Semantic tag recommendation based on associated words exploiting the interwiki links of Wikipedia

The volumes of multimedia content and users have increased on social multimedia sites due to the prevalence of smart mobile devices and digital cameras. It is common for users to take pictures and upload them to image-sharing websites using their smartphones. However, the tag characteristics deteriorate the quality of tag-based image retrieval and decrease the reliability of social multimedia sites. In this article, we propose a semantic tag recommendation technique exploiting associated words that are semantically similar or related to each other using the interwiki links of Wikipedia. First, we generate a word relationship graph after extracting meaningful words from each article in Wikipedia. The candidate words are then rearranged according to importance by applying a link-based ranking algorithm and then the top-k words are defined as the associated words for the article. When a user uploads an image, we collect visually similar images from a social image database. After propagating the proper tags from the collected images, we recommend associated words related to the candidate tags. Our experimental results show that the proposed method can improve the accuracy by up to 14% compared with other works and that exploiting associated words makes it possible to perform semantic tag recommendation