# Ontology-Based Feature Selection: A Survey
## 5 Ontology-Based Feature Selection
### Document Classification
In [74], [PERSON] and [PERSON] study the effectiveness of ontology in the classification of Web documents. They are interested in a corpus with Web pages in four distinct categories from the domain of sports, namely cricket, football, hockey, and baseball. First, they use OWL to develop the ontology which represents the set of classes, individuals, and relationships for the domain under consideration. Then an ontology guided term-weighting technique is applied to extract and weight the feature terms that represent each document. Specifically, after stop-word removal and stemming, each obtained term from the Web page is looked-up in the Ontology. If the term is not found it is discarded, otherwise the sum of its TF-IDF scores from each document, is assigned as its semantic weight,  
\[w_{term}=\sum_{doc_{j}\in Documents}|term\;in\;doc_{j}|\cdot log\frac{| Documents|}{|Documents_{term}|}, \tag{10}\]  
where \(w_{term}\) is the term semantic weight and \(Documents_{term}\) are the documents that contain the term. The authors compared their method against the standard Bag of Words approach with TF-IDF term weighting and report that the use of ontology considerably improved the performance of the classification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Related Work** & **Application** & **Ontology** & **Feature Selection** & **Classifier** \\ \hline
[55] & Web Text & WordNet & TFIDF & NB, I Rip, J48, SVM \\ \hline
[56] & Web text & \
\begin{tabular}{c} Tourism, Space, \\ Film, WordNet \\ \end{tabular} &
\begin{tabular}{c} Web-based PMI \\ (\(NE_{score}\), \(SOC_{score}\)) \\ \end{tabular} & - \\ \hline
[59] & \
\begin{tabular}{c} Text \\ (Medical) \\ \end{tabular} & UMLS &
\begin{tabular}{c} Frequency, \\ Hill Climbing \\ \end{tabular} & KNN \\ \hline
[62] & Text & WordNet & MFS, TFIDF & NB \\ \hline
[64] & \
\begin{tabular}{c} Clinical Notes \\ (CAD) \\ \end{tabular} & UMLS & TFIDF, PSO &
\begin{tabular}{c} NP, LSVM, \\ KNN, DT, LR \\ \end{tabular} \\ \hline
[66] & \
\begin{tabular}{c} Medication list \\ (hospital re-entry) \\ \end{tabular} &
\begin{tabular}{c} RxNorm, \\ NDF-RT \\ \end{tabular} & IGR & NB \\ \hline
[69] & \begin{tabular}{c} Web text \\ (Tweets) \\ \end{tabular} & \
\begin{tabular}{c} Custom, \\ BabelNet \\ \end{tabular} &
\begin{tabular}{c} Ontology-based, \\ SWRL \\ \end{tabular} & - \\ \hline
[70] & Text & WordNet & Similarity & SVM \\ \hline
[71] & \
\begin{tabular}{c} Text \\ (Clustering) \\ \end{tabular} & WordNet & Similarity &
\begin{tabular}{c} Special \\ K-means \\ \end{tabular} \\ \hline
[72] &
\begin{tabular}{c} Text \\ (Clinical) \\ \end{tabular} & UMLS & IG, Lin & SVM \\ \hline
[74] & \
\begin{tabular}{c} Web text \\ (Sports) \\ \end{tabular} & Custom & TFIDF &
\begin{tabular}{c} MNB, DT, \\ KNN, Rocchio \\ \end{tabular} \\ \hline
[75] & Text & \
\begin{tabular}{c} WordNet, \\ OpenCyc, SUMO \\ \end{tabular} &
\begin{tabular}{c} Custom \\ Mapping Score \\ \end{tabular} & SVM \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Document classification organized according to specific criteria. In their work [75], [PERSON] and [PERSON] attempt to improve text classification by replacing the traditional Bag of Words (BoW) document representation with Bag of Concepts (BoC) derived from multiple relevant ontologies. Initially, they employ the Jena Ontology API [76] to combine three ontologies, namely WordNet, OpenCyc, and SUMO. In order to align equivalent concepts, first they identify homographic concepts. Two concepts are homographic, when they belong to different ontologies, but share the same name or the same synonym. Homographic concepts are also equivalent, if their direct subconcepts or superconcepts, with respect to a particular relation type, are homographic. Once the ontologies have been aligned, a context is obtained for each concept c in the set of all ontologies (\(Ocont(c)\)), as the union of all synonyms of c, the names of all subconcepts and superconcepts of c and the synonyms of the subconcepts and superconcepts.