## 5 Conclusions  
We presented work on FMEA documents, a form of tabular documents comprising short snippets of domain-specific text, where causal relationships are being captured. These documents were originally intended for interpretation by human experts. Consequently, these documents tend to contain many inconsistencies. As a response, we systematically defined: (i) a domain-specific model, consisting of a concept scheme (i.e., types of cause/effects) and a relationship consistency scheme (i.e., valid relations between the concepts), and (ii) a model of inconsistencies, consisting of mixed-up cells (including reverse direction), missing information, and merged cells. We supported domain experts by developing an annotation tool to collect a specific dataset for the semiconductor manufacturing industry. As a result of this research, we can quantitatively assess the relation consistency presented in the FMEA documents.  
We trained and compared different models directly on the FMEA documents to obtain a consistency checking method, which achieves between 56% and 96% F1 on identifying the concept classes. The challenge of merged cells requires separate processing. Here, we adapted an existing method on intra-sentential pattern mining, which was adapted to suit our needs. The intra-sentential pattern extraction finally achieved an F1-score of 70% and 72% when combined with the causal cues-based method.  
The result of the developed models can also be added to the FMEA documents as additional information. The additional information added to existing FMEA (i.e., the classes of the concepts in an FMEA cell, also the cell status if it is merged) is beneficial for the reuse of the FMEA knowledge. A simple example may include retrieving only the direct effect or root cause.  
To summarize, data inconsistency detection in complex environments, similar to highly automated production lines, is likely to fail if conducted exclusively in a data-driven manner. Domain-specific knowledge needs to be considered in multiple stages of the processing pipeline in order to achieve sufficiently good results. In our case, having a causal model and the constraints implied by this model helped to detect many cases of data inconsistency. Our method can be adapted in the future by considering more external data sources for more meaningful representation learning of the text contained in the FMEAs and providing means of interactions and feedback of human experts to address the detected consistency impairments. Finally, as future work, we aim to integrate the results of this research (i.e., FMEA and the additional information provided by the proposed method) in a knowledge discovery approach to predict missing links in the FMEA.  
Conceptualization, H.R. and R.K.; methodology, H.R. and R.K.; software, H.R.; validation, H.R. and R.K.; formal analysis, H.R. and R.K.; investigation, H.R and R.K.; resources, H.R.; data curation, H.R.; writing--original draft preparation, H.R. and R.K.; writing--review and editing, H.R. and R.K.; visualization, H.R.; supervision, R.K.; project administration, R.K.; funding acquisition, R.K. All authors have read and agreed to the published version of the manuscript.  
This research was conducted under the framework of the ECSEL AI4 DI \"Artificial Intelligence for Digitising Industry\" project. The project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No. 826060. The JU receives support from the European Union's Horizon 2020 research and innovation programme, and Germany, Austria, Czech Republic, Italy, Latvia, Belgium, Lithuania, France, Greece, Finland, and Norway.  
Not applicable.  
The authors thank the domain experts for their effort and feedback during iterations of discussion and labeling. Moreover, the authors thank the reviewers for their great help on the article during its review progress.  
The authors declare no conflict of interest.