Semantic web is a kind of webs that is able to describe things to be understood by computers. Automatically answering any query without human interactions is one of the key challenges in computer science area. Semantics can help in answering such queries. Consequently, extracting information from unstructured documents and transforming them into semantic web form is an important trend. Semantic web mining is a combination of two trends; semantic web and web mining. Our extracting and structuring system clarify the meaning of the web mining. The obtained data converted to the semantic web format. And so, the semantic web mining trend was illustrated. This paper concentrates on extracting data from the web page tables. Data on the Web in the HTML tables are mostly structured. However; we usually do not know the structure in advance. Thus, data of interest cannot be directly queried. Data extraction and structuring system is proposed to put data extracted into the semantic web form. After putting extracted data in the semantic web format, it can be queried using semantic web query language. Experimental results show that the data of interest can be located and build its new structure using semantic web. Keywords semantic web; information extraction; information structuring; natural language processing; wrapper generation; semantic web mining; extracting and structuring data. 1.

Increasingly data on the Web is stored in the form of Semantic Web data. Because of today’s information overload, it becomes very important to store and query these big datasets in a scalable way and hence in a distributed fash-ion. Cloud Computing offers such a distributed environment with dynamic reallocation of computing and storing resources based on needs. In this work we introduce a scalable distributed Semantic Web database in the Cloud. In order to reduce the number of (unnecessary) intermediate results early, we apply bloom filters. Instead of computing bloom filters, a time-consuming task during query processing as it has been done traditionally, we precompute the bloom filters as much as possible and store them in the indices besides the data. The experimental results with data sets up to 1 billion triples show that our approach speeds up query processing significantly and sometimes even reduces the processing time to less than half. TYPE OF PAPER AND KEYWORDS