An intelligent model to construct specialized domain ontologies

Search engines and information retrieval (IR) systems provide a mechanism for users to access large amounts of information available through the Internet. However, in order to find the desired information, the user has to go through a staggering amount of information retrieved from highly dynamic resources. Experimental results show that the approach proposed for constructing specialized domains improves the precision of information retrieval. Our approach involves enriching the user´s query with related linguistic semantic and statistical semantic related concept terms. We employ natural language process (NLP) techniques such as WordNet engine to enrich the user´s query with semantic lexical synonymous terms and a probabilistic topic model such as latent dirichet allocation (LDA) to extract highly ranked topic from a query´s retrieved information. Furthermore, an intelligent learning algorithm, reinforcement learning (RL) is integrated into the design to assist end users in selecting the concept domains that are most relevant to their needs.