# Marriage is a Peach and a Chalice:
Modelling Cultural Symbolism on the Semantic Web

Bruno Sartini<EMAIL_ADDRESS>0000-0002-9152-4402 University of
BolognaBolognaItaly40126 ,  Marieke van Erp<EMAIL_ADDRESS>0000-0001-9195-8203 KNAW Humanities ClusterOudezijds Achterburgwal
185AmsterdamThe Netherlands1012 DK and  Aldo Gangemi<EMAIL_ADDRESS>0000-0001-5568-2684 University of BolognaBolognaItaly40126

(2021)

###### Abstract.

In this work, we fill the gap in the Semantic Web in the context of Cultural
Symbolism. Building upon earlier work in (Sartini and Gangemi, 2021), we
introduce the Simulation Ontology, an ontology that models the background
knowledge of symbolic meanings, developed by combining the concepts taken from
the authoritative theory of Simulacra and Simulations of Jean Baudrillard with
symbolic structures and content taken from “Symbolism: a Comprehensive
Dictionary” by Steven Olderr. We re-engineered the symbolic knowledge already
present in heterogeneous resources by converting it into our ontology schema
to create HyperReal, the first knowledge graph completely dedicated to
cultural symbolism. A first experiment run on the knowledge graph is presented
to show the potential of quantitative research on symbolism.

symbolism; semantic web; ontology; linked data; knowledge graph

††journalyear: 2021††copyright: acmlicensed††conference: Proceedings of the
11th Knowledge Capture Conference; December 2–3, 2021; Virtual Event,
USA††booktitle: Proceedings of the 11th Knowledge Capture Conference (K-CAP
’21), December 2–3, 2021, Virtual Event, USA††price: 15.00††doi:
10.1145/3460210.3493552††isbn: 978-1-4503-8457-5/21/12††ccs: Information
systems Web Ontology Language (OWL)††ccs: Information systems Query
languages††ccs: Information systems Resource Description Framework (RDF)

##  1\. Introduction

Symbols are strongly related to human expression and communication, for this
reason, they can be found in many contexts such as art, literature, music, as
well as more recently in movies and commercials. However, the knowledge about
symbolism, which encompasses interpretations of cultural objects, along with
the information about canonical symbols from different cultures is mostly
stored in unstructured sources such as encyclopedias and dictionaries of
symbols. Accessing and processing this information quantitatively is therefore
currently not possible. This paper presents a novel approach in modeling
cultural symbolic knowledge. We focus on conventional symbolic knowledge as
expressed by experts or in a dictionary as opposed to symbolic knowledge
expressed in a specific work of art.

Symbols are complex objects: the same symbol might convey different meanings
depending on the cultural or artistic context in which it is found (Frazer,
1922; Roberts, 2013). Furthermore, different cultures might convey the same
meaning with different symbols. This is for example the case for the concept
of marriage, that is expressed in the Chinese Culture by the symbol of a peach
(Otto, 2014), but in the Celtic context, the concept of marriage is expressed
by the symbol of a Chalice (Olderr, 2012).

Linked open data sets in the cultural heritage domain currently lack domain-
specific ontologies that are able to express symbolic relationships in a
structured way. (Sartini and Gangemi, 2021) explains how the lack of semantic
models on the domain of symbolism negatively affects the interlinking of
resources through their symbols and shows promising results in the application
of a prototype ontology schema to a small set of data. Given the impact of
symbols, not only on the representation, comparison and evolution of human
cultures, but also in common expressions, we are bringing symbolic knowledge,
that has been accumulated throughout centuries, into a machine readable and
open format to foster quantitative research on this topic.

Specifically, our goal is to fill the gaps on cultural symbolism that
currently exist in linked open data through: (i) the Simulation Ontology, a
newly developed ontology that conceptualises symbolic relationships; and (ii)
HyperReal, a knowledge graph that contains data from heterogeneous sources
converted into the Simulation Ontology schema. The Simulation Ontology and
HyperReal are interoperable with current knowledge graphs, open resources and
utilisable to encode symbolic knowledge contained in written unstructured
sources.

