## 4 Discussion  
The proposed consistency improvement methods address two main aspects of data consistency: cell consistency and relation consistency. For the relation consistency, the results show the usefulness and the importance of domain knowledge being represented in the proposed classification scheme, which was derived from extensive interviews with multiple domain experts. This classification scheme allows for the assessment of relation consistency presented in the FMEA documents. Interestingly, we noticed that although the distribution of the concept classes in P_FMEA is different from the one in UP_FMEA, the expected concepts class distributions based on the discussion with the experts is not present. For example, despite the fact that in UP_FMEA, concepts belonging to the lower two classes are more represented than in P_FMEA, we noticed that the majority concept class in both P_FMEA and UP_FMEA cells is the Physical concept class. This does not conform to the initial expectation and the definition of the FMEAs. In addition to the presence of concepts belonging to concepts' classes out of the FMEA's type scope, these observations are the first indication of inconsistency in the causal relations. Leveraging the annotated dataset, we discovered that UP_FMEA documents are less consistent than P_FMEA, where more Missing Information relations occur. In other words, as a result of this research, we can quantitatively assess the relation consistency presented in the FMEA documents. The results of this research can be integrated into many downstream tasks. An example is to support domain experts in the process of creating new, more consistent FMEA documents. Additionally, for the automated relation consistency checking, we found this to be highly dependent on the classification method. Thus, different classification architectures are required due to the highly imbalanced distribution of the classes. Namely, for minority classes (e.g., 5M and Parametric), models with fewer parameters, such as the model with \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **F1 Score** & **Precision** & **Recall** \\ \hline Causal cues & 60\% & 92\% & 45\% \\ Intra-sentential pattern & 70\% & 61\% & 81\% \\ Union ensemble & 72\% & 62\% & 86\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The detection of merged FMEA cells. GRU-based architecture, perform better than models with a higher number of parameters, such as the model with LSTM-based architecture. Additionally, each type of relation has a different distribution regarding FMEA cell concept classes. For example, Same Concept relations mainly occur between FMEA cells labeled with concept class Physical. In contrast, Reversed relations mainly occur between FMEA cells labeled with concept classes Physical and UP-C/E. Moreover, Missing Information relations mainly occur between FMEA cells labeled with concept class \"'Parametric', 'Physical', '5M', 'Physical'\" and \"'UP-C/E', 'Functional'\". Finally, consistent relation FMEA cells are labeled with concept class \"Physical\", \"Functional\" and \"UP-C/E\", \"Physical\". Consequently, the FMEA relation consistency performance depends on the classifier performance. The combination of the classes mainly occurs for each relation type. For the cell consistency, in FMEA documents, merged cells occur frequently. Over a third of the FMEA cells are merged based on our labeled subset. Thus, we have devised two approaches to detect merged cells. The first approach is based on causal cues. This approach achieved a 60% F1 score. The high F1 score is attributed to the high precision, which is over 90%. However, as expected, the causal cues-based approach has limited recall because it does not cover cases (b), (c), and (d) of merged cells (please check Figure 2). As such, we proposed the intra-sentential-based approach as an alternative solution. The intra-sentential pattern-based approach showed good results even with no labeled data for the cell consistency improvement.