and comprehensive knowledge base derived from static dictionaries and the HS specification, and dynamic processing data. Based on this ontology, a reasoning engine is developed to generate HS codes intelligently for the given product names. Information systems can use the engine to get HS codes for submitted products and find applicable policies automatically. The ontology and the engine have been implemented in a Java-based platform and published as a HS Web service. In this paper, knowledge structure, reasoning mechanism and implementation details for the domain ontology and reasoning engine are presented. A test bed in the application environment has been conducted and experimental results have been obtained. The ontology and the service have the potential to be widely used by authorities and international traders of importing and exporting industry around the world.

[1]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[2]  Takahiro Kawamura,et al.  Semantic Matching of Web Services Capabilities , 2002, SEMWEB.

[3]  Mark Klein,et al.  Towards High-Precision Service Retrieval , 2002, SEMWEB.

[4]  Trevor J. M. Bench-Capon,et al.  A Comparison of Four Ontologies for the Design of Legal Knowledge Systems , 1998, Artificial Intelligence and Law.

[5]  Amit P. Sheth,et al.  Semantic Interoperability and Integration 3 S-Match : an algorithm and an implementation of semantic matching , 2005 .

[6]  Vasant Honavar,et al.  Ontology-Driven Information Extraction and Knowledge Acquisition from Heterogeneous, Distributed, Autonomous Biological Data Sources , 2001 .

[7]  Fausto Giunchiglia,et al.  S-Match: an Algorithm and an Implementation of Semantic Matching , 2004, ESWS.

[8]  Pascale Fung,et al.  Creating a Bilingual Ontology: A Corpus-Based Approach for Aligning WordNet and HowNet , 2002 .

[9]  David W. Embley,et al.  Towards Ontology Generation from Tables , 2005, World Wide Web.

[10]  Erhard Rahm,et al.  Similarity flooding: a versatile graph matching algorithm and its application to schema matching , 2002, Proceedings 18th International Conference on Data Engineering.

[11]  Zhang Wei-ming,et al.  Ontology-based information retrieval model for the semantic Web , 2005, 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service.

[12]  Bob J. Wielinga,et al.  KADS: a modelling approach to knowledge engineering , 1992 .

[13]  Guus Schreiber,et al.  The Semantic Web – ISWC 2004 , 2004, Lecture Notes in Computer Science.

[14]  Mark Klein,et al.  Massachusetts Institute of Technology Abraham Bernstein University of Zurich Toward High-Precision Service Retrieval , 2022 .

[15]  Wei Xie,et al.  Inspection-oriented coding service based on machine learning and semantics mining , 2006, Expert Syst. Appl..

[16]  André Valente,et al.  Types and Roles of Legal Ontologies , 2003, Law and the Semantic Web.

[17]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[18]  Bob J. Wielinga,et al.  Using explicit ontologies in KBS development , 1997, Int. J. Hum. Comput. Stud..

[19]  Shiwen Yu,et al.  The specification of the Chinese Concept Dictionary , 2003, J. Chin. Lang. Comput..

[20]  Jun-feng Song,et al.  Ontology-Based Information Retrieval Model for the Semantic Web , 2005, EEE.

[21]  George A. Miller,et al.  Introduction to WordNet: An On-line Lexical Database , 1990 .

[22]  Mark A. Musen,et al.  PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment , 2000, AAAI/IAAI.

[23]  Erhard Rahm,et al.  COMA - A System for Flexible Combination of Schema Matching Approaches , 2002, VLDB.

[24]  Pedro M. Domingos,et al.  Learning to match ontologies on the Semantic Web , 2003, The VLDB Journal.

[25]  George A. Miller.  WordNet: A Lexical Database for English , 1992, HLT.

[26]  Tanveer F. Syeda-Mahmood,et al.  Searching service repositories by combining semantic and ontological matching , 2005, IEEE International Conference on Web Services (ICWS'05).

[27]  Hsueh-Foo Lin,et al.  Event-based ontology design for retrieving digital archives on human religious self-help consulting , 2005, 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service.

[28]  François Bry,et al.  Reasoning on the semantic web: beyond ontology languages and reasoners , 2005 .

[29]  Erhard Rahm,et al.  Generic Schema Matching with Cupid , 2001, VLDB.