##  2\. State of the art

In this section, we analyse the coverage of existing ontologies and knowledge
graphs with respect to their symbolic content and whether these are modelled
as instances of symbolism, and relationships that link a symbol to its
symbolic meaning.

General domain knowledge bases, such as Wikidata (Vrandečić and Krötzsch,
2014) and DBpedia (Auer et al., 2007) contain some properties that link a
resource to its symbolic meaning. In Wikidata, the property
P4878111https://www.wikidata.org/wiki/Property:P4878 (symbolizes) is a
qualifier for statements in which the property
P180222https://www.wikidata.org/wiki/Property:P180 (depicts) is used and it
should represent the symbolic meaning of elements depicted in a work of art.
Out of more than half a million elements that have been linked to a work of
art using the P180 property, only 313 have a symbolizes qualifier.333This data
was extracted through two queries in the Wikidata SPARQL portal: the query
https://w.wiki/3z56 counts the number of times P4878 is used as a qualifier,
the query https://w.wiki/3z5A counts the number of elements linked with the
property P180.

DBpedia uses the property dbp:symbol444http://dbpedia.org/property/symbol to
link a concept to a symbol that represents it. The range of the dbp:symbol
property is very general, as the property is used both to describe cultural
symbols, for instance thunderbolts as the symbol of
Zeus555https://dbpedia.org/page/Zeus but also to express that “metro” is the
symbol of the Atocha railway station in Madrid.
666https://dbpedia.org/page/Madrid_Atocha_railway_station

Iconclass (Couprie, 1983) is a classification system used for attributing of
subjects to works of art. Each subjects is given a specific code according to
the hierachical structure of the system. Specific codes are also given for
attributing subjects that appear with symbols, for example, the Iconclass code
11HH(MARY MAGDALENE)777http://iconclass.org/rkd/11HH%28MARY%20MAGDALENE%290/
is defined as “the penitent harlot Mary Magdalene; possible attributes: book
(or scroll), crown, crown of thorns, crucifix, jar of ointment, mirror[…]”. In
this sense, Iconclass contains relevant symbolic information for western art
subjects, but its linked open data version consists of a hierarchical SKOS
vocabulary. Our work, compared to Iconclass, uses linked open data semantic
web technologies to express more specific relationships between symbols and
their meaning.

CIDOC CRM(Bekiari et al., 2021) is a popular conceptual model for describing
museum and cultural heritage artefacts. Its event-centric structure makes it
possible to attribute a particular meaning to a specific cultural object. This
possibility was extended by the VIR ontology (Carboni and de Luca, 2019),
based on CIDOC, using a specific property
symbolize888https://ncarboni.github.io/vir/#K14_symbolize url not working for
every browser. Persistent URI of vir ontology: http://w3id.org/vir to link
certain elements in a representation of an artwork to their symbolic meaning.
The model was enriched to describe complex iconological case studies of
interpretation (Baroncini et al., 2021). However, the dependency of the
interpretation acts makes symbolic relationships encoded using this ontology
solely based on the single work of art (i.e. not generalized). In other words,
currently CIDOC and VIR permit to describe symbols that are only valid in the
context of the work of art in which they are found as a result of an
interpretation. The purpose of our work is to first introduce a model that can
express general background symbolic knowledge without covering the
hermeneutical act of interpretation. Parts of CIDOC model will be re-used to
develop an additional ontology of iconographic interpretations in future work.

Gartner (Gartner, 2020) proposes an ontology to conceptualize iconographical
recognition of subjects in artworks according to Panofsky’s second level of
interpretation.999Panofsky’s second level of iconographic interpretation is
about the subject matter of works of art. In this level characters, symbols,
places, events, allegories and stories are associated to the artistic motifs
present in the work of art (Panofsky, 1962). Among the classes that are
conceptualized in Gartner’s ontology there is a mention of symbols as
recognizing elements for specific work of art subject. Unfortunately, the
ontology has not been released yet.

