Antonio Gabriel López-Herrera

Science mapping software tools: Review, analysis, and cooperative study among tools

Science mapping aims to build bibliometric maps that describe how specific disciplines, scientific domains, or research fields are conceptually, intellectually, and socially structured. Different techniques and software tools have been proposed to carry out science mapping analysis. The aim of this article is to review, analyze, and compare some of these software tools, taking into account aspects such as the bibliometric techniques available and the different kinds of analysis.

A Model of an Information Retrieval System with Unbalanced Fuzzy Linguistic Information

Most information retrieval systems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express the weights of queries and the relevance degrees of documents. However, to improve the system–user interaction, it seems more adequate to express these linguistic weights and degrees by means of unbalanced linguistic scales, that is, linguistic term sets with different discrimination levels on both sides of the middle linguistic term. In this contribution we present an information retrieval system that accepts weighted queries whose weights are expressed using unbalanced linguistic term sets. Then, the system provides the retrieved documents classified in linguistic relevance classes assessed on unbalanced linguistic term sets. To do so, we propose a methodology to manage unbalanced linguistic information and we use the linguistic 2‐tuple model as the representation base of the unbalanced linguistic information. Additionally, the linguistic 2‐tuple model allows us to increase the number of relevance classes in the output and also to improve the performance of the information retrieval system.

An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field

This paper presents an approach to analyze the thematic evolution of a given research field. This approach combines performance analysis and science mapping for detecting and visualizing conceptual subdomains (particular themes or general thematic areas). It allows us to quantify and visualize the thematic evolution of a given research field. To do this, co-word analysis is used in a longitudinal framework in order to detect the different themes treated by the research field across the given time period. The performance analysis uses different bibliometric measures, including the h-index, with the purpose of measuring the impact of both the detected themes and thematic areas. The presented approach includes a visualization method for showing the thematic evolution of the studied field.

A recommender system for research resources based on fuzzy linguistic modeling

Nowadays, the increasing popularity of Internet has led to an abundant amount of information created and delivered over electronic media. It causes the information access by the users is a complex activity and they need tools to assist them to obtain the required information. Recommender systems are tools whose objective is to evaluate and filter the great amount of information available in a specific scope to assist the users in their information access processes. Another obstacle is the great variety of representations of information, specially when the users take part in the process, so we need more flexibility in the information processing. The fuzzy linguistic modeling allows to represent and handle flexible information. Similar problems are appearing in other frameworks, such as digital academic libraries, research offices, business contacts, etc. We focus on information access processes in technology transfer offices. The aim of this paper is to develop a recommender system for research resources based on fuzzy linguistic modeling. The system helps researchers and environment companies allowing them to obtain automatically information about research resources (calls or projects) in their interest areas. It is designed using some filtering tools and a particular fuzzy linguistic modeling, called multi-granular fuzzy linguistic modeling, which is useful when we have to assess different qualitative concepts. The system is working in the University of Granada and experimental results show that it is feasible and effective.

SciMAT : A new science mapping analysis software tool

This article presents a new open-source software tool, SciMAT, which performs science mapping analysis within a longitudinal framework. It provides different modules that help the analyst to carry out all the steps of the science mapping workflow. In addition, SciMAT presents three key features that are remarkable in respect to other science mapping software tools: (a) a powerful preprocessing module to clean the raw bibliographical data, (b) the use of bibliometric measures to study the impact of each studied element, and (c) a wizard to configure the analysis.

A FUZZY LINGUISTIC IRS MODEL BASED ON A 2-TUPLE FUZZY LINGUISTIC APPROACH

Information Retrieval Systems (IRSs) based on an ordinal fuzzy linguistic approach present some problems of loss of information and lack of precision when working with discrete linguistic expression domains or when applying approximation operations in the symbolic aggregation methods. In this paper, we present a new IRS model based on the 2-tuple fuzzy linguistic approach, which allows us to overcome the problems of ordinal fuzzy linguistic IRSs and improve their performance.

Sketching the first 45 years of the journal Psychophysiology (1964-2008): A co-word-based analysis

This article presents a keyword-based bibliometric study of the thematic evolution of the journal Psychophysiology since its first publication in 1964 until 2008. Bibliometric maps showing the most relevant associations among the main topics treated by the journal are provided separately for the periods 1964-1978, 1979-1988, 1989-1998, and 1999-2008. These maps offer insight into the conceptual structure of psychophysiology as a research discipline and help to visualize the division of the field into several interconnected subfields. Bibliometric maps created by co-word analysis can be used by both experts and novices to understand the current state of the art of a scientific field and to predict where future research could lead.

A Note on the ITS Topic Evolution in the Period 2000–2009 at T-ITS

In this paper, we extend the study of the intelligent transportation system (ITS) topic evolution presented by Li et al. To do so, we apply an approach that combines both H-index-based performance analysis and science mapping to detect, visualize, and evaluate conceptual ITS themes and ITS thematic areas published by the journal IEEE Transactions on Intelligent Transport Systems during the decade (2000-2009). The primary consequence of this is the detection of three important thematic areas: COMPUTER-VISION and TRAFFIC-FLOW, which are related to research in ITS applied to vehicles, and AIRCRAFT-TRAFFIC, which is related to research in ITS applied to aircraft/airport.

A bibliometric study about the research based on hybridating the fuzzy logic field and the other computational intelligent techniques: A visual approach

In this paper, a bibliometric study on the interconnections between the fuzzy logic theory field and the other soft-computing techniques is presented. Bibliometric maps showing the associations between the main concepts between these research fields are provided for the periods 1990-1999, 2000-2003, and 2004-2007. The maps provide insight into the structure of these fields. They visualize the division of the field into several subfields and they indicate the relations between these subfields. The maps are created by co-word analysis. Experts can use these maps to forecast emerging trends for hybrid intelligent systems.

Applying multi-objective evolutionary algorithms to the automatic learning of extended Boolean queries in fuzzy ordinal linguistic information retrieval systems

The performance of information retrieval systems (IRSs) is usually measured using two different criteria, precision and recall. Precision is the ratio of the relevant documents retrieved by the IRS in response to a users query to the total number of documents retrieved, whilst recall is the ratio of the number of relevant documents retrieved to the total number of relevant documents for the users query that exist in the documentary database. In fuzzy ordinal linguistic IRSs (FOLIRSs), where extended Boolean queries are used, defining the users queries in a manual way is usually a complex task. In this contribution, our interest is focused on the automatic learning of extended Boolean queries in FOLIRSs by means of multi-objective evolutionary algorithms considering both mentioned performance criteria. We present an analysis of two well-known general-purpose multi-objective evolutionary algorithms to learn extended Boolean queries in FOLIRSs. These evolutionary algorithms are the non-dominated sorting genetic algorithm (NSGA-II) and the strength Pareto evolutionary algorithm (SPEA2).