# Ontology-Based Feature Selection: A Survey
## 7 Conclusions  
This study provided an overview of ontology-based feature selection for classification processing. The presented approaches in selected application domains showed that ontologies can effectively uncover dominant features in diverse knowledge domains and can be integrated into existing feature selection and classification algorithms. Specifically, in the context of text classification, domain-specific ontologies combined mainly with the WordNet taxonomy, can be utilized to map terms in documents to concepts in the ontology, thus replacing specific term-based document features with abstract and generic concept-based features. The latter capture the content of the text and can be used to train accurate and efficient classifiers. In the field of manufacturing engineering, ontologies can be employed to map human knowledge to concepts that serve as features for case-based reasoning and support decision making, such as selection of manufacturing process or noise source identification. In the domain of urban management, building type recognition can be facilitated by ontology. Moreover, the benefits of using an ontology-based framework to drive feature selection were investigated regarding software development/engineering applications such as recommendations systems and security information/privacy approaches. Finally, certain open issues and challenges were discussed and a number of relevant problems were identified. Although, this survey is by no means exhaustive, it demonstrates the broad applicability and feasibility of ontology-based feature extraction and selection.  
[PERSON] investigated and wrote the classification, feature selection problem, and wrote the first draft of the paper; [PERSON] proposed the idea, and investigated the ontology-based feature problem; [PERSON] investigated the ontology-based frameworks and wrote the respective sections; All authors contributed to the final version of the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
**Acknowledgments:** The authors want to thank the reviewers for their effort to provide their valuable comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations**  
The following abbreviations are used in this manuscript:  
ALS  Airborne Laser Scanning  
BoC  Bag of Concepts  
BoW  Bag of Words  
CAD  Coronary Artery Disease  
CARs  Class Association Rules  
CBR  Case-based reasoning subsystem  
DSS  Decision support system  
DC  Dublin Core  
DT  Decision Tree  
EEL  Entity Extraction and Linking  
FE  Feature Engineering  
FOAF  Friend Of A Friend  
IG  Information Gain  
IGR  Information Gain Ratio  
KNN  K-Nearest neighbor  
LCS  Least Common Subsumer  
LD  Linked data  
LR  Logistic Regression  
LSVM  Linear Support Vector Machine  
LVQ  Learning Vector Quantization  
MFS  Maximal frequent sequence  
MPS  Suitable manufacturing process  
NB  Naive Bayes  
NDF-RT  National Drug File - Reference Terminology  
NE  Named entities  
NER  Named-Entity Recognition  
NLPP  Natural Language Processing Parser  
NVH  Noise, Vibration and Harshness  
OC  Ontological classes  
OWL  Web Ontology Language  
PCA  Principal Component Analysis  
PMI  Pointwise Mutual Information  
PoS  Part of speech  
PSO  Particle Swarm Optimization  
RBC  Rule-Based Classification  
RDF  Resource Description Framework  
REL  Relation Extraction and Linking  
RDFS  RDF Scheme  
RF  Random Forest  
RS  Recommendation (or Recommender) systems  
SC  Subsumer concept  
SOM  Self-organizing Map