# Ontology-Based Feature Selection: A Survey
## 5 Ontology-Based Feature Selection
### Other Applications
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Related Work** & **Application** & **Ontology** & **Feature Selection** & **Classifier** \\ \hline
[91] & \begin{tabular}{c} Recommender \\ system \\ \end{tabular} & \
\begin{tabular}{c} Custom \\ domain ontology \\ \end{tabular} & Ontology-based &
\begin{tabular}{c} KNN \\ summary \\ \end{tabular} \\ \hline
[92] & \
\begin{tabular}{c} Recommender \\ system \\ \end{tabular} & DBpedia & Information &
\begin{tabular}{c} KNN \\ Gain \\ \end{tabular} \\ \hline
[93] & \
\begin{tabular}{c} Recommender \\ system \\ \end{tabular} &
\begin{tabular}{c} Movie \\ Ontology \\ \end{tabular} & Filtering & Clustering \\ \hline
[94] & Manufacturing & \
\begin{tabular}{c} Custom \\ domain ontology \\ \end{tabular} &
\begin{tabular}{c} Similarity \\ measure \\ \end{tabular} & Rule-based \\ \hline
[95] & Manufacturing &
\begin{tabular}{c} Custom \\ domain ontology \\ \end{tabular} & Filtering & Rule-based \\ \hline
[96] & Manufacturing &
\begin{tabular}{c} Custom \\ domain ontology \\ \end{tabular} & Filtering & Rule-based \\ \hline
[97] & Manufacturing & \
\begin{tabular}{c} Custom \\ domain ontology \\ \end{tabular} & Filtering &
\begin{tabular}{c} Rule-based \\ (Pearson Coef.) \\ \end{tabular} \\ \hline
[98] & Manufacturing &
\begin{tabular}{c} Custom \\ domain ontology \\ \end{tabular} & Filtering & Rule-based \\ \hline
[99] & \begin{tabular}{c} Urban \\ management \\ \end{tabular} & \
\begin{tabular}{c} Custom \\ domain ontology \\ \end{tabular} & Filtering &
\begin{tabular}{c} Random \\ Forest \\ \end{tabular} \\ \hline
[100] & \begin{tabular}{c} Information \\ security \\ \end{tabular} & \
\begin{tabular}{c} Custom \\ domain ontology \\ \end{tabular} & Filtering &
\begin{tabular}{c} Decision \\ Tree \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Related works organized according to specific criteria. products, through the matching of appropriate features and attributes with main process characteristics and capabilities. The semantic Web rule language (SWRL [101]) has been used as an effective method to represent causal relations. The purpose of the CBR subsystem is to find the optimal product-to-process matching. It does so in two steps. First, it scans the ontology for a similar product. To quantify product similarity, appropriate feature and attribute similarity measures have been developed and human experts have been employed to assign proper weights to features and attributes. If a matching product is found then the corresponding process is presented to the decision maker, otherwise SWRL rule-based reasoning is used to find a suitable manufacturing process. Finally, the ontology is updated with the newly extracted knowledge. The authors presented a use case to demonstrate the usability and effectiveness of the proposed DSS and argue that in the future such systems will become more and more relevant. In [96], [PERSON] et al. develop an ontology-based representation model to select appropriate machining processes as well as the corresponding inference rules. The ontology is quantified in terms of features, process capability with relevant properties, machining process, and relationships between concepts. A reasoning inference mechanism is applied to obtain the final set of processes for individual features. The determination of the process that corresponds to the highest contribution is carried out through a solid mechanism that associates the capability of the candidate processes with the accuracy requirements of a specific feature. The appropriate machining process is, then, selected so that the relationship constraint between a pre-specified set of processes is met. The whole process selection scheme is neutral (i.e., general enough) in the sense that it does not depend on a specific restriction, and thus it constitutes a reusable platform. [PERSON] et al. [97] also apply ontology within the mechanical engineering domain, in particular the field of Noise, Vibration, and Harshness (NVH).