Information content measures of semantic similarity between documents based on Hadoop system
Retrieving documents in response to the user's query is the most commonly text retrieval task. For our work, we have mainly focused on detecting the semantic similarity between documents in large documents collection and queries. In this paper, we investigated MapReduce as a specific framework for managing distributed processing in dataset pattern and semantic similarity measures of documents. Then we study the state of the art of different approaches for computing the semantic similarity of documents. We propose an approach based on parallel algorithm of semantic similarity measures using MapReduce and WordNet to detect the relevant documents in the face of the query. Finally, we are leading basic experiments to assess the performance of the proposed approach and noted the leverage of Hadoop and MapReduce to the semantic similarity measures between documents.
2016 International Conference on Wireless Networks and Mobile Communications (WINCOM)