DOCODE-Lite: A Meta-Search Engine for Document Similarity Retrieval

The retrieval of similar documents from large scale datasets has been the one of the main concerns in knowledge management environments, such as plagiarism detection, news impact analysis, and the matching of ideas within sets of documents. In all of these applications, a light-weight architecture can be considered as fundamental for the large scale of information needed to be analyzed. Furthermore, the relevance score for documents retrieval can be significantly improved using several previously built search engines and taking into account the relevance feedback from users. In this work, we propose a web-services architecture for the retrieval of similar documents from the web. We focus on software engineering to support the manipulation of users’ knowledge into the retrieval algorithm. An human evaluation for the relevance feedback of the system over a built set of documents is presented, showing that the proposed architecture can retrieve similar documents by using the main search engines. In particular, the document plagiarism detection task was evaluated, for which its main results are shown.