Measuring the relatedness between scientific publications using controlled vocabularies

Measuring the relatedness between scientific publications is essential in many areas of bibliometrics and science policy. Controlled vocabularies provide a promising basis for measuring relatedness and are widely used in combination with Salton's cosine similarity. The latter is problematic because it only considers exact matches between terms. This article introduces two alternative methods - soft cosine and maximum term similarities - that account for the semantic similarity between non-matching terms. The article compares the accuracy of all three methods using the assignment of publications to topics in the TREC 2006 Genomics Track and the assumption that accurate relatedness measures should assign high relatedness scores to publication pairs within the same topic and low scores to pairs from separate topics. Results show that soft cosine is the most accurate method, while the most widely used version of Salton's cosine is markedly less accurate than the other methods tested. These findings have implications for how controlled vocabularies should be used to measure relatedness.

Measuring publication relatedness using controlled vocabularies

Measuring the relatedness between scientific publications has important applications in many areas of bibliometrics and science policy. Controlled vocabularies provide a promising basis for measuring relatedness because they address issues that arise when using citation or textual similarity to measure relatedness. While several controlled-vocabulary-based relatedness measures have been developed, there exists no comprehensive and direct test of their accuracy and suitability for different types of research questions. This paper reviews existing measures, develops a new measure, and benchmarks the measures using TREC Genomics data as a ground truth of topics. The benchmark test show that the new measure and the measure proposed by Ahlgren et al. (2020) have differing strengths and weaknesses. These results inform a discussion of which method to choose when studying interdisciplinarity, information retrieval, clustering of science, and researcher topic switching.