Semantic meta-search using cohesion network analysis

Abstract

Online searching is one of the most frequently performed actions and search engines need to provide relevant results, while maintaining scalability. In this paper we introduce a novel approach grounded in Cohesion Network Analysis in the form of a semantic search engine incorporated in our Hub-Tech platform. Our aim is to help researchers and people unfamiliar with a domain find meaningful articles online, relevant for their project scope. In addition, we integrate state-of-the-art technologies to ensure scalability and low response time, namely SOLR – for data storage and full-text search functionalities – and Akka – for parallel and distributed processing. Preliminary validations denote promising search results, the software being capable to suggest articles in approximately the same way as humans consider them most appropriate – 75% are close results and top 20% are identical to user recommendations. Moreover, Hub-Tech recommended more suitable articles than Google Scholar for our specific task of searching for articles related to a detailed description given as input query (50 + words).