Information Retrieval (IR) techniques have been extensively studied since late 1940s and achieved great success evidenced particularly by popular online search engines. However, various classical information retrieval models also have witnessed criticism for emphasizing computation with occurrence of words while ignoring semantics (i.e. meaning of words, search context and etc). Research of the Semantic Web in recent years has provided an opportunity to migrate from mere word-computing to semantic-enhanced information search and retrieval. In this paper, we describe a methodology by combing the Semantic Web, information retrieval, information extraction and social network analysis techniques to elicit semantics from available metadata and ontology in order to develop a semantic-enhanced information search and retrieval system.