Measuring publication relatedness using controlled vocabularies
<|reference_start|>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.<|reference_end|>
@article{alnor2024measuring,
    title={Measuring publication relatedness using controlled vocabularies},
    author={Emil Dolmer Alnor},
    journal={arXiv preprint arXiv:2408.15004},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15004},
    primaryClass={cs.IR cs.IT cs.SI math.IT}
}