# Construction of Disease-Symptom Knowledge Graph from Web-Board Documents
## 4 System Overview
### Determination of \(d_{t,i}\) and \(wc_{\textit{Synt},ij}\) Features
The logistic function can be written as:  
\[F(x)=\frac{1}{1+e^{-(\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2})}} \tag{12}\]  
\(F(x)\) is interpreted as the probability of the given outcome to be predicted where \(x_{1}\) and \(x_{2}\) are attribute variables, and \(\beta_{0}\), \(\beta_{1}\), and \(\beta_{2}\) are the model estimators which play the role of momentum for each attribute. The LR learning is to determine \(\beta_{0}\), \(\beta_{1}\), and \(\beta_{2}\) for \(d_{t,i}\) and \(wc_{Symt,ij}\) as \(x_{1}\) and \(x_{2}\) features, respectively, in each disease-symptom pair (\(d_{t,i}\), \(wc_{Symt,ij}\)) \begin{table}
\begin{tabular}{c c c c c c} \hline \(d_{t,i}\) (Type\({}_{1}\)) & **CE-Rel** & **NonCE-Rel** & \(wc_{Symt,ij}\) & **CE-Rel** & **NonCE-Rel** \\ \hline \(d_{1,3}\) & 0.0590 & 0.0601 & S6 & 0.0754 & 0.0237 \\ \(d_{1,7}\) & 0.0465 & 0.0474 & S7 & 0.0344 & 0.0227 \\ \(d_{1,17}\) & 0.0755 & 0.0769 & S10 & 0.0419 & 0.0350 \\ \(d_{1,58}\) & 0.0962 & 0.0980 & S28 & 0.0084 & 0.0435 \\ \(d_{1,67}\) & 0.0631 & 0.0643 & S31 & 0.0195 & 0.0047 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ \(d_{t,i}\) (Type\({}_{2}\)) & **CE-rel** & **nonCE-rel** & \(wc_{Symt,ij}\) & **CE-rel** & **nonCE-rel** \\ \(d_{2,41}\) & 0.0203 & 0.0216 & S6 & 0.0247 & 0.0734 \\ \(d_{2,54}\) & 0.0365 & 0.0389 & S7 & 0.0247 & 0.0327 \\ \(d_{2,63}\) & 0.0365 & 0.0389 & S10 & 0.0277 & 0.0471 \\ \(d_{2,70}\) & 0.0397 & 0.0424 & S28 & 0.0398 & 0.0064 \\ \(d_{2,18}\) & 0.0397 & 0.0424 & S31 & 0.0068 & 0.0199 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ \hline \end{tabular}
\end{table} 
Table 2: Show the probabilities of \(d_{t,i}\) and \(wc_{Symt,ij}\) with the CE-rel and nonCE-rel classes from Type\({}_{1}\) and Type\({}_{2}\) disease documents (where the values of the \(d_{t,i}\) features and the \(wc_{Symt,ij}\) features with the symptom-concept codes are \(d_{1,3}\) = HepatitisB\(,d_{1,7}\) = German-measles, \(d_{1,17}\) = TyphoidFever, \(d_{1,58}\) = Dysentery, \(d_{1,67}\) = Enteritis, \(d_{2,18}\) = diabetes, \(d_{2,41}\) = PulmonaryVascularDisease, \(d_{2,54}\) = Mycoraditis, \(d_{2,63}\) = BrainDisease, \(d_{2,70}\) = Anemia, S\({}_{6}\) = haveFever (person), S\({}_{7}\) = haveHeadache (person), S\({}_{10}\) = vomit (person), S\({}_{28}\) = weaken (person), S\({}_{31}\) = haveSweat (person)). with either the positive/CErel class or the negative/nonCErel class formed by the automatic supervised learning on each disease-type learning sample. Determination of \(d_{t,i}\)wcSym\({}_{t,ij}\) Pairs Having CErel for Collecting \(d_{t,i}\)-SymGroup\({}_{dt,1}\) Pair with CErel  
There are three steps in the CErel determination from the test corpus consisting of 70 \(d_{i}\) documents: \(d_{t,i}\) and \(wc_{symt,ij}\) Determination, Determination of \(d_{t,i}\)wc\({}_{symt,ij}\) Pairs having CErel, and Collection of \(d_{t,i}\)-SymGroup\({}_{dt,i}\) Pairs having CErel  
1. \(d_{t,i}\) and \(wc_{symt,ij}\) Determination  
The disease name concept, \(d_{t,i}\) is determined from the \(d_{t,i}\) document having \(d_{i}\) as the topic name in Type\({}_{t}\) by the named-entity recognition [30; 31] followed by the named-entity translation from Thai to English by the Lexitron dictionary in each disease type of the test corpus. After stemming words and eliminating stop words of the test corpus on each disease type, the EDU\({}_{h,Synt,ij}\) occurrence is identified by the predicate-verb term set (V; V = Verbstrong \(\cup\) Vinf) to each EDU\({}_{h}\) occurrence on the \(d_{t,i}\) document. According to R1-R5, each \(wc_{symt,ij}\) feature of the \(wc\) expression on the identified EDU\({}_{h,Synt,ij}\) occurrence is then determined by translating all terms in the predicate-argument pattern from Thai to English by the collected element concepts in the Verbstrong, Verbweak, Noun, Adj, and Adv sets.