Scope resolution of logical connectives in NL constraints

Natural Language Constraints have a vital role in Business Organization. The main problem is scope ambiguity when these NL constraints are translated into formal languages. Human beings can understand the context in which these constraints are defined but it is most difficult for a machine to understand the exact meanings of these constraints in their context and this leads to crash the Business System. Therefore before translating these business constraints to OCL, the scope ambiguities should be resolved for correct translation of NL to OCL. For this purpose a new technique is proposed for handling the scope of logical operators used in NL constraints by using the Markov Logic. The subject and scope knowledge and Markov logic integrates into natural language processing. The subject knowledge has been deployed as context knowledge and scope knowledge was acquired from the business constraints. Markov logic was applied to NL constraints for selecting the most possible meaning of an ambiguous NL constraint based on the context. The presented work shows that by handling the identified cases of scope ambiguities of logical operators, we can take correct translation of business constraints into formal specifications.