# Causal Pathway Extraction from Web-Board Documents
###### Abstract  
This research aim is to extract causal pathways, particularly disease causal pathways, through cause-effect relation (CErel) extraction from web-board documents. The causal pathways benefit people with a comprehensible representation approach to disease complication. A causative/effect-concept expression is based on a verb phrase of an elementary discourse unit (EDU) or a simple sentence. The research has three main problems; how to determine CErel on an EDU-concept pair containing both causative and effect concepts in one EDU, how to extract causal pathways from EDU-concept pairs having CErel and how to indicate and represent implicit effect/causative-concept EDUs as implicit mediators with comprehension on extracted causal pathways. Therefore, we apply EDU's word co-occurrence concept (wrdCoc) as an EDU-concept and the self-Cartesian product of a wrdCoc set from the documents for extracting wrdCoc pairs having CErel into a wrdCoc-pair set from the documents after learning CErel on wrdCoc pairs by supervised-machine learning. The wrdCoc-pair set is used for extracting the causal pathways by wrdCoc-pair matching through the documents. We then propose transitive closure and a dynamic template to indicate and represent the implicit mediators with the explicit ones. In contrast to previous works, the proposed approach enables causal-pathway extraction with high accuracy from the documents. causal-pathway extraction; transitive closure; word co-occurrence 2021 11 10342 11 10342 11 10342 11 10342 11 10342 11 10342 11 10342 11 10342 11 10342 11 10342 11 11 10342 11 11 11 11 11 11 11 11 11 11  
contains CausativeConcept, EffectConcept and CErel to become the knowledge structure). With regards to [PERSON] et al. [2], the causal pathway in epidemiologic studies is a path starting at the exposure and ending at the disease that follows the direction of the arrows. All arrows of the causal pathway point in the same direction from the exposure toward the outcome, e.g., the disease occurrence is the outcome, [3]. According to [PERSON] and [PERSON] [3], the causal pathway as A \(\rightarrow\) M\({}_{j}\rightarrow\) B contains the exposure (A) which might cause the outcome (B) or through an intermediate process or variable called a mediator (M\({}_{j}\)) where M\({}_{j}\) is a single mediator if \(j=1\); and M\({}_{j}\) is either sequential-mediators or multilevel-mediators if \(j=2\),3,...,num which is an integer. However, our research concerns the extraction of the causal pathway with either a single mediator or sequential mediators mostly occurring on the documents. With regards to our disease document, A, M\({}_{j}\), and B are either a causative event/state concept or an effect event/state concept which is mostly based on a verb phase of an elementary discourse unit (EDU) where an EDU is a simple sentence or a clause [4]. The EDU expression of the research is based on the general linguistic expression in Figure 1 after stemming words and the stop word removal. Where NP1 and NP2 are noun phrases; VP is a verb phrase; Noun is a noun concept set; Verb\({}_{\text{strong}}\) is a strong verb concept set consisting of causative-verb concepts and effect-verb concepts; Verb\({}_{\text{weak}}\) is a weak verb concept set requiring more information, i.e., Noun, to become the causative/effect concept, e.g., _?i/have+in/fat+=/accumulate_ (_'have accumulated fat'_) and _?i/be+in/disease_ (_'get disease'_); Adv is an adverb concept set; Adj is the adjective concept set; and Adjphrase is an adjective phrase. The example of a causal pathway is expressed on the document by a sequence of Cause-Effect pairs (CEpairs) having CErel on the document is shown in Example 1. **Example 1**.