Preserving contextual information in cultural heritage metadata through multidimensional knowledge graphs

2026-09-07

AI Summary

为解决文化遗产元数据中上下文信息丢失的问题，本文提出多维知识图谱方法来同时建模和查询跨多个社会、文化和政治维度的数据。

Abstract

Using Knowledge Graphs (KGs) to describe Cultural Heritage Objects (CHOs) supports semantic richness and interoperability. However, standard KGs fail to capture the context-dependent validity of statements. This limitation is critical for cultural heritage metadata, which must often accommodate evolving or conflicting viewpoints, such as colonial versus post-colonial perspectives or shifting scientific consensus. While current knowledge representation methods address basic contextualization via provenance, qualifiers or reification, they lack a unified framework to simultaneously model and query data across multiple social, cultural, and political dimensions. To bridge this gap, we introduce the conceptual foundations of Multi-dimensional Knowledge Graphs (MKGs) and discuss how they preserve complex, multi-layered contexts in CHO metadata.

Problem

• Knowledge Graphs
• Cultural Heritage Objects
• context-dependent validity
• conflicting viewpoints
• multidimensional knowledge graphs

Innovation

• Multi-dimensional Knowledge Graphs
• contextual information
• cultural heritage metadata