Collaborative Information Retrieval Model Based on Fuzzy Clustering

Abstract

The collaborative approach has shown interest in several fields of application, particularly in information retrieval to satisfy a need for shared information. Despite this collaboration, the search for relevant information is always a tedious task as long as the mass of information continues to increase, part of which is a source, while other parties represent comments on these sources. It is obvious that nowadays we witness an explosion of multimedia documents so that multimedia information retrieval techniques remain insufficient to satisfy the needs of the user despite the collaborative framework: multimedia-type documents cannot be rich in information and more specifically the video documents. We consider, therefore, annotations as a new source of information. In addition to their relevance, we notice that annotations express generally brief ideas using some words that they cannot be comprehensible independently of his context. To use them, a classification is considered necessary. The emergence of new annotations should be considered and therefore the classification should be extended. A centroid is determined in a virtual way to represent each annotation class. From where, the interest to use the fuzzy classification to know which elements can belong to several clusters. It consists, in a calculation of the center of gravity of all the existing classes. This is the reason why; we proposed a fuzzy clustering-based annotation. In the experiments, we tried to consider a relevance feedback system based on confidence network considering new relevant classified annotations as a source of information. To validate this model, we have carried out a set of experiments and we have obtained encouraging results.