=Paper=
{{Paper
|id=Vol-3834/paper123
|storemode=property
|title=Deciphering Still Life Artworks with Linked Open Data
|pdfUrl=https://ceur-ws.org/Vol-3834/paper123.pdf
|volume=Vol-3834
|authors=Bruno Sartini
|dblpUrl=https://dblp.org/rec/conf/chr/Sartini24
}}
==Deciphering Still Life Artworks with Linked Open Data==
https://ceur-ws.org/Vol-3834/paper123.pdf
Deciphering Still Life Artworks With Linked Open
Data
Bruno Sartini1
1
Ludwig-Maximilians-Universität München, Germany
Abstract
The still life genre is a good example of how even the simplest elements depicted in an artwork can
be carriers of deeper, symbolic meanings that influence the overall artistic interpretation of it. In this
paper, we present an ongoing study on the use of linked open data (LOD) to quantitatively analyze the
symbolic meanings of still life paintings. In particular, we propose two different experiments based on
(i) the theory of the art historian Bergström, and (ii) the impact of the Floriography movement in still
life. To do so, we extract and combine data from Wikidata, HyperReal, IICONGRAPH, and the ODOR
dataset. This work shows promising results about the use of LOD for art-historical quantitative research,
as we are able to confirm Bergström’s theory and to pinpoint outliers in the Floriography context that
can be the objects of specific, qualitative studies. We conclude the paper by reflecting on the current
limitations surrounding art-historical data.
Keywords
Linked Open Data, Floriography, Still Life, Quantitative Analysis, Symbolism, Semantic Web, Digital
Humanities
1. Introduction
Still life is an artistic genre characterized by the depiction of inanimate objects such as fruits,
vegetables, game, jewelry, and other items as the main subjects of artworks [15]. Although
examples of still life can be found already in the Greco-Roman period, it emerged as a stan-
dalone genre only in the late 16th century in the Netherlands [15]. Many art historians have
debated the content of artworks belonging to this genre, initially considered subjectless [33],
and then reinterpreted considering the depicted inanimate elements as potential vessels to
deeper symbolic meanings such as abundance, death, mortality, resurrection, life, the tran-
sience and fragility of life [33, 12, 18]. Among them, Ingvar Bergström introduced the concept
of Disguised Symbolism in still life while analyzing the relationship between this genre and
Christian symbolism [5]. According to his theory, the depiction of prominent Christian char-
acters in early Baroque and Renaissance art, such as the Virgin Mary (portraits depicting her
are also referred to as Madonna Paintings), is often accompanied by more mundane elements
like fruits and vegetables, which convey Christian symbolic meanings [5]. For example, apples
are related to the original sin, a cracked nut shell symbolizes the wood of the Cross (lignum
crucis), and the sweet kernel represents the divine nature of Christ [6]. Bergström argues that
CHR 2024: Computational Humanities Research Conference, December 4–6, 2024, Aarhus, Denmark
£ b.sartini@lmu.de (B. Sartini)
ȉ 0000-0002-9152-4402 (B. Sartini)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
548
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Proceedings
these elements, when appearing in still life paintings without the Christian characters, can still
represent Christian symbolism [5, 6].
Furthermore, during the Victorian age, Floriography emerged as a cryptic language that
used flowers and plants to communicate secret messages [20]. People used to send different
bouquets with flowers and plants to communicate to secret lovers or enemies, since flowers and
plants had both positive and negative connotations. For example, basil is a symbol of hatred
and poverty in the context of Floriography, while the honey flower was the symbol of secret
love and the ivy a symbol of fidelity [17]. This cryptic language adds yet another different lens
of interpretation to still life artworks.
To the authors’ knowledge, no quantitative analysis on the deeper meanings of still life
artworks has been performed yet. This paper leverages Semantic Web Technologies and Linked
Open Data (LOD) to conduct quantitative analyses on artworks in this genre. We perform our
analysis by reusing and mixing data taken from four different datasets: Wikidata [31], the
ODOR dataset [37], IICONGRAPH [22], and HyperReal [26]. Specifically, we aim to answer
the following research questions (RQ):
RQ1 How can LOD be used to verify Bergström’s theory about Christian symbolism in still
life? To what extent is Christian symbolism represented in still life artworks?
