Prediction of relevance between requests and web services using ANN and LR models

Authors:
Keyvan Mohebbi;Suhaimi Ibrahim;Norbik Bashah Idris
Affiliations:
Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia (UTM), Malaysia;Advanced Informatics School (AIS), Universiti Teknologi Malaysia (UTM), Malaysia;Advanced Informatics School (AIS), Universiti Teknologi Malaysia (UTM), Malaysia
Venue:
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2013

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Abstract

An approach of Web service matching is proposed in this paper. It adopts semantic similarity measuring techniques to calculate the matching level between a pair of service descriptions. Their similarity is then specified by a numeric value. Determining a threshold for this value is a challenge in all similar matching approaches. To address this challenge, we propose the use of classification methods to predict the relevance of requests and Web services. In recent years, outcome prediction models using Logistic Regression and Artificial Neural Network have been developed in many research areas. We compare the performance of these methods on the OWLS-TC v3 service library. The classification accuracy is used to measure the performance of the methods. The experimental results show the efficiency of both methods in predicting the new cases. However, Artificial Neural Network with sensitivity analysis model outperforms Logistic Regression method.