A Comparative Analysis of Web Search Query: Informational Vs. Navigational Queries

The search engines are mainly used to retrieve relevant information. Information retrieval researchers show that queries are the basis for providing better search engine performance. The search query is becoming a means for users to search for their needed information. Web search query is one of the common search queries that is widely used in domain areas. However, the main challenge is the absence of a clear understanding of how web search query influences the users’ behavior on different web search engines. With the emergence of different types of a web search query, the understanding of user behavior on a web search query guides in improving the performance of many web search engines. Current research focused on using informational queries to search relevance information from a database while ignoring the importance of navigational queries. In this paper, we compared the informational and navigational type of a web search query that is mostly used in academic settings. Specifically, we examine the problems, solutions and techniques used in each of these types. We used a query log to conduct an experiment using BM25 mathematical model. The results indicated that the informational search query performed best because several keywords have been included to properly explain the queries. Also, language vocabularies used in informational queries contributed to better search performance. We believed that the outcomes of our comparisons will guide web search engine developers on the right search query for their web search engines

An Approach of a Personalized Information Retrieval Model based on Contents Semantic Analysis

En este trabajo se presenta una primera aproximación de un modelo de recuperación de información personalizada basado en el procesamiento semántico del contenido. El modelo propuesto reduce la sobrecarga de información innecesaria para los usuarios y mejora los resultados recuperados mediante la combinación de un procesamiento semántico de contenido aplicado a las consultas y documentos indexados, y la información de los perfiles de usuarios. La aplicabilidad de la propuesta fue evaluada en el contexto de un motor de búsqueda real, a través de consultas diseñadas por expertos en diferentes dominios y la medición de su rendimiento. Los resultados obtenidos fueron comparados con los del motor de búsqueda puesto a prueba, lográndose mejoras en cuanto a la precisión y exhaustividad.In this paper, an approach of a personalized information retrieval model based on the semantic processing of the content is proposed. The proposed model reduces the unnecessary information overload for users and improves the retrieval results through combining a content semantic processing applied to the queries and indexed documents, and information user processing from different perspectives. The applicability of the proposal was evaluated in the context of a real web search engine, through several queries designed by experts and associated to differents topics, and the measurement of their performance. The results were compared to those obtained by the search engine put to the test, achieving improvements the retrieval results.Este trabajo ha sido parcialmente financiado por el proyecto METODOS RIGUROSOS PARA EL INTERNET DEL FUTURO (MERINET), financiado por el Fondo Europeo de Desarrollo Regional (FEDER) y el Ministerio de Economía y Competitividad (MINECO), Ref. TIN2016-76843-C4-2-R