Abstract:
Recent years have seen cloud manufacturing as an emerging paradigm in manufacturing industry. When manufacturing organisations provision their manufacturing resources as consumable services over the Internet, particular manufacturing resource rules have to be provided along with the resources. The rules are usually given by experienced mechanical engineers in natural language, and thus need to be converted into semantic rules (in computer language) that can be understood by the search engine of cloud manufacturing system. Previously the conversion process was manually conducted, which is time-consuming and error-prone. This thesis, therefore, proposes a natural language processing (NLP) approach that can autonomously complete the conversion process. The Rules Converter has been developed as a prototype to prove such a NLP approach by leveraging the Stanford parser, Apache Jena framework, and WordNet. The evaluation result shows that Rules Converter can achieve as high as a 95% success rate when converting the manufacturing resource rules to semantic rules.
