Applying Genetic Algorithm In Query Improvement Problem

This paper presents an adaptive method using genetic algorithm to modify user’s queries, based on relevance judgments. This algorithm was adapted for the three well-known documents collections (CISI, NLP and CACM). The method is shown to be applicable to large text collections, where more relevant documents are presented to users in the genetic modification. The algorithm shows the effects of applying GA to improve the effectiveness of queries in IR systems. Further studies are planned to adjust the system parameters to improve its effectiveness. The goal is to retrieve most relevant documents with less number of non-relevant documents with respect to user's query in information retrieval system using genetic algorithm

Intelligent Fusion of Structural and Citation-Based Evidence for Text Classification

This paper investigates how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity, five derived from the citation structure of the collection, and three measures derived from the structural content, and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our empirical experiments using documents from the ACM digital library and the ACM classification scheme show that we can discover similarity functions that work better than any evidence in isolation and whose combined performance through a simple majority voting is comparable to that of Support Vector Machine classifiers

Personalization of Search Engine Services for Effective Retrieval and Knowledge Management

The Internet and corporate intranets provide far more information than anybody can absorb. People use search engines to find the information they require. However, these systems tend to use only one fixed term weighting strategy regardless of the context to which it applies, posing serious performance problems when characteristics of different users, queries, and text collections are taken into consideration. In this paper, we argue that the term weighting strategy should be context specific, that is, different term weighting strategies should be applied to different contexts, and we propose a new systematic approach that can automatically generate term weighting strategies for different contexts based on genetic programming (GP). The new proposed framework was tested on TREC data and the results are very promising

Técnicas evolutivas para la extracción automática de conocimiento

Esta línea de investigación propone el diseño, desarrollo y evaluación de técnicas automáticas para extracción de conocimiento, de tal forma que sean capaces de sobrellevar la búsqueda dentro de grandes espacios de información. Para ello se propone, en primera instancia, la resolución de un problema de interés general: el de reformulación automática de consultas. Una resolución automática para este problema podría ser utilizada en diversas aplicaciones, tales como monitorear un tópico de interés, especificar trackers temáticos sobre redes sociales, identificar entidades y relaciones entre entidades en grandes corpus de documentos o recolectar material para portales temáticos. Por sus características (alta dimensionalidad del espacio de búsqueda, carencia de subestructura optima, posibilidad de aprovechamiento de múltiples soluciones) el uso de computación evolutiva parece adecuado para abordar su resolución. Un primer aporte de esta línea dentro del área radica en la consideración de la in- corporación de operadores booleanos y otro tipo de modificadores a las consultas reformuladas y el control de la diversidad, ambos pensados como un mecanismo para lograr mayor expresión en las consultas y, por lo tanto, mayor poder para expresar los conceptos de interés involucrados. El segundo aporte consiste en proponer un marco de evaluación adecuado para la metodología desarrollada y el estudio y comparación con otras técnicas. Por último, el aporte final aborda la aplicación de los métodos desarrollados en dominios específicos tales como bioinformática (e.g. para identificación de interacciones entre entidades biológicas) o redes sociales (e.g. para realizar minería de opiniones mediante trackers temáticos).Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Enhancing recall and precision of web search using genetic algorithm

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to rapid growth of the number of Web pages, web users encounter two main problems, namely: many of the retrieved documents are not related to the user query which is called low precision, and many of relevant documents have not been retrieved yet which is called low recall. Information Retrieval (IR) is an essential and useful technique for Web search; thus, different approaches and techniques are developed. Because of its parallel mechanism with high-dimensional space, Genetic Algorithm (GA) has been adopted to solve many of optimization problems where IR is one of them. This thesis proposes searching model which is based on GA to retrieve HTML documents. This model is called IR Using GA or IRUGA. It is composed of two main units. The first unit is the document indexing unit to index the HTML documents. The second unit is the GA mechanism which applies selection, crossover, and mutation operators to produce the final result, while specially designed fitness function is applied to evaluate the documents. The performance of IRUGA is investigated using the speed of convergence of the retrieval process, precision at rank N, recall at rank N, and precision at recall N. In addition, the proposed fitness function is compared experimentally with Okapi-BM25 function and Bayesian inference network model function. Moreover, IRUGA is compared with traditional IR using the same fitness function to examine the performance in terms of time required by each technique to retrieve the documents. The new techniques developed for document representation, the GA operators and the fitness function managed to achieves an improvement over 90% for the recall and precision measures. And the relevance of the retrieved document is much higher than that retrieved by the other models. Moreover, a massive comparison of techniques applied to GA operators is performed by highlighting the strengths and weaknesses of each existing technique of GA operators. Overall, IRUGA is a promising technique in Web search domain that provides a high quality search results in terms of recall and precision