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As the size of experimental data, documents, and web pages grows larger, it will be difficult for scientists to search for appropriate data and grid web-services through keyword-based grid search portal interfaces. A well designed and dynamically updated knowledge base can help understand user queries when they are structurally different, but semantically correlated, with actual data stored in a grid or cloud. In this paper we discuss the implementation of a knowledge base creation tool for grid and cloud based wikipages. In order to achieve accurate results in the query match-making process, we store the knowledge base in the rich OWL format and update it automatically with subsequent inference of new facts. Our framework extracts grid and cloud related wikipages and web pages using syntactic Link Grammar Parser and creates core ontology models specific to the grid and cloud domain. In this paper we describe two core components of the ontology generation framework - an information extraction framework and a core ontology model. We present the accuracy of search in terms of precision and recall and its relationship with the dynamically updated ontological domain knowledge base.