Evaluating the Performance of Semantic Service Search Engine based-on SVM

Abstract

Semantic service search engine is designed for DE, to provide semantic search support. Digital ecosystem (DE) is comprised of heterogeneous and distributed species which can play the dual role of service provider and service requester. DE needs to provide a reliable and trustworthy link between service providers and service requesters. So, semantic service search engine is a conceptual framework of service-ontology-based system which delivers semantic search support to DE. This paper is intended to measure the performance of the semantic service search engine based-on Support Vector Machine (SVM). The performance is evaluated through six performance indicators of information retrieval (IR). They are Precision, Recall, Mean average precision, Harmonic mean, Fallout rate and Mean Reciprocal Rank. The conclusion to this evaluation and future works are provided in the final section. General TermsQuality of service (QoS) evaluation, semantic service search engine, support vector machine, information retrieval, performance indicators.