Iconography and iconology are fundamental domains when it comes to
understanding artifacts of cultural heritage. Iconography deals with the study
and interpretation of visual elements depicted in artifacts and their
symbolism, while iconology delves deeper, exploring the underlying cultural and
historical meanings. Despite the advances in representing cultural heritage
with Linked Open Data (LOD), recent studies show persistent gaps in the
representation of iconographic and iconological statements in current knowledge
graphs (KGs). To address them, this paper presents IICONGRAPH, a KG that was
created by refining and extending the iconographic and iconological statements
of ArCo (the Italian KG of cultural heritage) and Wikidata. The development of
IICONGRAPH was also driven by a series of requirements emerging from research
case studies that were unattainable in the non-reengineered versions of the
KGs. The evaluation results demonstrate that IICONGRAPH not only outperforms
ArCo and Wikidata through domain-specific assessments from the literature but
also serves as a robust platform for addressing the formulated research
questions. IICONGRAPH is released and documented in accordance with the FAIR
principles to guarantee the resource's reusability. The algorithms used to
create it and assess the research questions have also been made available to
ensure transparency and reproducibility. While future work focuses on ingesting
more data into the KG, and on implementing it as a backbone of LLM-based
question answering systems, the current version of IICONGRAPH still emerges as
a valuable asset, contributing to the evolving landscape of cultural heritage
representation within Knowledge Graphs, the Semantic Web, and beyond.

该论文介绍了 IICONGRAPH，它是一个由 ArCo 和 Wikidata 的图标和图标叙述进行扩展和改进而创建的知识图谱，以填补当前知识图谱中图标和图标叙述的表达方面存在的差距。该研究表明，IICONGRAPH 不仅在领域特定评估方面优于 ArCo 和 Wikidata，还作为一个强大的平台来解决提出的研究问题。

IICONGRAPH: 知识图谱中的改进图标学和图标学陈述

IICONGRAPH: improved Iconographic and Iconological Statements in  Knowledge Graphs

Iconography in art is the discipline that studies the visual content of
artworks to determine their motifs and themes andto characterize the way these
are represented. It is a subject of active research for a variety of purposes,
including the interpretation of meaning, the investigation of the origin and
diffusion in time and space of representations, and the study of influences
across artists and art works. With the proliferation of digital archives of art
images, the possibility arises of applying Computer Vision techniques to the
analysis of art images at an unprecedented scale, which may support iconography
research and education. In this paper we introduce a novel paintings data set
for iconography classification and present the quantitativeand qualitative
results of applying a Convolutional Neural Network (CNN) classifier to the
recognition of the iconography of artworks. The proposed classifier achieves
good performances (71.17% Precision, 70.89% Recall, 70.25% F1-Score and 72.73%
Average Precision) in the task of identifying saints in Christian religious
paintings, a task made difficult by the presence of classes with very similar
visual features. Qualitative analysis of the results shows that the CNN focuses
on the traditional iconic motifs that characterize the representation of each
saint and exploits such hints to attain correct identification. The ultimate
goal of our work is to enable the automatic extraction, decomposition, and
comparison of iconography elements to support iconographic studies and
automatic art work annotation.

本文介绍了一种新的绘画数据集，使用卷积神经网络对基督教宗教绘画中的圣徒图像进行分类，并得到了良好的性能表现。研究结果表明，这种分类器可以有效地识别每个圣徒的独特视觉特征，以进行图像自动解析和比较，以支持图标学的研究和自动艺术作品注释。