Quality of Descriptive Information on Cultural Heritage Objects: Definition and Empirical Evaluation

2026-02-19

AI Summary

This study addresses the absence of a universally accepted and empirically validated definition of descriptive data quality in the cultural heritage domain. It proposes, for the first time, a domain-specific data quality dimension framework developed through a systematic empirical evaluation that integrates literature review, dimensional analysis, and problem annotation on real-world datasets. By bridging the gap between data quality theory and cultural heritage practice, this work delivers an actionable, contextually tailored, and empirically grounded definition of data quality. The resulting framework provides clear guidance for digital initiatives in the field, supporting more effective data curation, interoperability, and long-term preservation efforts within cultural heritage institutions.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Effective data processing depends on the quality of the underlying data. However, quality issues such as inconsistencies and uncertainties, can significantly impede the processing and subsequent use of data. Despite the centrality of data quality to a wide range of computational tasks, there is currently no broadly accepted, domain-independent consensus on the definition of data quality. Existing frameworks primarily define data quality in ways that are tailored to specific domains, data types, or contexts of use. Although quality assessment frameworks exist for specific domains, such as electronic health record data and linked data, corresponding approaches for descriptive information about cultural heritage objects remain underdeveloped. Moreover, existing quality definitions are often theoretical in nature and lack empirical validation based on real-world data problems. In this paper, we address these limitations by first defining a set of quality dimensions specifically designed to capture the characteristics of descriptive information about cultural heritage objects. Our definition is based on an in-depth analysis of existing dimensions and is illustrated through domain-specific examples. We then evaluate the practical applicability of our proposed quality definition using a curated set of real-world data quality problems from the cultural heritage domain. This empirical evaluation substantiates our definition of data quality, resulting in a comprehensive definition of data quality in this domain.

Problem

• data quality
• cultural heritage
• descriptive information
• quality dimensions
• empirical evaluation

Innovation

• data quality
• cultural heritage
• descriptive information
• quality dimensions
• empirical evaluation