Improving relational aggregated search from big data sources using stacked autoencoders

Relational aggregated search (RAS) is defined as a complementary set of approaches in which the relations between information nuggets are taken into account. From this viewpoint, the relational aggregated search should retrieve information nuggets and their relations, that are used to coherently assemble the final search result. Traditional approaches used for RAS are based on Information Extraction (IE) techniques and knowledge bases (ontologies, linked data) as the major sources for identifying and extracting useful relations between different results. However, with the big data collections stored on the web, the different results obtained from the different verticals for a given query are not homogeneous. Therefore, the challenge is to extract the different features related to each vertical result, and to recognize the various relationships between these results. In this paper, we propose a new solution based on a Deep Learning architecture, specifically, stacked autoencoders. This approach enables us to exploit the advantages of deep neural networks. Experimental results show that our model achieves good accuracy for 30 queries, and demonstrate that the use of stacked autoencoders for representation learning is more beneficial for clustering tasks and can considerably improve aggregated search results.