Motivated to move their business applications to cloud computing. Keywords: Cloud Ontology, Query Composition, Semantic Search, Word Sense Disambiguation,ĭue to the characteristics of cloud computing, many organizations and customers are being Have shown that 80% of S&M and 82% of HR relevant queries have been assigned correctly. Results, the proposed mechanism has achieved 90%, and 86% in the F-Score measure forĬlassifying S&M, and HR cloud services, respectively. Streaming and Multimedia (S&M), and Human Resource (HR). Mechanism on two sets of cloud services related to concepts in the referenced cloud ontology Service searching queries using English natural language.

Secondly, anticipating a system that enables non-specialized users to flexibly compose their cloud Is two folded: firstly, classifying cloud services into their corresponding cloud ontology concepts Using BabelNet knowledge base and FP-Growth mining algorithm. The gap between English natural language terms and concepts of the referenced cloud ontology In this paper, we propose a new mechanism bridging But, it is difficult to truly match such queriesīecause of natural language ambiguities. Users always prefer toĬompose queries using their natural languages. The difficulty to select suitable concepts from referenced cloud ontology. Accordingly, constructing a suitable cloud serviceĭiscovery mechanism will exclude an immense obstacle especially for non-expert users due to Ontologies as one of the main building blocks. Hand, the majority of cloud service discovery and repository frameworks depend on cloud In spite of the rapid growth of cloud services, a wide spectrum of enterprises and users do notīuy such services as a result of insufficient knowledge about cloud technology. Hassan of Computers and Informationįatma A. Mapping Lexical Gaps In Cloud Ontology Using BabelNet and International Journal of Computer Science & Security (IJCSS), Volume (13) : Issue (2) : 2019 36 Mapping Lexical Gaps In Cloud Ontology Using BabelNet and FP-Growth For matching users' queries, the results have shown that 80% of S&M and 82% of HR relevant queries have been assigned correctly. According to our experimental results, the proposed mechanism has achieved 90%, and 86% in the F-Score measure for classifying S&M, and HR cloud services, respectively. We have applied the proposed mechanism on two sets of cloud services related to concepts in the referenced cloud ontology Streaming and Multimedia (S&M), and Human Resource (HR). The contribution of this paper is two folded: firstly, classifying cloud services into their corresponding cloud ontology concepts secondly, anticipating a system that enables non-specialized users to flexibly compose their cloud service searching queries using English natural language. In this paper, we propose a new mechanism bridging the gap between English natural language terms and concepts of the referenced cloud ontology using BabelNet knowledge base and FP-Growth mining algorithm. But, it is difficult to truly match such queries because of natural language ambiguities.

Users always prefer to compose queries using their natural languages. Accordingly, constructing a suitable cloud service discovery mechanism will exclude an immense obstacle especially for non-expert users due to the difficulty to select suitable concepts from referenced cloud ontology. On the other hand, the majority of cloud service discovery and repository frameworks depend on cloud ontologies as one of the main building blocks. In spite of the rapid growth of cloud services, a wide spectrum of enterprises and users do not buy such services as a result of insufficient knowledge about cloud technology.

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