Semantic Computing, Cloud Computing, and Semantic Search Engine

Abstract

Semantic Computing extends Semantic Web both in breadth and depth. It bridges, and integrates, technologies such as software engineering, user interface, natural language processing, artificial intelligence, programming language, grid computing and pervasive computing, among others, into a complete and unified theme. Cloud Computing, the dream of computing as a utility, shifts user programs and data from personal computers to the clouds, providing all kinds of resources over the Internet as services to customers and letting customers pay for them in a way much like they pay for traditional utilities such as electricity. This paper analyzes both Semantic Computing and Cloud Computing, and introduces the Semantic Search Engine, an infrastructure and implementation of Semantic Computing, that demonstrates how Semantic Computing can benefit Cloud Computing.

Cloud computing service discovery framework for IaaS and PaaS models

“…Architecture of Semantic Computing [22] As it is shown in the architecture, the search engine analyzes and converts signals such as pixels and words (content) to meanings (semantics); it also provides the information resources for Semantic Integration and Semantic Applications [20]. With the help of this architecture, a semantic service search engine can be developed.…”

“…Unlike traditional computing, semantic computing is being used to extract or process the content and semantics of passive data and active processes, search engines that are using this method are very effective with resourceintensive services [20,21]. Figure 6 is the general architecture of semantic computing, which is being used as the fundamental model to develop a service discovery framework similar to a search engine.…”

“…It works similar to a regular search engine however; the focus is on cloud services only. The model provides the user with an interface in order to search for services, the query is translated into service query description language (SQDL) which processes the query and enables it for further processes [20]. There is also another language used in this model called service capability description language (SCDL), which is for describing the capability of each service [20].…”

“…The model provides the user with an interface in order to search for services, the query is translated into service query description language (SQDL) which processes the query and enables it for further processes [20]. There is also another language used in this model called service capability description language (SCDL), which is for describing the capability of each service [20]. A matcher is being used in order to do the comparison between the SQDL and SCDL, then if the user finds the desired service it uses the Service Invoker that gets the final results from the matching of SQDL and SCDL.…”

Transformative and Innovative E-Gov for the Next Generation: Linkages of Back Offices for One-Stop Portal

“…In addition, to provide customization functionality for the portal i.e. mash-up and access to external contents, information extraction techniques 9 are necessary, for example, the use of hidden tables in web pages to induce entity relationships [12], statistical entity extraction [19], entity disambiguation using Markov Logic Networks, and semantic web with cloud computing [20].…”