NDLI: Automated analysis of logical connectives in business constraints

Content Provider	IEEE Xplore Digital Library
Author	Akbar, S. Chaudhri, A.A. Bajwa, I.S.
Copyright Year	2013
Description	Author affiliation: Sch. of Comput. Sci., Univ. of Birmingham, Birmingham, UK (Bajwa, I.S.) \|\| Dept. of Comput. Sci. & IT, Islamia Univ., Bahawalpur, Pakistan (Akbar, S.) \|\| Dept. of Bus., Higher Colleges of Technol., Dubai, United Arab Emirates (Chaudhri, A.A.)
Abstract	Business constraints play a key role in business processes. Typically, the business rules are represented in a natural language such as English. Since, the natural languages are naturally ambiguous and one of the reasons of ambiguity is use of logical connectives that can make the NL sentences more ambiguous. Use of logical connectives result in multiple senses of a business rule specifically the XOR role of OR connective makes the things more difficult. To model the Business process specifications and business rules, one need to analyse NL based business rules manually or automatically. During analysis of NL business rules, one had to deal with various occurrences of logical connectives. In this paper, we present a novel approach to identify the type and scope of a logical connective in a target business rule. The presented approach incorporates the Markov Logic approach to handle ambiguity in logical connectives. Initial results of the experiments are also discussed at the end of the paper.
Sponsorship	IEEE United Arab Emirates Sect.
Starting Page	209
Ending Page	213
File Size	542979
Page Count	5
File Format	PDF
DOI	10.1109/CTIT.2013.6749505
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-11
Publisher Place	United Arab Emirates
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analytical models Computational modeling Semantics Business Rules Artificial neural networks Markov Logic Markov processes Logical Connectives NL Ambiguity Business
Content Type	Text
Resource Type	Article

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