Using Semantic Web for Information Retrieval Based on Clonal Selection Strategy

It is well known that information retrieval systems based entirely on syntactic contents have serious limitations. In order to achieve high precision and recall on IR systems, the incorporation of natural language processing techniques that provide semantic information is needed. For this reason, by determining the semantic for the constituents of documents, a clustering method is presented in this paper. The goal is to find the conjoined point which can combine the advantages of both textual part and visual part, and to use for IR systems. It can help to well extract the meaning of a term. Thus, we can take the formalized meaning, instead of the lexical term, and consequently resolve the word sense ambiguity. Experimental results show that the proposed SWCSM model significantly improves the average precision and recall and reduces the overall search time.