A Question-Answering Model Based on Knowledge Graphs for the General Provisions of Equipment Purchase Orders for Steel Plants Maintenance

Abstract

Recently, equipment replacement and maintenance repair and operation (MRO) optimization have substantially increased owing to the aging and deterioration of industrial plants, such as steel-making factories in Korea. Therefore, plant owners are required to quickly review equipment supply contracts, i.e., purchase order (PO) documents, with suppliers and vendors. Currently, there is inconsistency in the time and quality required for the PO document review process by engineers, depending on their manual skills and practice. This study developed a general provisions question-answering model (GPQAM) by combining knowledge graph (KG) and question-answering (QA) techniques to search for semantically connected contract clauses through the definition of relationships between entities during the review of equipment purchase contracts. The PO documents analyzed in this case study were based on one steel-making company’s general provisions (GP). GPQAM is a machine learning (ML)-based model with two sub-models (i.e., KG and QA) that automatically generates the most relevant answers to semantic search questions through a cypher query statement in GP for the PO engineers. First, based on the developed GP lexicon and its classifying taxonomy to be stored in the Neo4j graph database (GDB), the KG sub-model finds the corresponding synonyms and consequently shows GP-related information in a graphic form. Second, the QA sub-model is a function to find and answer contract information within the KG and applies pattern-matching technology based on the Aho–Corasick (AC) algorithm. Third, nodes with the meaning most similar to the question are selected using similarity measurement if a response cannot be extracted through the pattern-matching process. Forty-five pilot test questions were created and applied to the GPQAM model evaluation. The F1 score was 82.8%, indicating that the unsupervised training methods developed in this study could be better applied to a semantic QA process in plant engineering documents, where sufficient training data are limited and bargained. An expert survey of PO practitioners confirmed that the semantic QA capability of GPQAM might be efficient and useful for their work. As the first case of applying KG technology to semantic QA for plant equipment PO contracts, this study might be a meaningful contribution to the steel plant industry and, therefore, extended to construction and engineering contract applications.