Processing Textual Information from Industrial Systems Using Semantic Networks

A paradigm shift is emerging in system reliability and maintainability. The military and industrial sectors are moving away from the traditional breakdown and scheduled maintenance and adopting concepts referred to as Condition Based Maintenance. In addition to signal processing and subsequent diagnostic and prognostic algorithms these new technologies require storage of large volumes of both quantitative and qualitative information and means to retrieve old cases from these case libraries and match them with a current problem. A semantic network based approach is being presented for natural language processing of qualitative information available from industrial systems in the form of textual descriptions. Syntactic rules are used to extract relationships between the words and the spatial arrangement is preserved using semantic networks. Compared to other current automated methods to manipulate text messages which are computationally expensive, this technique takes advantage of the semi structured nature of the text and domain limited vocabulary in industrial environments in order to create an architecture that processes textual information efficiently and effectively. Domain knowledge is taken into consideration while interpreting the text and creating the semantic networks. These semantic networks form a part of cases in a dynamic case based reasoning system, which constitutes an integral module of integrated diagnosis-prognosis architecture. This approach assists in retrieving short text based cases taking into account the semantic meaning of the sentence and not just conventional frequency based information.