# Ontology-Based Feature Selection: A Survey
## 5 Ontology-Based Feature Selection
### Other Applications
Similar to the previous work, authors map important aspects of noise identification to ontological concepts, which serve as features for reasoning. They propose an ontology-based system for identifying noise sources in agricultural machines. At the same time, their method provides an extensible framework for sharing knowledge for noise diagnosis. Essentially, they seek to encode prior knowledge relating noise sound signals (targets) with vibrational sound signals (sources) in an ontology, equipped with rules, and perform reasoning to identify noise sources based on the characteristics of test input and output sound signals (parotic noise). In order to build the ontology, first, professional experience, literature, and standard specifications were surveyed to extract the concepts related to NVH. The Protege tool was used to convert the concept knowledge into an OWL ontology and implement the SWRL rules, which match sound source and parotic noise signals. The Pellet tool is employed for reasoning. To quantify the signal correlations, the time signals are converted to the frequency domain and the values for seven common signal characteristics are calculated. Specifically, relation of the frequency of the parotic signal to the ignition frequency, peak frequency, Pearson coefficient, frequency doubling, loudness, sharpness, and roughness. The effectiveness of the method was demonstrated in a use case, where the prototype system correctly identified the main noise source. After improving the designated area the noise was significantly reduced. The authors argue that the continuous improvement in the knowledge base and rule set of the ontology model has the potential to allow the design system to perform reasoning that simulates the thinking process of the expert in the field of NVH. [PERSON] et al. [99] develop an ontological framework to classify buildings based on data acquired with Airborne Laser Scanning (ALS). They followed five steps. Initially, they pre-processed the ALS point cloud and applied the eCognition software to convert it to raster data, which were used to delineate buildings and remove irrelevant objects. Additionally, they obtained values for 13 building features grouped in four categories: extent features, which define the size of the building objects, shape features, which describe the complexity of building boundaries, height, and slope of the buildings' roof. In the next step, human expert knowledge and knowledge available in literature were employed to define three general purpose building ontology classes, independent of the application and the data at hand, namely Residential/Small Buildings, Apartment/Block Buildings, and Industrial and Factory Buildings. In order to identify the metrics that were mostly relevant to the identification of building types, a set with 45 samples was used to train a random forest classifier with 500 trees and \(\sqrt{m}\) features (m number of input features). The feature selection process identified slope, height, area, and asymmetry as the most important features. The first three were modeled in the ontology with empirically determined thresholds by the RF classifier. Finally, building type classification was carried out based on the formed ontology. The classification accuracy was assessed by means of precision, recall, and \(F\)-measure and the authors reported convincing results for class A while classes B and C had less accurate results. However, they argue that their method can prove useful for classifying building types in urban areas. Finally, two interesting applications of ontology-based feature selection algorithms concern the Recommendation Systems (RS) and the information security/privacy research areas. In [91], [PERSON] et al. develop a filter-based feature selection algorithm by incorporating ontology-driven data summarization for Linked Data (LD)-based Recommendation System (RS).