## 1 Introduction
In this case, a concept is a separable (identifiable) phenomenon that acts either as (i) a root cause, (ii) an effect of the failure mode observed in the product characteristics, or (iii) a failure mode as an intermediate state in the causal chain. However, based on the analysis of actual FMEA documents from a semiconductor manufacturing company, the majority of data consistency impairments are attributed to one of two main categories: (a) FMEA documents' cell consistency; (b) FMEA relations consistency (i.e., consistency impairments concerning the implicit causal relation between the FMEA documents' columns). Figure 1 depicts our observations concerning consistencies found in actual FMEA documents. In the category of FMEA documents' cell consistency impairments, we noticed cases of merged cells wherein the short text of a single cell comprises multiple concepts or even relations between multiple concepts, e.g., a causal chain of multiple causes and effects. In the category of FMEA relations consistency, we noticed the following:  
1. Cases of missing information in the causal chain, where the documented relation actually describes a subsequent effect of the cause but skips its direct effect;
2. Cases of conflict in the direction of the relations, i.e., the direction of the causal effect relation is reversed. These consistency impairments are mainly attributed to the manual creation of FMEA documents by human domain experts, the broad definition of FMEAs' columns, and the complex nature of causal relations (e.g., many-to-many relations with transitive perception [14]). Hence, any disagreement among experts on the exact semantics of these columns will increase consistency impairments within these documents. Formally, these documents will still conform to the structure (i.e., tabular); their content no longer aligns with the intended semantics. Consequently, in the era of industry digitalization, these consistency impairments seriously limit the effectiveness of including causal domain knowledge documented by the FMEA process. In this paper, we first propose methods to improve the consistency of FMEA documents by defining a classification scheme to provide an improved understanding of the consistency impairments with respect to the causal relations expressed in the FMEA documents. The classification scheme is derived from domain experts' perception of the short text in the FMEA cell. The classification scheme can be applied in the form of metadata annotations to the tabular data to assess the data consistency for a given FMEA. Still, the dataset size limits the feasibility of a manual annotation process, i.e., in practice, it is not possible to manually label all available datasets in the production environment. As such, we leverage advances in artificial intelligence and natural language processing for an automated or at least semiautomated classification method. Consequently, the reversed direction of causal relations, which indicates swapped cells in the FMEA, is effectively addressed via this classification method combined with experts' logic. Moreover, we are able to distinguish different types of causal relations based on the classes of the concept that it connects. We hypothesize that these classes and these different types of relationships would be extremely beneficial for downstream tasks, such as information retrieval and knowledge discovery, effectively addressing the challenges related to missing information issues. Next, this paper proposes a pattern-based method for merged cell identification in FMEA documents. This method leverages predefined patterns for causal cues to identify merged FMEA cells. Additionally, a patterns extraction method calculates the Mutual Information based on the co-occurrence of the terms on FMEA cells to identify merged cells is also presented. In summary, our contributions are as follows:  
* We highlight the importance of including causal domain knowledge to improve the data analytics methods robustness;  
Figure 1: Comparison between ideal FMEA and actual FMEA.