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The most fundamental step in semantic information processing (SIP) is to construct knowledge base (KB) at the human level; that is to the general understanding and conception of human knowledge. WordNet has been built to be the most systematic and as close to the human level and is being applied actively in various works. In one of our previous research, we found that a semantic gap exists between concept pairs of WordNet and those of real world. This paper contains a study on the enrichment method to build a KB. We describe the methods and the results for the automatic enrichment of the semantic relation network. A rule based method using WordNet's glossaries and an inference method using axioms for WordNet relations are applied for the enrichment and an enriched WordNet (E-WordNet) is built as the result. Our experimental results substantiate the usefulness of E-WordNet. An evaluation by comparison with the human level is attempted. Moreover, WSD-SemNet, a new word sense disambiguation (WSD) method in which E-WordNet is applied, is proposed and evaluated by comparing it with the state-of-the-art algorithm.