A new technique for building maps of large scientific domains based on the cocitation of classes and categories, Scientometrics, 61: 129-145

Scientometrics (Impact Factor: 2.18). 09/2004; 61(1). DOI: 10.1023/B:SCIE.0000037368.31217.34
Source: OAI


Our objective is the generation of schematic visualizations as interfaces for scientific domain analysis. We propose a new technique that uses thematic classification (classes and categories) as entities of co-citation and units of measure, and demonstrate the viability of this methodology through the representation and analysis of a domain of great dimensions. The main features of the maps obtained are discussed, and proposals are made for future improvements and applications.

Download full-text


Available from: Zaida Chinchilla-Rodríguez
  • Source
    • "However, these maps were local journal maps of limited sets, that is, of the order of between ten and one hundred journals. Beyond local maps, the advent of visualization techniques in Windows during the 1990s (e.g., the graphical user interface) and computers with more memory has made it feasible since the early 2000s to develop global maps—that is, including all the available data about journal-journal citations in a single map (e.g., Boyack, Klavans & Börner, 2005; de Moya-Anegón et al., 2004; Rosvall & Bergstrom, 2008). On the basis of a number of studies Klavans & Boyack (2009) concluded that in the meantime a consensus about the structure of the journal literature had formed from the various mapping efforts: the maps indicate a torus-like shape in which the disciplines are grouped in a circle with the computer sciences relatively more toward the center (ibid., p. 471). "
    [Show abstract] [Hide abstract]
    ABSTRACT: We compare the network of aggregated journal-journal citation relations provided by the Journal Citation Reports (JCR) 2012 of the Science and Social Science Citation Indexes (SCI and SSCI) with similar data based on Scopus 2012. First, global maps were developed for the two sets separately; sets of documents can then be compared using overlays to both maps. Using fuzzy-string matching and ISSN numbers, we were able to match 10,524 journal names between the two sets; that is, 96.3% of the 10,930 journals contained in JCR or 51.2% of the 20,553 journals covered by Scopus. Network analysis was then pursued on the set of journals shared between the two databases and the two sets of unique journals. Citations among the shared journals are more comprehensively covered in JCR than Scopus, so the network in JCR is denser and more connected than in Scopus. The ranking of shared journals in terms of indegree (that is, numbers of citing journals) or total citations is similar in both databases overall (Spearman's rho > 0.97), but some individual journals rank very differently. Journals that are unique to Scopus seem to be less important--they are citing shared journals rather than being cited by them--but the humanities are covered better in Scopus than in JCR.
    Full-text · Article · Apr 2014 · Journal of the Association for Information Science and Technology
  • Source
    • "Does this structure change over time? Co-citation network is considered a valid source for obtaining such relational information on content similarity (Leydesdorff and Vaughan 2006; Moya-Anegon et al. 2004) and mapping and clustering in network analysis are suitable techniques for addressing this sort of research questions (Waltman et al. 2010). "
    [Show abstract] [Hide abstract]
    ABSTRACT: This study aims to map the content and structure of the knowledge base of research on intercultural relations as revealed in co-citation networks of 30 years of scholarly publications. Source records for extracting co-citation information are retrieved from Web of Science (1980–2010) through comprehensive keyword search and filtered by manual semantic coding. Exploratory network and content analysis is conducted (1) to discover the development of major research themes and the relations between them over time; (2) to locate representative core publications (the stars) that are highly co-cited with others and those (the bridges) connecting more between rather than within subfields or disciplines. Structural analysis of the co-citation networks identifies a core cluster that contains foundational knowledge of this domain. It is well connected to almost all the other clusters and covers a wide range of subject categories. The evolutionary path of research themes shows trends moving towards (e.g. psychology and business and economics) and away from (e.g. language education and communication) the core cluster over time. Based on the results, a structural framework of the knowledge domain of intercultural relations research is proposed to represent thematic relatedness between topical groups and their relations.
    Full-text · Article · Jul 2013 · Scientometrics
  • Source
    • "The term fingram stands for fuzzy inference-gram. It was coined in [4] by inspiration on the term scientogram firstly introduced by Vargas-Quesada and Moya-Anegón [29] in the search for a new tool aimed at visualizing the structure of science [35]. We have recently proposed a methodology for visual representation and exploratory analysis of the fuzzy inference process in FRBSs [30]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Understand the behavior of Fuzzy Rule-based Systems (FRBSs) at inference level is a complex task that allows the designer to produce simpler and powerful systems. The fuzzy inference-grams –known as fingrams– establish a novel and mighty tool for understanding the structure and behavior of fuzzy systems. Fingrams represent FRBSs as social networks made of nodes representing fuzzy rules and edges representing the degree of interaction between pairs of rules at inference level (no edge means no significant interaction). We can analyze fingrams obtaining helpful information such as detecting potential conflicts between rules, unused rules and redundant ones. This paper introduces a newmodule for fingram generation and analysis included in the free software tool GUAJE. This tool aims to design, analyze and evaluate fuzzy systems with good interpretability-accuracy trade-off. In addition, GUAJE includes several intuitive and interactive tutorials to uncover the possibilities it offers. One of them generates and enhances a fuzzy system, analyzing each improvement through the use of fingrams, and lets the user reproduce the illustrative case study described in this paper.
    Full-text · Article · Jun 2013 · International Journal of Computational Intelligence Systems
Show more