Frizo A. L. Janssens

Combining full text and bibliometric information in mapping scientific disciplines

In the present study results of an earlier pilot study by Glenisson, Glanzel and Persson are extended on the basis of larger sets of papers. Full text analysis and traditional bibliometric methods are serially combined to improve the efficiency of the two individual methods. The text mining methodology already introduced in the pilot study is applied to the complete publication year 2003 of the journal Scientometrics. Altogether 85 documents that can be considered research articles or notes have been selected for this exercise. The outcomes confirm the main results of the pilot study, namely, that such hybrid methodology can be applied to both research evaluation and information retrieval. Nevertheless, Scientometrics documents published in 2003 cover a much broader and more heterogeneous spectrum of bibliometrics and related research than those analysed in the pilot study. A modified subject classification based on the scheme used in an earlier study by Schoepflin and Glanzel has been applied for validation purposes.

Towards mapping library and information science

In an earlier study by the authors, full-text analysis and traditional bibliometric methods were combined to map research papers published in the journal Scientometrics. The main objective was to develop appropriate techniques of full-text analysis and to improve the efficiency of the individual methods in the mapping of science. The number of papers was, however, rather limited. In the present study, we extend the quantitative linguistic part of the previous studies to a set of five journals representing the field of Library and Information Science (LIS). Almost 1000 articles and notes published in the period 2002-2004 have been selected for this exercise. The optimum solution for clustering LIS is found for six clusters. The combination of different mapping techniques, applied to the full text of scientific publications, results in a characteristic tripod pattern. Besides two clusters in bibliometrics, one cluster in information retrieval and one containing general issues, webometrics and patent studies are identified as small but emerging clusters within LIS. The study is concluded with the analysis of cluster representations by the selected journals.

A hybrid mapping of information science

Previous studies have shown that hybrid clustering methods that incorporate textual content and bibliometric information can outperform clustering methods that use only one of these components. In this paper we apply a hybrid clustering method based on Fisher’s inverse chisquare to integrate full-text with citations and to provide a mapping of the field of information science. We quantitatively and qualitatively asses the added value of such an integrated analysis and we investigate whether the clustering outcome is a better representation of the field by comparing with a text-only clustering and with another hybrid method based on linear combination of distance matrices. Our data set consists of almost 1000 articles and notes published in the period 2002–2004 in 5 representative journals. The optimal number of clusters for the field is 5, determined by using a combination of distance-based and stability-based methods. Term networks present the cognitive structure of the field and are complemented by the most representative publications. Three large traditional sub-disciplines, particularly, information retrieval, bibliometrics/scientometrics, and more social aspects, and two smaller clusters about patent analysis and webometrics, can be distinguished.

Subject clustering analysis based on ISI category classification

The study focuses on the analysis of the information flow among the ISI subject categories and aims at finding an appropriate field structure of the Web of Science using the subject clustering algorithm developed in previous studies. The clustering journals and ISI subject categories provide two subject classification schemes through different perspectives and levels. The two clustering results have been compared and their accordance and divergence have been analyzed. Several indicators have been used to compare the communication characteristics among different ISI subject categories. The neighbour map of each category clearly reflects the affinities between the “core” category and its satellites around.

Dynamic hybrid clustering of bioinformatics by incorporating text mining and citation analysis

To unravel the concept structure and dynamics of the bioinformatics field, we analyze a set of 7401 publications from the Web of Science and MEDLINE databases, publication years 1981-2004. For delineating this complex, interdisciplinary field, a novel bibliometric retrieval strategy is used. Given that the performance of unsupervised clustering and classification of scientific publications is significantly improved by deeply merging textual contents with the structure of the citation graph, we proceed with a hybrid clustering method based on Fishers inverse chi-square. The optimal number of clusters is determined by a compound semiautomatic strategy comprising a combination of distance-based and stability-based methods. We also investigate the relationship between number of Latent Semantic Indexing factors, number of clusters, and clustering performance. The HITS and PageRank algorithms are used to determine representative publications in each cluster. Next, we develop a methodology for dynamic hybrid clustering of evolving bibliographic data sets. The same clustering methodology is applied to consecutive periods defined by time windows on the set, and in a subsequent phase chains are formed by matching and tracking clusters through time. Term networks for the eleven resulting cluster chains present the cognitive structure of the field. Finally, we provide a view on how much attention the bioinformatics community has devoted to the different subfields through time.

A comparative analysis of publication activity and citation impact based on the core literature in bioinformatics

A novel subject-delineation strategy has been developed for the retrieval of the core literature in bioinformatics. The strategy combines textual components with bibliometric, citation-based techniques. This bibliometrics-aided search strategy is applied to the 1980–2004 annual volumes of the Web of Science. Retrieved literature has undergone a structural as well as quantitative analysis. Patterns of national publication activity, citation impact and international collaboration are analysed for the 1990s and the new millennium.

Journal cross-citation analysis for validation and improvement of journal-based subject classification in bibliometric research

The objective of this study is to use a clustering algorithm based on journal cross-citation to validate and to improve the journal-based subject classification schemes. The cognitive structure based on the clustering is visualized by the journal cross-citation network and three kinds of representative journals in each cluster among the communication network have been detected and analyzed. As an existing reference system the 15-field subject classification by Glänzel and Schubert (Scientometrics 56:55–73, 2003) has been compared with the clustering structure.

Multiple-vector user profiles in support of knowledge sharing

This paper describes an algorithm to automatically construct expertise profiles for company employees, based on documents authored and read by them. A profile consists of a series of high dimensional vectors, each describing an expertise domain, and provides a hierarchy between these vectors, enabling a structured view on an employees expertise. The algorithm is novel in providing this layered view, as well as in its high degree of automation and its generic approach ensuring applicability in an industrial setting. The profiles provide support for several knowledge management functionalities that are difficult or impossible to achieve using existing methods. This paper in particular presents the initialization of communities of practice, bringing together both experts and novices on a specific topic. An algorithm to automatically discover relationships between employees based on their profiles is described. These relationships can be used to initiate communities of practice. The algorithms are validated by means of a realistic dataset.

M@CBETH: a microarray classification benchmarking tool

Microarray classification can be useful to support clinical management decisions for individual patients in, for example, oncology. However, comparing classifiers and selecting the best for each microarray dataset can be a tedious and non-straightforward task. The M@CBETH (a MicroArray Classification BEnchmarking Tool on a Host server) web service offers the microarray community a simple tool for making optimal two-class predictions. M@CBETH aims at finding the best prediction among different classification methods by using randomizations of the benchmarking dataset. The M@CBETH web service intends to introduce an optimal use of clinical microarray data classification.

Do material transfer agreements affect the choice of research agendas? The case of biotechnology in Belgium

In this paper we examine whether and to what extent material transfer agreements influence research agenda setting in biotechnology. Research agendas are mapped through patents, articles, letters, reviews, and notes. Three groups are sampled: (1) documents published by government and industry which used research materials received through those agreements, (2) documents published by government and industry which used in-house materials, (3) documents published by academia. Methodologically, a co-word analysis is performed to detect if there is a difference in underlying scientific structure between the first two groups of documents. Secondly, interviews with practitioners of industry and government are intended to capture their opinion regarding the impact of the signed agreements on their own research agenda choices. The existence of synchronic and diachronic common terms between co-word clusters, stemming from the first two groups of publications, suggests cognitive linkage. Moreover, interviewees generally do not consider themselves constrained in research agenda setting when signing agreements for receiving research materials. Finally, after applying a co-word analysis to detect if the first group of documents overlaps with the third group we cannot conclude that agreements signed by industry and government affect research agenda setting in academia.