# Ontology-Based Feature Selection: A Survey
## 5 Ontology-Based Feature Selection
### Document Classification
In order to determine which parent concepts are mostly relevant to the named entity \(ne_{i}\), a search engine is queried for common appearances of the \(ae\) and the \(ne_{i}\). The returned Web snippets are used to determine which parent synsets of \(sc_{i}\) are mostly related to \(ne_{i}\). Synsets in Wordnet are groupings of words from the same lexical category that are synonymous and express the same concept. Finally, a Web-based version of the PMI measure, defined as  
\[SOC_{score}(soc_{i},ne_{i},ae)=\frac{WebHitsCount(soc_{i}\&ne_{i}\&ae)}{WebHitsCount (soc_{i}\&ae)} \tag{5}\]  
is used to rank each of the extracted ontological classes (\(soc_{i}\)), related to a named entity. The \(soc_{i}\) with the highest score that exceeds a threshold is used as annotation. The authors tested their method in the Tourism domain. For the evaluation, they compared precision (ratio of correct feature to retrieved features) and recall (ratio of correct features to ideal features) against manually selected features from human experts. They report 70-75% precision and more than 50% accuracy and argue that such results considerably assist the annotation process of textual resources. [PERSON] et al. (2018) reduce the dimensionality of the text classification problem by determining an optimal set of concepts to identify document context (semantics). First, the document terms are mapped to concepts derived from a domain-specific ontology. For each set of documents of the same class, the extracted concepts are organized in a concept hierarchy. A hill-climbing algorithm [60] is used to search the hierarchy and derive an optimal set of concepts that represents the document class. They apply their method to classification of medical documents and use the Unified Medical Language System (UMLS) [61] as the underlying domain-specific ontology. UMLS query API is used to map document terms to concepts and to derive the concept hierarchy. For the hill-climbing heuristic, a frequency measure is assigned to each leaf concept node. The weight of parent nodes is the sum of the children's weights. Based on the assigned weights, a distance measure between two documents is derived, and used to define the fitness function. Test documents undergo the same treatment and are classified based on the extracted optimal representative concepts. For their experiments the authors use a KNN classifier and report improved accuracy, but admit that an obvious limitation of their method is that it is only applicable in domains that have a fully developed ontology hierarchy. [PERSON] et al. [62] obtain document vectors defined in a vector space model. This is accomplished in terms of the following steps. First, after identifying all the words in the documents, they remove the stop-words from the word data base, creating a BoW. Next, a stemming algorithm is applied to assign each word to its respective root word. Phrase frequency is estimated using a Part of Speech (PoS) tagger. Next, they apply the Maximal Frequent Sequence (MFS) [63] to obtain the highly frequent terms. MFS is a sequence of words that is frequent in a collection of documents, while it is not related to any other sequence of the same kind [63]. The final set of features is selected by examining similarities with ontology-based categories in WordNet [56] and applying a wrapper approach. Using the _TFIDF_ statistical measure weights are assigned to each term. Finally, the classifier is trained in terms of the naive Bayes algorithm. [PERSON] et al. [64] also address feature selection in the context of classification of medical documents. In particular, they aim at distinguishing clinical notes that reference Coronary Artery Disease (CAD) from those that do not. Similarly to [59], they use a query tool (MetaMap) to map meaningful expressions, in the training documents to concepts in UMLS. Since, their target is CAD documents, they only keep concepts such as \"Disease or Syndrome\" and \"Sign or Symptom\" and discard the rest.