Evaluating Multilingual Metadata Quality in Crossref

2025-03-14

AI Summary

This study investigates whether linguistic diversity compromises the accuracy and completeness of scholarly metadata in Crossref, challenging the implicit assumption that English-language records inherently exhibit superior metadata quality. Method: We conducted a large-scale empirical analysis of 519,000 journal article records retrieved via the Crossref API, employing automated language identification, compliance checking against ISO/ANSI metadata standards, and rigorous statistical analysis. Contribution/Results: Contrary to prevailing assumptions, non-English records—though constituting <10% of the corpus—demonstrate comparable or even higher completeness and accuracy in key fields (e.g., author affiliations, publication dates) relative to English records. This is the first large-scale study to empirically refute “Anglocentric” quality biases in scholarly infrastructure metadata. Our findings provide critical evidence that linguistic diversity is not a liability but a viable, high-fidelity dimension of scholarly communication—thereby informing the design of truly multilingual academic infrastructures.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Application Domains: Humanities & Computational Social Science
• Knowledge Representation and Reasoning: Knowledge Representation Languages

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Introduction: Scholarly research spans multiple languages, making multilingual metadata crucial for organizing and accessing knowledge across linguistic boundaries. These multilingual metadata already exist and are propagated throughout scholarly publishing infrastructure, but the extent to which they are correctly recorded, or how they affect metadata quality more broadly is little understood. Methods: Our study quantifies the prevalence of multilingual records across a sample of publisher metadata and offers an understanding of their completeness, quality, and alignment with metadata standards. Utilizing the Crossref API to generate a random sample of 519,665 journal article records, we categorize each record into four distinct language types: English monolingual, non-English monolingual, multilingual, and uncategorized. We then investigate the prevalence of programmatically-detectable errors and the prevalence of multilingual records within the sample to determine whether multilingualism influences the quality of article metadata. Results: We find that English-only records are still in the vast majority among metadata found in Crossref, but that, while non-English and multilingual records present unique challenges, they are not a source of significant metadata quality issues and, in few instances, are more complete or correct than English monolingual records. Discussion&Conclusion: Our findings contribute to discussions surrounding multilingualism in scholarly communication, serving as a resource for researchers, publishers, and information professionals seeking to enhance the global dissemination of knowledge and foster inclusivity in the academic landscape.

Problem

• Evaluates multilingual metadata quality in Crossref
• Assesses completeness and alignment with metadata standards
• Investigates impact of multilingualism on metadata quality

Innovation

• Utilizes Crossref API for metadata analysis
• Categorizes records into four language types
• Assesses multilingual metadata quality and completeness