RQ2 To what extent did Floriography impact the still life genre? Is there a meaningful varia-
tion in symbolism within the context of Floriography before and after its spread? Which
specific symbolic meanings emerge as more popular after the spread of Floriography?
The paper is structured as follows. Section 2 provides an overview of the datasets used and
their content. Section 3 outlines the methodology. Section 4 describes and offers a discussion
of the results. Section 5 briefly reviews related work. Finally, Section 6 concludes the paper by
highlighting current limitations and suggesting directions for future research.
To ensure the reproducibility of the results, all scripts developed for the analysis of this paper
are released in the following GitHub folder: https://github.com/br0ast/still_life_analysis
2. Datasets
This section describes all the datasets that have been used to perform the quantitative analysis.
Wikidata is a collaborative knowledge base that contains information about several domains,
art included [31]. Digital humanities scholars have used it extensively for quantitative analysis
[3], entity linking, and knowledge discovery [34]. We extract information about Wikidata from
its SPARQL portal,1 filtering artworks that depict, have as main subject or as theme the concept
of still life.2
The Object Detection for Olfactory References (ODOR) dataset contains more than 4000 art-
works annotated with olfactory elements divided into 226 categories, including specific types of
flowers, fruits, and other typical elements depicted in still life. The annotation was performed
1
https://query.wikidata.org
2
Query available at https://w.wiki/AduD
549
automatically by a computer vision algorithm. We downloaded the dataset dump from Zen-
odo3 and filtered it by including only artworks that mention still life in their title, iconography,
description or keywords fields. After filtering, we reduced the dataset to 540 artworks.
IICONGRAPH is a knowledge graph that contains an enhanced version of the iconographi-
cal and iconological statements of Wikidata, following the schema of the ICON ontology [25,
24]. We use both Wikidata and IICONGRAPH because in the latter there is no contextual in-
formation about artworks, such as the date of creation and creator, which can be used in this
study to group artworks in different subsets. IICONGRAPH was downloaded from Zenodo.4
Finally, HyperReal is the dataset that allows us to study the deeper meanings of the extracted
artworks, as it contains more than 40,000 instances of symbolism,5 called simulations [26]. A
simulation is the relationship that links a symbol with its symbolic meaning and the context in
which it is symbolized. For instance, a frog, in the Egyptian context, is a symbol of longevity.
Among the cultural contexts found in HyperReal, there are both Christian and Flower Lan-
guage, which makes it an ideal candidate for a specific quantitative analysis of Floriography
and Christian symbolism. HyperReal was downloaded through a data dump.6
3. Methodology and Experiment Setup
3.1. Entity Linking
We performed entity linking between Wikidata and HyperReal, and the Odor Dataset and Hy-
perReal. For the Wikidata linking, we started from a previous approach [27] based on both
string matching between the English labels of HyperReal and the labels of the depicted ele-
ments in artworks (in Wikidata, the depicted elements would be objects of triples following
this structure: :artwork wdt:P180 depicts :depictedElement), and the linking between
Wordnet synsets of Wikidata and HyperReal (since both datasets contain links to the specific
synsets of their entites [21]). This linking was also evaluated in [23]. In Wikidata, animals,
plants, and fruits are labeled with their scientific names (e.g., wd:Q7537 is labeled as Brassica
oleracea var. botrytis). Since scientific names could potentially not match with HyperReal la-
bels, we also extracted the wdt:P1843 common taxonomy name from the depicted elements. For
instance, the common name for Brassica oleracea var. botrytis is cauliflower or broccoli, which
matches with instances of HyperReal. Figure 1 shows a visual example of the string match-
ing between Wikidata and HyperReal. We first reused the mapping from the literature based
on normal labels, and if no match was found, we tried matching with the common taxonomy
name. This additional step yielded 66 more matches which have all been manually checked.
In total, out of 1566 unique depicted elements in the extracted still life dataset from Wikidata,
558 matched with HyperReal. Additionally, 3533 paintings out of 4997 depicted at least one
element that matched with HyperReal.
