# Ontology-Based Feature Selection: A Survey
## 5 Ontology-Based Feature Selection
### Document Classification
In the second step, each concept or instance is expanded with a set of related keywords from the BabelNet knowledge base, which are inserted to the ontology as OWL annotation properties. Using the enriched ontology, the Ontology Classifier module annotates input messages with low-level concepts and the Ontology Instantiator module populates the ontology with the annotated messages and associated users. The Ontology Reasoner executes a set of SWRL rules on the populated domain ontology and infers new concepts for the messages, which are also added in ontology as new semantic annotations. Finally, the Ontology Querying module is used to compute the radicalization indicators for each user of the considered dataset by executing specific queries on the populated ontology. The proposed system was tested on a randomly selected dataset containing radical and neutral Twitter messages against a baseline using the standard F1 score. The obtained results showed that the ontology-based approach gave higher precision and recall and overall better classification results. [PERSON] and [PERSON] [70] describe a framework to investigate the effectiveness of using WordNet semantic features in text categorization. In particular, they examine the effect that part-of-speech tagging, inclusion of WordNet features, and word sense disambiguation, have on text classification. POS features consist of the nouns, verbs, adjectives, and adverbs in the document. WordNet features are the synsets with specific relations to the document terms, namely synonyms, hypernyms, hyponyms, meronyms, and topics. Word sense disambiguation is performed in order to exclude irrelevant synsets from the feature set. For each synset, a score is computed as the sum of its similarities (common hypernyms and topics) to all other synsets. Synsets with a score below a predefined threshold are excluded. Experiments were performed using the SVM classifier on five standard datasets. The contribution of each task to the classification was quantified by means of the macro and micro variants of the F-measure metric. The authors conclude that using nouns, adjectives, and verbs in conjunction with the raw terms improve the classification, while adverbs have an adverse effect. Meronyms, hyponyms, topics, synonyms and hypernyms further improved the classification, in increasing order of importance. Finally, disambiguation was also found to benefit the classification. [PERSON] et al. [71] study the effect that incorporating ontology information in the feature selection process has on document clustering. First, they discuss a simple technique for feature selection and compare it against a word sense disambiguation process (WSD), where semantic relations have been considered in the document clustering. The simple procedure consists of a pre-processing step, which includes stop-word removal and stemming, and a cleaning step, where non-nouns are removed and only stemmed terms, identified as nouns are retained. Noun identification takes place with a simple lookup in the WordNet noun database. The WSD procedure replaces the selected nouns by their most appropriate senses (concepts) as used in the context of the document. Formally, if \(\delta(s_{q},s_{p})\) denotes the similarity between two senses \(s_{q}\) and \(s_{p}\), and \(S_{i}=\{s_{i1},s_{i2},\ldots,s_{ik}\}\) is the set of all senses associated with noun \(t_{  
measure determines the similarity of \(c_{1}\) and \(c_{2}\), otherwise the similarity is set to 0. The two techniques were evaluated on a standard dataset using the SVM classifier. Performance was measured with the macro-averaged F1 score. The authors report that the results match those of other top systems. Both imputed information gain and supervised Lin measure improved the classification, however the latter only marginally. They recognize that one limitation of their study is the small corpus size used to compute the semantic similarity measures and intend to experiment with other similarity measures, which do not depend on the corpus size.