Bibliometric cartography of information retrieval research by using co-word analysis

The aim of this study is to map the intellectual structure of the field of Information Retrieval (IR) during the period of 1987-1997. Co-word analysis was employed to reveal patterns and trends in the IR field by measuring the association strengths of terms representative of relevant publications or other texts produced in IR field. Data were collected from Science Citation Index (SCI) and Social Science Citation Index (SSCI) for the period of 1987-1997. In addition to the keywords added by the SCI and SSCI databases, other important keywords were extracted from titles and abstracts manually. These keywords were further standardized using vocabulary control tools. In order to trace the dynamic changes of the IR field, the whole 11-year period was further separated into two consecutive periods: 1987-1991 and 1992-1997. The results show that the IR field has some established research themes and it also changes rapidly to embrace new themes

The Trend and Intellectual Structure of Digital Archives Research

Archives are an extremely valuable part of cultural heritage since they represent the trace of the activities of a juridical person or organization in the course of their business. Through various information technology (IT), tremendous amount of digital archives (DA) are created. These archives are the basis for providing evidence and knowledge in everlasting memory of human society. The management of digital archives becomes a fast growing field throughout last decade and introduces abundant articles in academia. However, their trend and intellectual structure have remained obscure in the research community. To map the trend and intellectual structure of DA research, this study identifies the high-impact articles as well as the correlations among these scholar publications. In this study, text mining techniques, such as co-word and cluster analysis, have been deployed to investigate the intellectual pillars of the DA literature. This study exposes researchers to a new way of profiling knowledge networks and their relationships in the research area of DA, thereby helping academia and practitioners better understand up-to-date studies. The results of the mapping can help identify the research direction of DA research, provide a valuable tool for researchers to access DA literature, and act as an exemplary model for future research

Estudio comparativo sobre la visualización de redes de co-words a través de los descriptores del Science Citation Index y de Medline

Cantos Mateos, Gisela; Zulueta, María Ángeles; Vargas-Quesada, Benjamín; Chinchilla-Rodríguez, Zaida. Estudio comparativo sobre la visualización de redes de co-words a través de los descriptores del Science Citation Index y de Medline. Atas de I Congresso ISKO Espanha e Portugal / XI Congresso ISKO Espanha, Oporto (Portugal), 7 a 9 de novembro 201, p. 173-189Objetivos: El presente estudio se centra en la investigación desarrollada en España sobre células madre comprendida entre los años 1997 y 2010. El objetivo fundamental consiste en la comparación de las líneas de investigación que ofrece el análisis de distintos tipos de descriptores, según su naturaleza documental, a partir de su aparición conjunta en los documentos. Material y Métodos: Las fuentes utilizadas han sido las bases de datos del Science Citation Index Expanded (SCI-E) y Medline, empleando para el estudio el mismo conjunto documental. El análisis aplicado ha consistido en la representación y visualización de las relaciones que se establecen entre los términos de indización. De un lado, se han empleado los descriptores utilizados por el SCI para indizar sus documentos: KeywordsPlus (KW+) y Keywords Author (KWA) y de otro, los descriptores MeSH utilizados por Medline. Las herramientas utilizadas para la visualización han sido el software Pajek, en combinación con el algoritmo PathfinderNetwork (PfNET) para la simplificación de las relaciones y el software VOSviewer.Resultados y Discusión: se han recuperado 3.078 documentos. A partir de ellos, en función del tipo de descriptor seleccionado, se han obtenido distintas imágenes sobre la investigación española en células madre entre 1997 y 2010. La visualización más clara y completa es la que ofrecen los descriptores KW+, permitiendo detectar hasta un total de seis líneas de investigación. La visualización de los KWA, por su parte, ofrece una imagen más diluida de las líneas de investigación, reflejando, sobre todo, la investigación de carácter más básico. Finalmente, la representación de las relaciones de los descriptores MeSH también se aproxima, sobre todo, a los estudios de carácter más básico. Conclusión: la comparación de las visualizaciones ha permitido determinar que los descriptores KW+ son la unidad de análisis más adecuada a la hora de realizar un análisis temático sobre un domino científico.Peer reviewe

Measuring Author Research Relatedness: A Comparison of Word-based,Topic-based and Author Cocitation Approaches

Relationships between authors based on characteristics of published literature have been studied for decades. Author cocitation analysis using mapping techniques has been most frequently used to study how closely two authors are thought to be in intellectual space based on how members of the research community co-cite their works. Other approaches exist to study author relatedness based more directly on the text of their published works. In this study we present static and dynamic word-based approaches using vector space modeling, as well as a topic-based approach based on Latent Dirichlet Allocation for mapping author research relatedness. Vector space modeling is used to define an author space consisting of works by a given author. Outcomes for the two word-based approaches and a topic-based approach for 50 prolific authors in library and information science are compared with more traditional author cocitation analysis using multidimensional scaling and hierarchical cluster analysis. The two word-based approaches produced similar outcomes except where two authors were frequent co-authors for the majority of their articles. The topic-based approach produced the most distinctive map