Symbolic information can also be found in Wordnet (Fellbaum, 1998), a lexical
database of words and relationships between them that has been under
development by cognitive linguists since 1985. The dollar sign101010
http://wordnet-rdf.princeton.edu/id/06834465-n for example is defined as “a
symbol of commercialism or greed”, but it does not contain structured
information to distinguish between its meaning as denoting a currency and as a
symbol of greed. Only in some cases, a meaning is described as figurative,
although only in its definition. For instance, for
Albatross111111http://wordnet-rdf.princeton.edu/id/05697450-n the definition
“(figurative) something that hinders or handicaps” appears in the textual
description of the entry. Wordnet’s aim is not to distinguish between symbolic
and literal meanings, but the information that can be found in this resource
can be extracted and re-engineered in order to highlight the former from the
latter. As explained in Section 3.4, Wordnet’s symbolic data was ingested in
our knowledge graph.

##  3\. Ontology design and implementation

In this section, we explain the design stages of the ontology together with
the first implementation of the ontological model through a conversion of data
from multiple heterogeneous sources.

The ontology was designed following the agile ontology development
methodologies of SAMOD(Peroni, 2016) and extremeDesign(Presutti et al., 2009).
The design of the ontology was completed in three SAMOD iterations. The first
iteration covers the aspects related to the top-down modelling (subsection
3.1), the second and third iterations cover aspects related to the data-driven
modelling (subsection 3.2). For each iteration, the following documentation
items were generated: (i) a motivating scenario; (ii) an example of data that
covers the scenario in natural language; (iii) a series of competency
questions in natural language with the expected results related to the
example; (iv) a glossary that contains descriptions of the terms used in the
motivating scenario; (v) an ABOX that contains the example encoded using the
ontology schema as a turtle121212https://www.w3.org/TR/turtle/ serialisation,
(vi) unit tests that test the competency questions. The full documentation is
available at https://www.w3id.org/simulation/development.

###  3.1. Top-down Ontology Development

Eco (Eco, 1996) presents an overview of different theories on symbols and
symbolism. Compared to the highly debated topic (according to (Eco, 1996)) of
what symbols are and what can be considered a symbol, there is less discussion
about the relationship that links the symbol to what it symbolizes. This
relationship is partly related to Eco’s view on ratio difficilis: the meaning
of the expression is connected with its content and the “deciphering” or
inference of this meaning depends on whether the correlation between the
expression and content is based on pre-existing rules or it is something
personally created by the issuer of the symbolic linking.

Of the theories in Eco’s overview, Baudrillard’s Simulation and Simulacra
(Baudrillard, 1981), mentions the terms simulation, simulacrum and reality
counterpart as the three main elements of a symbolic relationship. A
simulation is intended as the relationship between a symbolic element and its
meaning. The meaning expressed in a simulation is different from the literal
meaning of the element. Lion as a symbol of courage (Olderr, 2012) is a
simulation, lion as “a large wild animal of the cat family with yellowish-
brown fur that lives in Africa and southern Asia” (Press, 2021) would not be
considered as a simulation. The simulacrum is the symbolic element, it is the
representation of something else. The reality counterpart is the “something
else” represented by the simulacrum, not the literal meaning of the simulacrum
itself. An olive branch (simulacrum) represents “peace” (reality counterpart).
The simulation “olive branch-peace” is the symbolic relationship that links
these two elements. Simulations are not universally valid, some only exist in
specific settings or contexts. An owl is the symbol of death in Hindu,
Japanese, and Mayan contexts. That means that the simulation owl-death exists
in those contexts. On the other hand, in a Siberian context, owls are symbols
of helpful spirits (Olderr, 2012).

