採用本體論及推論於資料語意偵錯之研究

Detecting Semantic Data Errors by Adopting Ontology and Reasoning

林英潔(Ying-Jie Lin)

指導教授 ： 劉艾華

淡江大學/管理學院/資訊管理學系碩士班/碩士(2007年)

https://doi.org/10.6846/TKU.2007.00053

摘要

隨著網際網路的快速發展，多樣化的網站資料也伴隨時間與空間有所變動，如何確保資料的合理性、正確性及完整性更是一大重要課題。目前大多數網站，其資料皆是由人工方式進行輸入，然而這樣的輸入方式往往會導致輸入錯誤資料的情況發生，而產生嚴重的虧損。雖然使用程式語言對於網站資料在語法上的正確性維護並不困難，然而要做到判斷網站內容在語意上是否合理化，卻是相當地複雜，相對地也提高維護網站資訊所需花費的人力與時間成本。
為了要讓機器了解資料中所隱含的意義，達到資料語意上的偵錯，本研究透過本體論提出語意推論架構，針對資料語意上的錯誤，採用專家系統發展程式語言Jess ( Java Expert System Shell ) 撰寫規則，讓機器能理解資料的涵義進行推論，檢查出語意上可能有問題的資料，減少因語意錯誤而導致不必要的損失與檢查過程所需耗費的時間及人力，進而提升工作效率和企業聲譽。

關鍵字

語意網路 ； 本體論 ； Jess ； 自動推論

並列摘要

Through the rapid development of internet, website contents also 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 usually result in incorrect data and thus the huge loss. Although it is not difficult to syntactically checking the correctness of the data on website, verifying the semantic meaning of the data involves complexity. Besides, it is also increasing the time and labor cost on maintaining website information.
This research presents a semantic reasoning framework using expert system development program language Jess (Java Expert System Shell) for the 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 loss due to semantic error and decreasing the time and effort on checking data while promoting the work efficiency and business reputation.

並列關鍵字

Semantic Web ； Ontology ； Jess ； Automated Reasoning

參考文獻

3. 關銘、民93，以OWL DL及SWRL為基礎建置推論雛型系統-以大學排課問題為例，中原大學資訊管理學系碩士論文。

1. Chandrasekaran, B., Josephson, J. R. and Benjamins, V. R., “What Are Ontologies, and Why Do We Need Them,” IEEE Intelligent Systems, Vol. 14, Iss. 1, 1999, pp. 20-26.

2. CLIPS, A Tool for Building Expert Systems, (http://www.ghg.net/clips/CLIPS.html)

5. Davis, R., Buchanan, B. G. and Shortliffe, E. H., “Production Rules as a Representation for a Knowledge-Based Consultation Program,” Artificial Intelligence, No. 8, 1977, pp.15-45.

8. Eriksson, H., “Using JessTab to Integrate Protégé and Jess,” IEEE Intelligent Systems, Vol. 18, Iss. 2, 2003, pp. 43-50.