Enriched semantic information processing using WordNet based on semantic relation network

The Semantic Web is an evolving extension of the World Wide Web in which the semantics of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. Semantic information processing is used to construct knowledge base at the human level. The most fundamental step in semantic information processing (SIP) is to construct knowledge base (KB) at the human level. WordNet has been built to be the most systematic and as close to the human level and is being applied actively in various works. Consequently, search results corresponding to different meanings may be retrieved, making identifying relevant results inconvenient and time-consuming. It has been found that a semantic gap exists between concept pairs of WordNet and those of real world. A study on the enrichment method to build a Knowledge Base was proposed here. 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. Moreover, WSD-SemNet, a new word sense disambiguation method in which E-WordNet is applied for Semantic Information Processing.