## 1 Introduction  
Two centuries have passed since the industrial revolution transformed manufacturing processes. Now in the era of industry digitalization and increased use of data, causal domain knowledge is, more than ever before, crucial. For example, in automated data analytics processes, causal domain knowledge inclusion helps to avoid biased results [1] and increases data analysis algorithms' robustness (e.g., increases machine learning algorithms' robustness [2]). Risk assessment and root cause analysis are two practices performed in the industry that capture and document causal domain knowledge. Almost a century has passed since the Failure Mode Effect Analysis (FMEA) process was first proposed as a tool for risk assessment [3]. In the FMEA process, a multidisciplinary team often uses the brainstorming method to identify lists of the failure modes and their possible root causes and potential effects. In addition to other information such as corresponding authors' information and the scope of the FMEA process, these lists are saved in a document (the FMEA document) typically using a standard tabular format. This tabular format's columns contain the failure mode, potential effects, and possible root causes, in addition to other columns such as risk priority number, detection, etc. The FMEA process successfulness as a risk assessment tool depends on (i) the complete and accurate identification of potential failure modes contained in a system and (ii) the rigorous evaluation of the risk level of these failure modes [4]. Now, the FMEA process is widely used in many industries. For example, the FMEA process is used in the design and validation of a hydraulic torque converter [5]. Additionally, the diagnosis of the bearing condition using the FMEA process is presented in [6]. Additionally, in [7], Bayesian networks are built from the FMEA process used by [PERSON] decision processes to model uncertainties within unmanned aerial vehicles. Thus, in the context of extracting causal domain knowledge in the industry, FMEA documents represent a valuable data source. As highlighted by previous research, the FMEA process is error-prone. As a result, FMEA documents suffer from data consistency issues. For example, [PERSON] et al., in [8] argued that due to the insufficient comprehensibility of the brainstorming session, cases of missing information might occur (i.e., by omitting failure modes). Consequently, many approaches are proposed to differently aggregate sources of information to improve the brainstorming sessions' comprehensibility. Thus, solutions based on ontologies are presented in [9; 10; 11] to satisfy the requirement to share, reuse, and maintain FMEA knowledge. Such solutions are labor-intensive and time-consuming. As such, text mining algorithms are proposed to extract a list of frequent failure modes and build the standard failure mode vocabulary (e.g., the method proposed in [12]). Additionally, FMEA process performance is limited in capturing rich information about implicit causal relations expressed between its columns. In order to enrich these causal relations with more information, a more sophisticated methodology, namely Failure Modes, Mechanisms, and Effects Analysis (FMMEA), is proposed in [13]. Since our research aims to extract causal knowledge from FMEA documents, which is, by design, limited in capturing rich information about the causal relations, we are faced with consistency impairments in these documents. Moreover, these consistency impairments, to our best knowledge, were not previously addressed. Especially when the intention is to extract causal domain knowledge, these consistency impairments limit the effectiveness of the extracted knowledge for downstream tasks. Therefore, in this paper, we argue that consistency impairments concerning the implicit causal relation between the FMEA documents' columns (i.e., root cause and failure mode, and failure mode and effect) are also significantly important. Ideally, in a given FMEA cell, a short, descriptive text represents a single concept.