DocumentCode
492245
Title
Statistical Machine Learning in Natural Language Understanding: Object Constraint Language Translator for Business Process
Author
Zhao, Li ; Li, Feng
Author_Institution
Dept. of Mechanism, ChangChun Univ., Changchun
fYear
2008
fDate
21-22 Dec. 2008
Firstpage
1056
Lastpage
1059
Abstract
Natural language is used to represent human thoughts and human actions. Business rules described by natural language are very hard for machine to understand. In order to let machine know the business rules, parts of business process, we need to translate them into a language which machine can understand. Object constraint language is one of those languages. In this paper we present a statistical machine learning method to understand the natural business rules and then translate them into object constraint language. Subsequently a translation algorithm for business process modeling is also provided. A real case, air cargo load planning process is proposed to illustrate the efficiency and effective of the method and the algorithm. The result has shown that this method and algorithm enrich business process modeling technology and enhance the efficiency of software developers in business process modeling.
Keywords
business process re-engineering; language translation; learning (artificial intelligence); natural languages; statistical analysis; business process modeling technology; natural business rules; natural language understanding; object constraint language translator; statistical machine learning; Constraint optimization; Humans; Laboratories; Learning systems; Machine learning; Machine learning algorithms; Natural languages; Power system modeling; Programming; Unified modeling language; Business Process; Object Constraint Language; Statistical Machine Translation;
fLanguage
English
Publisher
ieee
Conference_Titel
Knowledge Acquisition and Modeling Workshop, 2008. KAM Workshop 2008. IEEE International Symposium on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-3530-2
Electronic_ISBN
978-1-4244-3531-9
Type
conf
DOI
10.1109/KAMW.2008.4810674
Filename
4810674
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