本體論及推論應用於資料語意偵錯之研究

Detecting Semantic Data Errors by Adopting Ontology and Reasoning

10.6382/JIM.200907.0029

劉艾華(Ay-Hwa Andy Liou)；王茂年(Maw-Nian Wang)；林英潔(Ying-Jie Lin)

語意網路 ； 本體論 ； 專家系統 ； Jess ； 語意偵錯 ； Semantic Web ； Ontology ； Expert System ； Jess ； Semantic Detect

資訊管理學報

16卷3期（2009 / 07 / 01）

29 - 54

繁體中文

隨著網際網路的快速發展，電子商務儼然已成為傳統店面行銷外的另一個新興通路，由於網站資料呈現的多樣化、不受限制的交易時間及交易成本的低廉化，有愈來愈多的行業透過網路行銷，但網路交易的糾紛卻時有所聞。鑒於大多數網站其資料皆以人工方式輸入，網站資訊一旦發生錯誤，除使網路行銷者的商譽受損外，甚至可能產生嚴重的虧損。因此業者公佈網路資訊時，如何確保資料的正確與合理，實為未來電子商務的重要課題之一。 為使電腦了解資料所隱含的意義進而執行語意偵錯，本研究透過本體論設計語意推論架構，採用專家系統發展程式語言Jess撰寫規則，可早期發現網站資料隱含的錯誤語意，並有效減少網路交易因語意錯誤而導致企業形象與金錢的雙重損失。 本研究係以國內某大型3C賣場之網站商品建構本體論為實證，依商品類別及商品間關係定義，透過本體論界定其領域與範圍，建立具有階層架構的商品本體論，撰寫語意檢查規則，實證結果顯示：本研究成果使廠商在維護網站資訊時，可大幅減少耗用的人力與時間成本，維護網路資訊的正確性。

Through the rapid development of internet, website content is experiencing major changes in time and space. An important question is how to maintain the rationality, correctness and integrity of data. Most websites input data manually by the user, but this may result in incorrect data and thus huge losses. Although it is not difficult to syntactically check the correctness of the data on website, verifying the semantic meaning of the data involves complexity. Besides, the time and labor costs on maintaining website information is also increasing. This research presents a semantic reasoning framework using expert system development program language Jess (Java Expert System Shell) for reasoning under specific ontology. This allows the machine to understand the meaning of data and detect semantic errors of those data. This approach reduces unnecessary losses due to semantic error and decreasing the time and effort on checking data while promoting the work efficiency and business reputation.

1. Extensible Markup Language (XML), W3C Consortium
2. RDF Primer, W3C Recommendation
3. Bollegala D.,Honma, T.,Matsuo, Y.,Ishizuka, M.(2008).Mining for personal name aliases on the web aliases.Proceeding of the 17th International Conference on World Wide Web 2008
4. Brüggemann, S.(2008).Rule mining for automatic ontology based data cleaning.Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),4976,522-527.
5. Chandrasekaran, B.,Josephson, J. R.,Benjamins, V. R.(1999).What Are Ontologies, and Why Do We Need Them.IEEE Intelligent Systems,14(1),20-26.
6. Cruz, C.,Nicolle, C.(2008).XML-IS: Ontology-based integration architecture.WEBIST 2008-4th International Conference on Web Information Systems and Technologies
7. Daconta, M. C.,Obrst, L. J.,Smith, K. T.(2003).The Semantic Web: A Guide to the Future of XML, Web Services and Knowledge Management.Wiley.
8. Davis, R.,Buchanan, B. G.,Shortliffe, E. H.(1977).Production Rules as a Representation for a Knowledge-Based Consultation Program.Artificial Intelligence,8(1),15-45.
9. Ding, Y.(2001).Ontology: The enabler for the Semantic Web.Journal of Information Science,27(6),377-384.
10. JessTab Manual-Integration of Protégé and Jess
11. Eriksson, H.(2003).Using JessTab to Integrate Protégé and Jess.IEEE Intelligent Systems,18(2),43-50.
12. Friedman-Hill, E.(2003).Jess in Action: Rule-Based Systems in Java.Manning.
13. Giarratano, J. C.,Riley, G. D.(1998).Expert Systems: Principles and Programming.PWS.
14. Gruber, T. R.(1995).Towards Principles for the Design of Ontologies Used for Knowledge Sharing.International Journal of Human-Computer Studies,43(5-6),907-928.
15. Gruber, T. R.(1993).A Translation Approach to Portable Ontology Specifications.Knowledge to Acquisition,5(2),199-220.
16. Horrocks, I.,Patel-Schneider, P. F.,Harmelen, F. V.(2003).From SHIQ and RDF to OWL: The Making of a Web Ontology Language.Journal of Web Semantics,1(1),7-26.
17. Jovanovic, J.,Gasevic, D.,Devedzic, V.(2003).A GUI for Jess.Expert Systems with Applications,26(4),625-637.
18. McGuinness, D.L.,Fikes, R.,Rice, J.,Wilder, S.(2000).An Environment for Merging and Testing Large Ontologies.Proceedings of the Seventh International Conference on Principles of Knowledge Representation and Reasoning
19. Musen, M. A.(1992).Dimensions of Knowledge Sharing and Reuse.Computers and Biomedical Research,25(5),435-467.
20. Nault, B. R.,Storey, V. C.(1998).Using Object Concepts to Match Artificial Intelligence Techniques to Problem Types.Information and Management,34(1),19-31.
21. Navigli, R.,Velardi, P.,Gangemi, A.(2003).Ontology Learning and Its Application to Automated Terminology Translation.IEEE Intelligent Systems,18(1),22-31.
22. Negnevitsky, M.(2002).Artificial Intelligence-A Guide to Intelligent Systems.Addison Wesley.
23. Noy, N. F.,McGuinness, D. L.(2001).Stanford Knowledge Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880Stanford Knowledge Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880,未出版
24. Noy, N. F.,Sintek, M.,Decker, S.,Crubézy, M.,Fergerson, R. W.,Musen, M. A.(2001).Creating Semantic Web Contents with Protégé-2000.IEEE Intelligent Systems,16(2),60-71.
25. Ruckhaus, E.,Vidal, M. E.(2003).XWebSOGO: An Ontology Language to Describe and Query Web Sources.Workshop On Web Information And Data Management
26. Shortliffe, E. H.(1976).Computer-Based Medical Consultations: MYCIN.Elsevier:
27. Speel, P-H.,Schreiber, A. Th.,Joolingen, v. W.,Heijst, v. G.,Beijer, G. J.(2001).Conceptual Modelling for Knowledge-Based Systems.Encyclopedia of Computer Science and Technology.
28. Vaculín, R.,Sycara, K.(2007).Specifying and monitoring composite events for semantic web services.Proceedings of the 5th IEEE European Conference on Web Services, ECOWS 07

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