Ontology-based Resource Description and Discovery Framework. The ontology-driven approaches for search and discovery are increasing because of the power behind the semantic representation of linked data, including the description of resources and devices. Thus, in [64] we find an approach for an ontology-based resource description framework, developed particularly for ICT energy management purposes, where the focus is on energy- related semantic of resources and their properties. It proposes a scalable resource discovery method in the large and dynamic collections of ICT resources, based on semantics similarity inside a federated index using a Bayesian belief network. The framework allows users to identify the cleanest resource deployments in order to achieve a given task, taking into account the available energy source. This approach also uses the RDF data model to describe the resources to be stored in the database of the discovery system and uses a semantic analyzer to perform information processing based on such descriptions in order to determine the association of resources to the ontology concepts. The end user stack begins with a request for resources. The query is analyzed and keywords are extracted and processed by the semantic analyzer. Based on its knowledge base, it determines the required resources and their locations. If a resource is available, it will be triggered. In order to provide a powerful search method based on the proposed ontology, this architecture uses a Bayesian semantic graph which combines semantic inference and probability. The key idea is to integrate semantic links and reasoning rules which determine the cluster the resource belongs to by processing keywords and taking into account the context. As proposed in its ontology, a resource will be placed in one of the three categories: computing, storage and network. However, a user query like (RAM=64Mb, IP address=10.0.0.1) may lead to a confusion, because the result can be a server or a router. If further information, such as “bandwidth=1G” is added, a more accurate network resource could be found. The Bayesian semantic analyzer is used to deal with such confusion. It processes all the words in each resource description or user query and calculates the probability that the resource belongs to each cluster. Such a mechanism improves significantly the search operation. In order to define the concepts and clusters for the Bayesian network, the architecture builds a probabilistic table, as proposed in [65], which assigns joint probability va...