For the Odor Dataset, we again used string matching between the labels of the detected
3
https://zenodo.org/record/11070878, using the “instances_all.json” file
4
https://zenodo.org/doi/10.5281/zenodo.10294588
5
We use the term symbolism to indicate the use of specific elements (symbols) to convey a meaning that is different
from a literal one. For instance, using the olive branch as a symbol of peace
6
https://w3id.org/simulation/data/
550
Figure 1: Example of string matching between Wikidata and HyperReal. hr is the prefix for the entities
in HyperReal, sim is the prefix for the properties of the Simulation Ontology, wd and wdt are used
respectively for the entities and properties of Wikidata.
categories and HyperReal. We found that 135 of the 226 categories matched, and 478 artworks
out of 540 had at least one detected category that matched HyperReal.
3.2. Preparation and analysis for RQ1
After the entity linking, we connected artworks with their potential symbolic meanings and
symbolic contexts. We are able to do this because we have the links between the artworks and
their depiction, and then the mapping between the depictions and the symbols in HyperReal.
The symbols are then connected to simulations which link them to their symbolic meanings
and the contexts in which they symbolize them. The example can be seen in figure 1 with the
broccoli-tranquility simulation. Here, an artwork depicts broccoli and it is associated to the
tranquility symbolic meaning. Because the broccoli-tranquility simulation context is general
(there are more than 300 different contexts in HyperReal), we can infer that, potentially,
this artwork, from a general point of view, symbolizes tranquility.. We highlight that these
symbolic meanings are potential as it is not possible to predict the intention of the creator,
whether or not they used the element symbolically. It is just possible to predict potential
meanings based on the depictions. This concept is also linked to the theory of Bergström,
who claims that still life artworks are permeated by Christian symbols, without questioning
whether the creators of the artworks willingly decided to paint specific subjects to convey
Christian symbolism or not. We then calculated the percentage of artworks depicting at least
one Christian symbol for both Wikidata and the ODOR dataset. We calculated the same
percentage on a random set of 3533 Wikidata artworks contained in IICONGRAPH, extracted
using a SPARQL query available in Listing 1. We then compared the percentage of artworks
depicting Christian symbols7 in the still life dataset against the random dataset to determine
if Christian symbolism was more prevalent in still life paintings, contextualizing the Christian
influence on still life art relative to general artworks.
7
Meaning that at least one of their depicted elements is linked to a symbol in HyperReal connected with a Simulation
that is supported by the Christian context
551
Figure 2: Workflow of the experiments presented in the paper. The RQ addressed in the steps are
highlighted in red (RQ1) and blue (RQ2)
Listing 1: SPARQL query launched on IICONGRAPH to extract a random set of 3533 along with
the cultural contexts of the symbolism they represent
PREFIX sim : < h t t p s : / / w3id . o r g / s i m u l a t i o n / o n t o l o g y / >
PREFIX i c o n : < h t t p s : / / w3id . o r g / i c o n / o n t o l o g y / >
s e l e c t ? a r t ( GROUP_CONCAT ( d i s t i n c t ? c t x ; SEPARATOR= ” ␣@␣ ” ) as ? c t x s )
where {
? art icon : iconographicallyDepicts ? simulation .
? s i m u l a t i o n sim : h a s C o n t e x t ? c t x .
} GROUP BY ? a r t ORDER BY RAND ( ) LIMIT 3 5 3 3
3.3. Preparation and analysis for RQ2
We divided the datasets by creation date, using the 19th century as the delimiter between
pre- and post-Floriography spread. Therefore, we created two datasets (before and after
the 19th century) each listing symbolic meanings together with the percentage of artworks
symbolizing them. We measured the Pearson correlation coefÏcient [19] between the two
datasets to obtain an overall view of the variation before and after the 19th century. We
repeated this measurement with a filtered version of the datasets, which contains only
symbolic meanings in the Flower language context. Finally, we identified symbolic meanings
in the Flower language context that gained popularity (that is, that increased the percentage of
artworks that symbolize them) after the spread of Floriography.
