## 3 Methods
### Structured Data Representation of Causal Information  
Structured data representation is a crucial part of information extraction. In particular, representing causal relationships in a structured manner has received significant attention in recent years due to their importance in understanding the connections between different events, phenomena, and concepts. There are several ways to represent structured data in the context of causal relationships. One common approach is to use knowledge graphs to represent the relationships between different entities. In these graphs, each entity is represented as a node, and the causal relationships between entities are represented as edges. For example, a knowledge graph could represent the causal relationships between different failure modes and their causes and effects in the FMEA documents [13]. However, information extraction for causal relations is challenging due to several reasons. Causal relations are often part of our cognitive reasoning and can be articulated implicitly in text, making it difficult for automated systems to identify them accurately. Additionally, cognitive reasoning can differ from person to person, which can create disagreement in the interpretation of the same text. To mitigate these challenges, several scholars have proposed different annotation guidelines to guide annotators in the annotation process [28; 29; 30; 34]. Specifically, many scholars have recommended only extracting explicit causal relations [28]. In our structured data representation, we adapted these recommendations by specifying three entity types: cause, effect, and trigger. The cause and effect entities represent events or variables articulated in the text, while the trigger is the explicit causal clue that expresses the presence of the causal relation between the cause and the effect. To fully represent a causal relation, three types of entities--cause, trigger, and effect--along with two relations between them, are used. The first relation connects the cause and the trigger, while the second relation connects the trigger and the effect. Thisapproach helps to accurately capture explicit causal relations in texts and can be useful in various applications such as information retrieval and knowledge representation. Annotation guidelines are crucial for creating consistent training and testing data, particularly for data-driven methods. They facilitate the consistency required for the effective training and evaluation of such methods. These annotation guidelines aim to reduce the varying interpretations among different annotators. However, these guidelines may have limited applicability to domain-specific texts, such as those from the semiconductor industry, due to their unique characteristics. To address these challenges, we have devised annotation guidelines that combine and distill the diverse paradigms proposed by previous researchers. Our guidelines provide examples from a domain-specific data set to support experts in gaining a better grasp of the task. The developed annotation guidelines are detailed as follows:  
1. Causal relations are only annotated on text level, which corresponds to a single FMEA cell or a text box recognized by the OCR in the case of a presentation slides. Relations between entities that belong to different texts are disregarded. 2. Two entities--either two effects or two causes--are annotated as a single entity if they are linked to the same cause or effect [30]. Example: _Die chipping/crack effect due to Trigger dicing process condition/parameters and the wafer condition in ker area_Cause. 3. Causal relations can be chained [30]. The effect of a cause can be the cause of another effect. Example: _Due to Trigger a wrong implantation dose_Cause, the compensation was destroyed_Effect_Cause, and _therefore_Trigger, the lot was disregarded_Effect. In the example, the entity \"the compensation was destroyed\" is the effect of \"a wrong implementation dose\" and the cause of \"the lot was disregarded\". 4. Nested relations are allowed.