The World Wide Web (WWW), one of the world's most important sources, holds a huge amount of available information. Many people have exploited the web as a virtual storage. In spite of being publicly and readily available, web data can hardly be properly queried or manipulated as done, for instance, in traditional databases, because most web data may be represented heterogeneously. There is a needs for systems to help people to cope with the heterogeneous information.; An increasing demand for Web data leads to the question of how this information can be extracted and how semantically useful structures are mapped. Schema mapping in life sciences is complicated by several factors: heterogeneity and non-standard naming of scientific objects, and in many instances traditional mapping techniques fail completely. Therefore, advanced information extraction and schema, mapping techniques are required to process Web data automatically.; An impressive and unprecedented schema mapping technique has been designed in order to match seemingly unrelated terms using data type information and graph grammar mining technique.
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