Abstract: Existing question answering approaches have the disadvantages that they possess limited contextual understanding due to which the retrieval process they use is inefficient in nature. Embodiments disclosed herein provide a method and system for domain-driven knowledge augmented question answering. The system receives a raw corpus data as input, wherein the raw corpus data is a domain specific data. Further, an indexed corpus is generated from the raw corpus data, during which a document chunking approach is used. The indexed corpus is then used for processing received queries received, in order to generate response to the received user queries.

Abstract: Failure analysis of industrial plants are stored in various types of documents associated with industrial plant. The documents are used by operators of plant to address any deviation that is active in plant. The operator generally has prior knowledge of relevant processes, equipment and sensors described in a deviation scenario in these documents. However, a system that is envisaged to aid operator in real-time does not have this information readily available as this knowledge is spread across documents. Currently available systems manually curate failure knowledge thereby making the process time consuming and prone to human errors. Present disclosure provides method and system for performing extracting and collating failure knowledge from diverse sources in industrial plant.