FNN-ONTOCOM: A Hybrid Cost Estimation Approach Using Fuzzy and Neural Network for Ontology Engineering

Abstract

Ontology engineering is crucial for many areas such as information retrieval systems, data integration facilities, and basic decision support systems. Nevertheless, estimating the cost of ontology engineering projects is notoriously difficult to achieve. This challenge stems from the complexity and evolving nature of such projects. To solve this difficulty, we propose to improve the accuracy of cost estimation through a hybrid methodology that combines Fuzzy Ontology Cost Estimation Model (F-ONTOCOM) and Artificial Neural Networks (ANN). Fuzzy logic is used in our model to capture linguistic variables and other complex relationships within the scope of cost estimation. At the same time, ANN allows for the recognition of complex nonlinear interactions, enhancing the overall accuracy of prediction. This integration of fuzzy logic and neural networks leads to enhancements in the model's robustness, adaptability, and precision. Our approach features a methodology for 148 ontology engineering projects that include, but are not limited to, data scraping and preprocessing, fuzzy inference system design, neural network training, and validation processes. The results showed that the hybrid approach was champion over the traditional estimation approach in terms of effort estimation, Mean Relative Error (MRE), Mean Magnitude of Relative Error (MMRE), and the predictive accuracy over 21 randomly selected ontology projects.