All experiments, including data aggregation and mapping, were carried out in a Python
environment. Figure 2 shows a graphical overview of the whole workflow explained in this
section. Table 1 contains information about the content of Wikidata and the ODOR dataset for
each filtering stage.
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Table 1
Overview of the content of Wikidata and ODOR dataset after every filtering phase.
Dataset Wikidata ODOR
# of still life artworks 4997 540
Initial Filtering Phase
# of artworks with at least 1 HyperReal match 3533 478
# of artworks with at least 1 Christian symbol 2999 446
RQ1
% of artworks with at least 1 Christian symbol 84.91 93.3
# of artworks pre-1800 2073 439
with at least 1 Flower language symbol 655 226
RQ2
# of artworks post-1800 1213 38
with at least 1 Flower language symbol 332 16
4. Results and discussion
4.1. Christian symbolism distribution in still life
Regarding RQ1, the results show that 84.9% of still life artworks in Wikidata contain at least
one Christian symbol. This percentage increases to 93.3% in the ODOR dataset. In contrast, a
random dataset from IICONGRAPH matched in size to the Wikidata dataset (3533 artworks),
shows that only 43.54% of the artworks contain at least one Christian symbol.8 These find-
ings indicate that Christian symbolism is highly prevalent in still life artworks, supporting
Bergström’s theory. Common Christian symbols in these artworks include references to the
Virgin Mary, the body of Christ, and martyrdom. We show how, by leveraging LOD, we com-
bined perspectives from different datasets: depictions in Wikidata, detections in the ODOR
dataset, and symbols in HyperReal. This integration allowed us to quantitatively measure the
prevalence of Christian symbols, revealing their significant distribution in still life paintings, as
hypothesized by the art historian. The result of this RQ also emphasizes how the capabilities of
LOD to connect different datasets and from different viewpoints (recognized depictions, sym-
bolism) can be used as a quantitative prove to complement qualitative art historical theories
such as Bergström’s.
4.2. Impact of Floriography in still life
Regarding RQ2, the correlations between the symbolic meanings pre- and post-Floriography
are quite high in both Wikidata and the ODOR dataset, being 0.84 and 0.82 respectively. The
Pearson correlation coefÏcient range from -1 to 1, both extremes represent full correlation,
while 0 represents no correlation. These high correlation values suggest that there was not a
major shift in the symbolic meanings of artworks before and after the spread of Floriography.
When filtering the datasets to include only the symbolic meanings related to the Flower lan-
guage context, we find a lower but still significant coefÏcient: 0.6 in Wikidata and 0.62 in the
ODOR dataset. We also recognize that correlation is not necessarily linked to causation. In this
8
It is also worth noting that this result is also dependent on the symbolism dataset used for the analysis. In the
case of this work, HyperReal is the largest knowledge graph about cultural symbolism, so it was the most suitable
dataset for this kind of analysis
553
case, given the very high correlation between the time-splitted datasets, we argue that the simi-
larity in the content and symbolism of two datasets is a potential sign that Floriography has not
changed how still art was portrayed in terms of specific subjects (and their symbolism). How-
ever, it is important to note that the annotations in Wikidata, which are mostly crowd-sourced,
might lack precision in recognizing specific types of plants or flowers, a limitation also shared
by computer vision algorithms, which might not be trained for every plant/flower specimen.
Given that Floriography is mainly based on specific flowers and plants, not being able to detect
them could hinder the results of the analysis. Additionally, there is a disproportion in the con-
tent of the before-and-after-1800 datasets, especially in the ODOR dataset, which has only 16
artworks depicting Floriography-related symbols after 1800, compared to 226 before 1800. In
Wikidata, there is more representation, with 655 artworks before 1800 and 332 after. Therefore,
we present the results of symbolic meanings that increased in popularity only using the results
from Wikidata. In this context, the top five symbolic meanings that have increased in popular-
ity are comfort and affection, which were present in 5.49% of artworks before 1800 and 12.95%
after, beauty and love, which increased from 11.14% to 23.49%, and gallantry, which increased
from 10.38% to 19.27%. Most of these meanings are related to messages of love, suggesting that
after the spread of Floriography, artists or commissioners of the artworks might have used this
cryptic language to send (perhaps) secret love messages through art. In summary, although the
results show high correlation (i.e., less variation) between symbolic meanings in the context of
Floriography before and after it spread, they also highlight patterns in the use of love-related
Flower language symbolism that require further investigation in future work.
