## 2 Materials and Methods
These two hyperparameters increase the approach precision in detecting merged cells for a range of values where **MIth**\(>\) 0 and **OCH**\(>\) 1. However, it decreases the recall. The filtered n_grams are considered as patterns. **Step 5** Top Patterns and subpatterns extraction (pattern clustering): A Top Pattern is a pattern (n_gram) that is not contained by any other pattern. In this step, all the Top Patterns are extracted from the filtered n_grams resulting from Step 4. Furthermore, all the patterns from the same filtered n_grams contained in a Top Pattern are collected and considered subpatterns. The results of this step are a number of clusters represented by the Top Patterns and the member of this cluster, which are the subpatterns. **Step 6** Merged FMEA cell detection: A rule-based approach leverages the patterns extracted from Step 4 and Step 5 to predict the merged FMEA cell. This step matches all the patterns from Step 4 contained in the FMEA cell then checks if there is a Top Pattern (cluster) that contains all the matched patterns. If this criterion is not met, the algorithm predicts the cell is merged. The proposed approach to detect merged cells based on the intra-sentential pattern is highly dependent on the dataset where the Mutual Information is calculated. Thus, the **MIth** and **OCH** need to be adapted. As for the causal relation documented during the FMEA process, it depends on the scope of the FMEA and the internal agreement between the multidisciplinary team creating the FMEA. This agreement typically is not documented. Thus, for FMEA documents conducted on the same scope, significant differences in the semantics of the concepts described in the columns are found. This is worsened by the broad definition of the FMEA columns. For example, while [PERSON] et al. argue in [8] that the definition of failure is too narrow and that this might lead to the omission of failure modes, we believe that the definitions of the root causes and effects are too broad. This claim is also supported by observing common practices in datasets for knowledge graphs that include causal relations such as Atomic [19] and Glucose [20]. In such datasets, the authors define different types of causal relations and attempt to describe the semantics of causal relations. Consequently, the descriptions of the effects and the root causes are less consistent. Thus, because the transitive perception of causal relation might lead to the description of a distant cause or effect skipping the direct one. For example, given the causal chain [**A**]-[causes]->[**B**]-[causes]->[**C**] (**A**, **B**, **C** are three concepts in the FMEA documents), due to the transitive perception of causal relation [**A**]-[causes]->[**C**] is also valid and might be documented in the FMEA documents. However, the information about the mediation of the causal effect between **A** and **C** through **B** is missing, especially if the original causal chain [**A**]-[causes]->[**B**]-[causes]->[**C**] is not documented in FMEA documents. This missing information is critical, especially in industries with a long manufacturing process, where the manufacturing typically extends for hundreds of processing steps. Additionally, in some cases, the direction of the causal relation is reversed. For example, [**C**]-[causes]->[**B**] is documented instead of [**B**]-[causes]->[**C**]. This impairment might be attributed to human error or ambiguity of the temporal aspect while creating the FMEA document. In general, this might not pose a large problem for domain experts to cope with this missing information and automatically correct for the reversed relation due to their profound knowledge of the domain. However, in the case of automated data analytics, missing information and contradictions in the documented causal relation direction severely affects the usefulness of the data analytics method, especially in the considered causal model.