Measuring publication relatedness using controlled vocabularies
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929475920330752 |
|---|---|
| author | Alnor, Emil Dolmer |
| author_facet | Alnor, Emil Dolmer |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15004 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Measuring publication relatedness using controlled vocabularies Alnor, Emil Dolmer Information Retrieval Information Theory Social and Information Networks 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. |
| title | Measuring publication relatedness using controlled vocabularies |
| topic | Information Retrieval Information Theory Social and Information Networks |
| url | https://arxiv.org/abs/2408.15004 |