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Abstract

In the past several years studies have started to appear comparing the accuracies of various science mapping approaches. These studies primarily compare the cluster solutions resulting from different similarity approaches, and give varying results. In this study we compare the accuracies of cluster solutions of a large corpus of 2,153,769 recent articles from the biomedical literature (2004-2008) using four similarity approaches: co-citation analysis, bibliographic coupling, direct citation, and a bibliographic coupling-based citation-text hybrid approach. Each of the four approaches can be considered a way to represent the research front in biomedicine, and each is able to successfully cluster over 92% of the corpus. Accuracies are compared using two metrics-within-cluster textual coherence as defined by the Jensen-Shannon divergence, and a concentration measure based on the grant-to-article linkages indexed in MEDLINE. Of the three pure citation-based approaches, bibliographic coupling slightly outperforms co-citation analysis using both accuracy measures; direct citation is the least accurate mapping approach by far. The hybrid approach improves upon the bibliographic coupling results in all respects. We consider the results of this study to be robust given the very large size of the corpus, and the specificity of the accuracy measures used.

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Abstract

We describe two general approaches to creating document-level maps of science. To create a local map, one defines and directly maps a sample of data, such as all literature published in a set of information science journals. To create a global map of a research field, one maps "all of science" and then locates a literature sample within that full context. We provide a deductive argument that global mapping should create more accurate partitions of a research field than does local mapping, followed by practical reasons why this may not be so. The field of information science is then mapped at the document level using both local and global methods to provide a case illustration of the differences between the methods. Textual coherence is used to assess the accuracies of both maps. We find that document clusters in the global map have significantly higher coherence than do those in the local map, and that the global map provides unique insights into the field of information science that cannot be discerned from the local map. Specifically, we show that information science and computer science have a large interface and that computer science is the more progressive discipline at that interface. We also show that research communities in temporally linked threads have a much higher coherence than do isolated communities, and that this feature can be used to predict which threads will persist into a subsequent year. Methods that could increase the accuracy of both local and global maps in the future also are discussed.

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Abstract

In 1965, Price foresaw the day when a citation-based taxonomy of science and technology would be delineated and correspondingly used for science policy. A taxonomy needs to be comprehensive and accurate if it is to be useful for policy making, especially now that policy makers are utilizing citation-based indicators to evaluate people, institutions and laboratories. Determining the accuracy of a taxonomy, however, remains a challenge. Previous work on the accuracy of partition solutions is sparse, and the results of those studies, although useful, have not been definitive. In this study we compare the accuracies of topic-level taxonomies based on the clustering of documents using direct citation, bibliographic coupling, and co-citation. Using a set of new gold standards-articles with at least 100 references-we find that direct citation is better at concentrating references than either bibliographic coupling or co-citation. Using the assumption that higher concentrations of references denote more accurate clusters, direct citation thus provides a more accurate representation of the taxonomy of scientific and technical knowledge than either bibliographic coupling or co-citation. We also find that discipline-level taxonomies based on journal schema are highly inaccurate compared to topic-level taxonomies, and recommend against their use.