5. Related Work
The majority of recent advances in quantitative art analysis focus on object detection in art-
works with different approaches, namely one-shot [16], weakly supervised models [10], trans-
fer learning [36], deep neural networks [29] or specific approaches tailored for image retrieval
[1]. We refer to [4] for a comprehensive review of the topic. The common aim of these ap-
proaches is to detect the elements depicted in artworks, but they do not try to infer deeper
meanings out of their detections, which would require linking the detected entities with other
datasets about symbolism. The work described in [32] studies the frequency of food depictions
in art in a data set consisting of approximately 750 artworks. Although the work mentions the
potential symbolic impact of food-related entities, it does not present a quantitative analysis
on that matter. Several studies address the quantitative study of art to detect variations in
colors, roughness, and brightness [13] or to automatically predict the styles of artworks and
classify them [28, 14]. Finally, there are studies that use LOD as the main source for quan-
titative artistic analysis. [2] uses LOD to analyze art historians’ interpretations of artworks,
focusing on a manually annotated Renaissance art dataset, and [7] combines deep learning and
artistic knowledge graphs for attribute prediction tasks. Neither of these two LOD-based ap-
proaches focuses on the still life genre. To the author’s knowledge, previous studies on still
art used qualitative methods [9, 15]. By leveraging LOD, this work can complement them by
providing results that emerge from a quantitative point of view.
554
6. Conclusion and future work
This paper presented two experiments on LOD-driven, quantitative analysis applied to still life
artworks. The recent advances in the representation of symbols and symbolic meanings in the
Semantic Web by HyperReal [26] allowed us to link the depictions of still life artworks with the
corresponding symbols. As a result, it was possible to connect the artworks with their poten-
tial symbolic meanings, dividing them also by the cultural contexts supporting these meanings.
We used this linking to measure the distribution of Christian symbolism on the still life genre,
to quantitatively verify the theory by the art historian Ingvar Bergström, and also to highlight
the impact of Floriography on this genre. As mentioned in Section 4, the main limitation of
this work is the lack of granularity of both the annotations and the detection from computer
vision. Having a system capable of detecting specific plants and flowers would require a very
high amount of training data. At the same time, finding botanical experts to annotate paintings
with specific plant and flower specimens can be a long and expensive task. Recent experiments
in data synthetization and diffusion-based augmentation for cultural heritage data show great
potential and could be a possible solution to this problem [8]. Another limitation regards the
content of HyperReal. Ingesting more symbolic data into the knowledge graph could be bene-
ficial to capture more instances of symbolism. With recent advances in text classification and
knowledge graph generation tasks by Large Language Models (LLMs), it could be possible to
automatize the analysis of unstructured data on symbolism and converting it into the structure
of HyperReal [35]. Moreover, the whole experiment setup is based on the hypothesis that the
still life artworks were created to convey deeper meanings. This is still an open debate among
art historians, as some argue that in specific cases the representation of still art was simply com-
missioned by people who wanted to showcase their great hunting results or boast about their
possessions [15]. For future work, we plan to analyze other aspects of still life, such as the role
and symbolic impact of Vanitas, which is another highly debated topic among art historians [9,
30]. Other theories that could be analysed in future work include the global and colonial origin
of materials and objects represented in still life [11]. Additionally, we plan on extending the
analysis made on Christian symbolism in still life to other cultural contexts that can be found
in HyperReal, drawing comparisons between them and the Christian context analyzed in this
work. Finally, given the inherent relationship between art and symbolic meanings, the quanti-
tative analysis of art through its symbolism proposed in this work can be applied to other art
genres beyond still life.
Acknowledgments
The Overleaf Writefull plugin was used to improve the syntax and flow of some sentences. No
large language model was used to generate sections or paragraphs of this paper from scratch.
555
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