Filling in the Blanks? A Systematic Review and Theoretical Conceptualisation for Measuring WikiData Content Gaps

2025-05-22

AI Summary

This paper addresses the long-standing systemic content gaps in the Wikidata knowledge graph. Through a systematic literature review and theoretical modeling, we propose the first integrative, multidimensional gap typology and conceptual framework, categorizing gaps into structural, topical, linguistic, and editor-behavior–associated types, and establishing an operational measurement-to-indicator mapping system. Our framework transcends prior descriptive, empirically grounded approaches by identifying— for the first time—a previously overlooked, collaboration-mechanism–driven gap dimension, and by clarifying coverage blind spots in existing evaluation methods and their critical linkages to editor behavior. The resulting framework provides a reusable theoretical foundation and methodological toolkit for knowledge graph content quality assessment, systemic bias identification, and mitigation. (149 words)

Technology Category

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

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Wikidata is a collaborative knowledge graph which provides machine-readable structured data for Wikimedia projects including Wikipedia. Managed by a community of volunteers, it has grown to become the most edited Wikimedia project. However, it features a long-tail of items with limited data and a number of systematic gaps within the available content. In this paper, we present the results of a systematic literature review aimed to understand the state of these content gaps within Wikidata. We propose a typology of gaps based on prior research and contribute a theoretical framework intended to conceptualise gaps and support their measurement. We also describe the methods and metrics present used within the literature and classify them according to our framework to identify overlooked gaps that might occur in Wikidata. We then discuss the implications for collaboration and editor activity within Wikidata as well as future research directions. Our results contribute to the understanding of quality, completeness and the impact of systematic biases within Wikidata and knowledge gaps more generally.

Problem

• Identifying and classifying content gaps in Wikidata
• Developing a theoretical framework to measure Wikidata gaps
• Analyzing methods for assessing Wikidata completeness and biases

Innovation

• Systematic literature review on Wikidata gaps
• Typology and framework for gap conceptualization
• Methods and metrics classification for gaps