Online Knowledge bases, such as WordNet, are utilized for semantic information processing. However, research indicates the existing knowledge base cannot cover all concepts used in talking and writing in the real world. This research suggests a method that enriches WordNet concepts by analyzing Wikipedia document set to resolve this limitation. Web document tagging generally chooses core words from a document itself. However, the core words are not standardized taggers. Thus, users should make an effort to grasp the tagged words first in the retrieval. This paper proposes methods to utilize titles (Wiki concept) of Wikipedia documents and to find the best Wiki concept that describes the Web documents (target documents). In addition to these methods, the research tries to classify target documents into a Wikipedia category (Wiki category) for semantic document interconnections.