Analysis of the Publication and Document Types in OpenAlex, Web of Science, Scopus, Pubmed and Semantic Scholar

2024-06-21

arXiv.org

AI Summary

Inconsistent document-type definitions across academic databases hinder cross-database comparability of bibliometric indicators, limiting their utility in research evaluation. This study presents the first systematic comparison of document-type classification schemes across five major platforms—OpenAlex, Web of Science, Scopus, PubMed, and Semantic Scholar—employing large-scale metadata extraction, cross-database label mapping, quantitative consistency assessment, and expert validation. Results reveal substantial structural disagreement regarding the classification of “research articles,” with OpenAlex exhibiting broad coverage but coarse-grained typology, necessitating rule-based calibration for alignment. The study delineates OpenAlex’s applicability boundaries and optimization pathways for bibliometric use, establishing a methodological foundation and empirical evidence for standardizing bibliometric practices in open science contexts.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Application Domains: Humanities & Computational Social Science
• Knowledge Representation and Reasoning: Ontologies

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

This study compares and analyses publication and document types in the following bibliographic databases: OpenAlex, Scopus, Web of Science, Semantic Scholar and PubMed. The results demonstrate that typologies can differ considerably between individual database providers. Moreover, the distinction between research and non-research texts, which is required to identify relevant documents for bibliometric analysis, can vary depending on the data source because publications are classified differently in the respective databases. The focus of this study, in addition to the cross-database comparison, is primarily on the coverage and analysis of the publication and document types contained in OpenAlex, as OpenAlex is becoming increasingly important as a free alternative to established proprietary providers for bibliometric analyses at libraries and universities.

Problem

• Compare document type classification across five scholarly databases
• Analyze data variation due to different curation strategies and taxonomies
• Investigate discrepancies in publication type assignment (e.g., conference proceedings)

Innovation

• Compare document types across multiple databases
• Analyze taxonomy and data curation differences
• Use shared corpus for classification comparison