Our scheme consists of three components: answer medium selection, query Generation for Multimedia search, and multimedia data selection and presentation. This approach automatically determines which type of media information should be added for a textual answer. It then automatically collects data from the web to enrich the answer.

EXISTING SYSTEM:

Along with the proliferation and improvement of underlying communication technologies, community QA (cQA) has emerged as an extremely popular alternative to acquire information online, owning to the following facts. First, information seekers are able to post their specific questions on any topic and obtain answers provided by other participants. By leveraging community efforts, they are able to get better answers than simply using search engines.

In this paper, we propose a novel scheme which can enrich community-contributed textual answers in cQA with appropriate media data.

DISADVANTAGES OF EXISTING SYSTEM:

• Fully automated QA still faces challenges that are not easy to tackle, such as the deep understanding of complex questions and the sophisticated syntactic, semantic and contextual processing to generate answers.
• Existing cQA forums mostly support only textual answers unfortunately, textual answers may not provide sufficient natural and easy-to grasp information.
• The results of the media resource analysis
• For multimedia data selection and presentation, we propose a method that explores image search results to replace

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System : Pentium IV 2.4 GHz.
• Hard Disk  : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• Ram : 512 Mb.

SOFTWARE REQUIREMENTS:

• Operating system : – Windows XP.
• Coding Language : NET, C#.Net.
• Data Base :  SQL Server 2005