Cognitive Agents for Web Based Search Engines: A Review

Abstract

Search Engine (SE) is the most used information retrieval tool in the present scenario. In spite of the huge involvement of users in search engines, their limited capabilities to understand the user context and emotions places high load on the user to maintain the search momentum. Thus the research is being done on the web search process including the users. The aim is to reduce the contextual and emotional mismatch between the SE's and users. Personalized user query processing, current knowledge updation on users, information retrieval effectiveness and user satisfaction are the major challenges to be addressed in this field. To meet the high level requirements of the users and to improve the intellectualization level of the search, various cognitive agent based SE models are proposed in the literature. The challenges of the cognitive agents in present search engine scenario are obtaining sufficient percepts from the environment, understanding it, gathering the knowledge and retrieving the information from the huge volume of data. Recent advancements in the field of semantic search engines, knowledge gathering techniques and information retrieval methods are discussed which can better the cognitive agent based search engines by addressing the challenges. Overall the goal is to enhance the search experience by fewer repetitions of queries and making it easier for the users to retrieve the highly relevant documents of their interest.