In recent years, we witnessed a huge growth of multimedia data on the Web. New lower-cost technologies and greater bandwidth allowed the Web to evolve into a multimedia format. However, the lack of tools that can make multimedia format easily accessible on the Web led us to a non-searchable and non-indexable data of the Web, also known as Deep Web. In line with these observations, this dissertation addresses the problem of how to publish audio and video content on the Web. We present a tool and a novel approach that facilitates the indexing and retrieval of the objects with the help of traditional search engines. The tool automatically generates static Web pages that describe the content of the objects and organize this content to facilitate locating segments of the audio or video which correspond to the descriptions. The static Web pages can be translated to others languages to reach other user populations. An annotation process is also performed to embed machine-readable data into the Web pages. The dissertation also presents an in-depth experiment, publishing learning objects based on audio and video, to assess the efficacy of the technique.