Figure 1. Simulation Pattern - Classes and Properties

From the analysis of Simulation theory, the following competency questions
emerged: (Q1.1) What are the reality counterparts of the simulations that have
a specific simulacrum? (Q1.2) What are the simulations that exist within a
certain context? (Q1.3) What are the simulations in which a certain element
participates as either the simulacrum or the reality counterpart? (Q1.4) What
are the simulacra that share the same reality counterpart in their respective
simulations and what is the context in which their simulations exist? A
negative competency question131313Intended as a competency question which
should not return any result because it asks for something which does not
follow the logical structure of the ontology. was also formulated: (Q1.5) Are
there simulations that have multiple simulacra?

As the ontology was not intended as an in-depth description of the
philosophical theories of Baudrillard, only the main concepts of his work were
reused for the conceptualization of symbolic meanings. To establish a solid
foundation in existing good practices, (Hitzler et al., 2016) three off-the-
shelf ontology patterns were reused to design the OWL2 version of the
Simulation ontology:
situation141414http://www.ontologydesignpatterns.org/cp/owl/situation.owl
provides a general structure and vocabulary for N-ary relations;
semiotics151515http://www.ontologydesignpatterns.org/cp/owl/semiotics.owl
founds Baudrillard’s simulations as semiotic acts. An expression (here a
simulacrum) denotes a reference (here a reality counterpart), with an
interpreted meaning (here muted, but actualizable in the context of a specific
interpretation act e.g. in iconology); and information
realization,161616http://www.ontologydesignpatterns.org/cp/owl/informationrealization.owl
which lets us distinguish information objects from their realization or
manifestation (here relevant for distinguishing conventional simulations from
simulations interpreted e.g. for a specific work of art). In other words, the
simulation ontology for conventional symbolic meaning presented here holds
between pure information objects (e.g. a lion prototype), and concepts (e.g.
force), or stereotyped individuals (e.g., a generic Persia).171717This is
contrasted by using different ontologies that implement simulation
occurrences, e.g. a specific interpretation of Persepolis’ Achaemenid Persian
relief with the Sign of Lion, as a realized simulacrum of Achaemenid Persia
power. The design pattern for simulations is presented in Figure 1.

Simulacra, reality counterparts, and contexts of symbolic meanings are linked
in this pattern through the n-ary relationship (situation) class Simulation.
For example, in an Egyptian context, bees signify resurrection. This can be
modelled through the hasSimulacrum, hasRealityCounterpart and hasContext
properties in our model, as shown in Figure 2.

To evaluate the initial version of the developed pattern, we qualitatively
compare its ontological structure to the structure of a dictionary of symbols.
We selected Olderr’s (Olderr, 2012) dictionary of symbols for this task
because it offers more than 40,000 symbolic meanings. Moreover, its structure
is similar to a dictionary of synonyms and antonyms: for each symbol, a list
of potential symbolic meanings is provided along with the context in which
those symbolic meanings are valid, often without any additional information.
As we will discuss in the conversion Section 3.4 the repetitive patterns in
the structure of this dictionary facilitate the automatic conversion of its
data into the ontology schema. An example of fitting the pattern to the
structure of the dictionary is shown in Figure 3. The example shows the “hook”
entry of the dictionary. Hook translates in our ontology as the simulacrum.
The terms in green (e.g. Christian) represent the contexts in which some hook
simulations are valid. The terms in blue represent reality counterparts of
simulations that have hook as a simulacrum. However, some reality counterparts
(in yellow) are introduced by phrases such as “related to”, “attribute of”
that suggest a specific type of simulation. Also, the terms in red represent
some variants of the “hook” entry that have their own symbolic meaning. Both
variants and specific simulations were addressed in the design extension of
the pattern. The conceptualized classes and properties derived from this
qualitative comparison are described in the Subsection 3.2.

