# Ontology-Based Feature Selection: A Survey
## 5 Ontology-Based Feature Selection
### Document Classification  
As presented in Table 1, there are several works related to ontology-based document classification, in different domains, using different approaches and ontologies. In the following paragraphs we provide insights to a selected representative set of those works. [PERSON] et al. (2018) use the WordNet ([PERSON] et al., 2018) lexical taxonomy (as an ontology) to classify Web text documents based on their semantic similarities. In the first phase, a number of filters are applied to each document to extract an initial vector of terms, called Bag of Words (BoW), which represent the document space. In particular, a Natural Language Processing Parser (NLPP) parses the text and extracts words in the form of tagged components (part of speech), such as verbs, nouns, adjectives, etc. Words that contain symbolic characters, non-English words, and words that can be found in pre-existing stopping word lists, are eliminated. Furthermore, in order to reduce redundancy, stemming algorithms are used to replace words with equivalent morphological forms, with their common root. In the second phase, all words in the initial BoW are examined for semantic similarities with categories in WordNet. Specifically, if a path exists in the WordNet taxonomy, from a word to a WordNet category via a common parent (hypernym), then the word is retained, otherwise it is discarded. Once the final set of terms has been selected, the feature vector for each document is generated by assigning a weight to each term. Authors use the Frequency-Inverse Document Frequency (_TFIDF_) statistical measurement, since it computes  
Figure 2: (a) Standard feature selection. (b) Ontology-based feature selection. the importance of a term \(t\), both in an individual document and in the whole training set. _TFIDF_ is defined as:  
\[TFIDF(t)=TF(t)\times IDF(t) \tag{1}\]  
where  
\[TF(t)=\frac{Number\ of\ occurances\ of\ term\ t}{Total\ number\ of\ terms\ in\ doc} \tag{2}\]  
and  
\[IDF(t)=log\frac{Total\ Number\ of\ docs}{Number\ of\ docs\ with\ term\ t} \tag{3}\]  
Effectively, terms that appear frequently in a document, but rarely in the overall corpus, are assigned larger weights. Authors compared against the Principal Component Analysis (PCA) method and report superior classification results. However, they recognize that a limitation in their approach is that important terms that are not included in WordNet will be excluded from the feature selection. [PERSON] et al. (2017), employ the Web to support feature extraction from raw text documents, which describe an entity (symbolized with \(ae\)), according to a given ontology of interest. In the first step, the OpenNLP (2018) parser analyzes the document and detects potential named entities (\(PNE\)) related to the \(ae\), as noun phrases containing one or more words beginning with a capital letter. A modified Pointwise Mutual Information (PMI) measure is used to rank the \(PNE\) and identify those that are most relevant to the \(ae\) according to some threshold. In particular, for each \(pne_{i}\in PNE\) probabilities are approximated by Web hit counts provided by a Web search engine,  
\[NE_{score}(pne_{i},ae)=\frac{WebHitsCount(pne_{i}\&ae)}{WebHitsCount(pne_{i})} \tag{4}\]  
In the second step, a set of Subsum Concepts (\(SC\)) is extracted from the retained Named Entities (\(NE\)). To do so, the text is scanned for instances of certain linguistic patterns that contain each \(ne_{i}\in NE\). Each pattern is used in a Web query and the resulting Web snippets determine the subsumer concepts representing the \(ne_{i}\). Next, the extracted \(SC\) are mapped to ontological classes (\(OC\)) from the input ontology. Initially, for each \(ne_{i}\) all its potential subsumer concepts are directly matched to lexical-similar ontological classes. If no matches are found then WordNet is used to expand the \(SC\) and direct matching is repeated. Specifically, the parents (hypernyms) in the WordNet hierarchy of each subsumer concept \(sc_{i}\) are added to \(SC\).