A Multi-Objective Genetic Algorithm for Learning Linguistic Persistent Queries in Text Retrieval Environments

Persistent queries are a specific kind of queries used in information retrieval systems to represent a user’s long-term standing information need. These queries can present many different structures, being the “bag of words” that most commonly used. They can be sometimes formulated by the user, although this task is usually difficult for him and the persistent query is then automatically derived from a set of sample documents he provides. In this work we aim at getting persistent queries with a more representative structure for text retrieval issues. To do so, we make use of soft computing tools: linguistic information is considered for weighting the terms of Boolean queries by means of ordinal linguistic values (linguistic queries), and multiobjective evolutionary algorithms are applied to build the linguistic persistent query. Experimental results will show how using an expressive linguistic information-based query structure and a proper learning process to derive it, we can get more flexible, comprehensible and expressive user profiles.