Figure 2. Simulation Pattern - Bee-resurrection simulation example Figure 3.
Fitting the simulation pattern over Olderr’s dictionary "hook" entry

###  3.2. Data-driven Ontology Enrichment

To cover concepts not dealt with in Baudrillard’s Simulation theory, we add 7
new properties and 9 new classes via a bottom-up approach.

Simulations are linked to the sources that support their existence, opening up
new competency questions: (Q2.1) What are the simulations and respective
reality counterparts that have the same simulacrum but a different source?
(Q2.2) What are the contexts of the simulations listed in a specific source?
(Q2.3) What are the sources of a specific simulation? Another negative
competency question was formulated: (Q2.4) Are there simulations that do not
have a source? The source of the simulation was included in the ontology using
the class source. PROV-O(Lebo et al., 2013) is the W3C standard ontology to
express provenance. The property of PROV-O
wasDerivedFrom181818https://www.w3.org/TR/prov-o/#wasDerivedFrom is used to
link a simulation to its source.

In Olderr’s dictionary of symbols (Olderr, 2012), simulacra and reality
counterparts can have variants that can belong to different simulations
compared to the original simulacra or reality counterparts. Variants in this
ontology are either represented as (i) a narrower concept than the original
simulacrum or reality counterpart, such as night bird, which in Olderr’s
dictionary is a variant of bird, (ii) as a set of things of which the original
simulacrum is a part of, such as in the case of black and white, which is a
variant of black in the same dictionary, or (iii) as the simulacrum put in a
specific situation such as bloodstone placed in a glass of water during a
drought as a variant of bloodstone.

Variants are conceptualized in the ontology with the introduction of the
property hasVariant that links either a simulacrum or reality counterpart to
its variant (which can be either another simulacrum or a reality counterpart).
We test this property using additional competency questions to handle the
conceptualization of variants: (Q3.1) What are the variants of a certain
element? (Q3.2) What are the reality counterparts of the simulations with a
specific simulacrum or its variants?

In Olderr’s dictionary, Simulations can be specialized according to the
specific symbolic relationship that links a simulacrum with its reality
counterpart. If a simulacrum is the emblem of a certain reality counterpart,
or an allusion, this specific relationship can be expressed through
specialized simulations. Olderr (Olderr, 2005) provides some definitions for
specific symbolic relationships such as the Allusion, described as

> […]a reference to an historical person or event, or an artistic or literary
> work. To have an “albatross around one’s neck” is an allusion to Coleridge’s
> poem “The Rhyme of the ancient Mariner”.

or the Association, described as

> […]something linked in memory or imagination, or by correlation or an
> analogy with an object, idea, person, or event. The letter “A” is associated
> with beginning.

Although we suggest potential users of the ontology to follow his definitions,
given the highly subjective topic of the ontology, we tried to not limit the
use of the classes through logical constrains, as seen in section 3.3. In some
specialized simulations, the reality counterpart might not be the exact
symbolic meaning of a simulacrum. In these cases, the reality counterpart
might be something that is prevented, elicited, restored by the simulacrum in
a symbolic way. Finally, some simulations might have a reality counterpart
that represents the symbolic meaning of the simulacrum, and an additional one
that represents something that is prevented, elicited, restored by the
simulacrum. For instance, in an Arabian context, an agate is seen as a charm
for healthy blood (Olderr, 2012). Therefore, the resulting simulation is
agate-charm-healthyBlood where agate is the simulacrum, charm is a reality
counterpart, healthy blood is an elicited reality counterpart and Arabian is
the context. Given that a reality counterpart, such as healthy blood, does not
change its identity whether it is prevented, elicited, or generally
symbolically meant by a Simulacrum, we decided to introduce specific reality
counterpart relationships as sub-properties of hasRealityCounterpart and not
as sub-classes of RealityCounterpart.

