Context-based information analysis for the Web environment

Finding the relevant set of information that satisfies an information request of a Web user in the availability of today's vast amount of digital data is becoming a challenging problem. Currently, available Information Retrieval (IR) Systems are designed to return long lists of results, only a few of which are relevant for a specific user. In this paper, an IR method called Context-Based Information Analysis (CONIA) that investigates the context information of the user and user's information request to provide relevant results for the given domain users is introduced. In this paper, relevance is measured by the semantics of the information provided in the documents. The information extracted from lexical and domain ontologies is integrated by the user's interest information to expand the terms entered in the request. The obtained set of terms is categorized by a novel approach, and the relations between the categories are obtained from the ontologies. This categorization is used to improve the quality of the document selection by going beyond checking the availability of the words in the document by analyzing the semantic composition of the mapped terms.