Semantic Web Mining: Using Ontology Learning and Grammatical Rule Inference Technique

Nowadays the Web has proved to be as a rich and extraordinary data source of information, where multiple domains can be accessed and mined. Mining Web data is referred as Web Mining. Some of the objectives of mining web data include finding relevant information discovering new knowledge from web personalized, web synthesis and learning about individual users. Amongst these the most common use is finding relevant information. We simply specify a set of keywords or query as a request or a reference and we get a list of pages, ranked as per similarity of query. Currently searching web face with one problem that many times outcome is not satisfactory because of irrelevance of the information. Searching the exact information from such a huge repository of unstructured web data is still main area of research interest. One solution to this problem is Semantic Web. The Semantic Web is an extension of current Web in which information is given as well defined meaning, hence enabling computers and people to work with better coordination. By using the existing web semantically new semantically structure can be exploited, and then the results of web mining can be improved, thereby building semantic Web. Semantic Web leads the idea of Ontology learning. Ontology learning, through which the outcome leads from the web unstructured data towards the exactness. Many researchers have suggested variety of methods for ontology learning especially concerned to specific data types. In this paper we suggest the process of Ontology learning for the extraction of semantics through Grammatical Rule Extraction Technique.