# A Named Entity and Relationship Extraction Method from Trouble-Shooting Documents in Korean
## 8 Conclusions  
This paper presents an automation method that can extract the following from equipment maintenance documents: (1) parts of the equipment; (2) the failure mode of the equipment; (3) solutions for defect management; and (4) relation sets established based on the semantic relationships between the entities. Using the Korean BERT model, from the failure mode data, \"PART\" and \"FAILURE\" were extracted, and from the repair action data, \"PART\", \"FAILURE\", and \"ACTION\" were extracted. Then, relation sets based on dependency relationships were established by applying the proposed rules to the extracted results. Previous text-based research for engineering documents focused on finding keywords or technical terminologies in sentences. However, knowledge extraction from engineering \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Relation Set** & **Correct Relation Sets** & **All Relation Sets** & **(\%)** \\ \hline PART-FAILURE & 47 & 52 & 90.38 \\ \hline FAILURE-PART & 18 & 18 & 100 \\ \hline PART-ACTION & 68 & 85 & 80.00 \\ \hline FAILURE-ACTION & 25 & 26 & 96.15 \\ \hline FAILURE-PART-ACTION & 5 & 5 & 100 \\ \hline SUM & 163 & 186 & 87.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Relation set recognition results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Kkma**} & \multicolumn{2}{c}{**Mecab**} \\ \cline{3-6}  & & **Failure** & **Action** & **Failure** & **Action** \\ \hline \multirow{4}{*}{CRF} & Precision & 0.7440 & 0.7085 & 0.7042 & 0.6746 \\ \cline{2-6}  & Recall & 0.7420 & 0.7218 & 0.7154 & 0.6895 \\ \cline{2-6}  & F1 score & 0.7430 & 0.7151 & 0.7098 & 0.6820 \\ \hline \multirow{4}{*}{KLUE-NER} & Precision & 0.7246 & 0.6705 & 0.7712 & 0.6759 \\ \cline{2-6}  & Recall & 0.8315 & 0.6853 & 0.8834 & 0.7021 \\ \cline{1-1} \cline{2-6}  & F1 score & 0.7744 & 0.6778 & 0.8235 & 0.6888 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: A comparison of the combination of NER approaches and morpheme analyzers. documents should consider not only named entities in sentences but also relations between entities. This paper proposes a rule-based approach combining an NER approach and a dependency parsing approach in order to extract both the named entities and their relations. The KLUE NER model and dependency parser were used to implement the proposed approach. The proposed rules were suggested to resolve the semantic heterogeneity of the relations between named entities. This study has the following limitations, and additional follow-up studies are needed accordingly. In this paper, NER was implemented only for three elements: the equipment itself, or parts of the equipment; the failure mode of the equipment; and its repair action. Information regarding equipment maintenance, however, consists of various elements, and the named entities that can be extracted can also be expanded. In addition, if the expressions represented in the failure mode data and the repair action data differ, even if they refer to the same entity, the repair process would not be performed. This problem can be solved by linking the documents to bills of material or design documents used in the actual plant and manufacturing sectors. Semantic heterogeneity and inconsistency in technical terminologies and relations could be resolved if a domain ontology was provided. The proposed system could be useful for domain engineers to build a domain ontology gradually and semi-automatically. A study on how to build a domain ontology for text-based research for engineering documents could be conducted in the future based on the proposed approach. Conceptualization, H.S. and H.L. ; Methodology, H.S., H.L. and J.H.L. ; Formal analysis, M.J. and J.H.L. ; Writing--original draft, M.J. and J.H.L. ; Writing--review & editing, H.L. ; Funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.