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An Intelligent Database Application for the Semantic Web

Semantic Web is one of the important research fields that came into light recently. Its major concern is to convert the World Wide Web from just a huge repository of unrelated text, into useful linked pieces of information. Linking the information is not only based on text similarity, but mainly on the meanings and realworld relations between items. In this research, we use the techniques of the semantic web, to create formal definition rules for some kind of unstructured data, and to query this data for information that might be not explicitly stated, and that would otherwise be very difficult to extract. For this purpose, we develop an integrated system that can extract data from both unstructured and structured documents, and then answer users' queries using the extracted data together with some inference rule that help to deduce more information from this data. As an example of this data, we take the social relationships found in death-ads in newspapers. These ads contain both implicit and explicit information about various relationships among people. These relationships, if arranged and organized in a proper way, can be used to extract and infer other hidden, not explicitly mentioned relationships.