To address specific simulations, we formulate the following competency
questions: (Q3.3) What are the simulations in which the simulacra are seen as
symbolical protection against reality counterparts? (Q3.4) What are the
simulations that have a specific reality counterpart and other additional
reality counterparts, and what specific relationship link those simulations to
their reality counterparts? (Q3.5) What are the simulations and their
respective simulacra, contexts and reality counterparts in which their
simulacrum is a symbolical cure for their reality counterpart?

The newly added classes and properties are summarized in Table 1. Examples of
use, and a glossary containing definitions of all classes and properties can
be found in the ontology development documentation.

Class |  Specific reality counterpart property
---|---
Association Simulation |  no specific property
Correspondence Simulation |  no specific property
Manifestation Simulation |  no specific property
Relatedness Simulation |  no specific property
Attribute Simulation |  no specific property
Allusion Simulation |  no specific property
Protection Simulation |  preventedRealityCounterpart
Emblematic Simulation |  no specific property
Healing Simulation |  healedRealityCounterpart
no specific simulation class |  restoredRealityCounterpart
no specific simulation class |  easedRealityCounterpart
no specific simulation class |  elicitedRealityCounterpart
Table 1. Specific Simulation and related reality counterpart properties

###  3.3. Ontology Axiomatisation

The simulation ontology uses OWL logical axioms to specify its
conceptualisation, to enable logical inferences on the symbolic knowledge
graph, and to perform automated classification and consistency checking.

Besides the basic classes and properties that have been introduced in the
previous sections, and their subsumption and domain/range axioms, we exemplify
some OWL axioms that perform restrictions on the main classes, written in
Manchester Syntax(Horridge et al., 2006).

Source | # of Simulacra | # of Rc | # of Contexts | # of Simulations | # of Triples
---|---|---|---|---|---
Olderr’s dictionary of symbols | 8,900 | 17,959 | 303 | 37,647 | 450,679
DBpedia | 3,024 | 782 | 36 | 3,727 | 47,696
Wordnet | 61 | 76 | 5 | 81 | 1,191
Total | 11,663 | 18,676 | 323 | 41,416 | 498,525
Table 2. Knowledge Graph statistics

  * •

Simulation:

            hasContext some Context
        hasRealityCounterpart some RealityCounterpart
        hasSimulacrum exactly 1 Simulacrum
        wasDerivedFrom some Source

  * •

Healing Simulation:

            healedRealityCounterpart exactly 1
        RealityCounterpart

  * •

Protection Simulation:

            preventedRealityCounterpart exactly 1
        RealityCounterpart

We also introduce a property chain to provide a direct relation facility
between simulacra and reality counterparts:

        isSimulacrumOf o hasRealityCounterpart
        SubPropertyOf symbolicMeaning

Further details can be found in the ontology
documentation.191919https://www.w3id.org/simulation/docs

###  3.4. Creating the Symbolic Knowledge Graph

The knowledge base was developed by extracting and converting data from
different sources: DBpedia, Wordnet and Olderr’s Symbolism: a comprehensive
dictionary.

#### DBpedia Conversion

We use the DBpedia SPARQL endpoint202020https://dbpedia.org/sparql to retrieve
resources related to the property dbp:symbol and for resources of type
skos:Concept212121http://www.w3.org/2004/02/skos/core#Concept that contain the
string "symbol" in their label and that have these concepts as their subject
(using the property dct:subject.222222http://purl.org/dc/terms/subject) For
instance, the resource dbr:Eagle232323http://dbpedia.org/resource/Eagle is the
subject of
dbc:National_symbols_of_Liechtenstein.242424http://dbpedia.org/resource/Category:National_symbols_of_Liechtenstein

To filter out station signs and chemical codes that are also modelled using
the dbp:symbol property, but are purely iconic tools, we exclude resources of
type Railway Stations and Public Companies. On the one hand, in the triple
?subject dbp:symbol ?object, ?subject becomes our reality counterpart, and
?object the simulacrum. The ?type  of the same subject, in the triple ?subject
rdf:type ?type was considered as the context of the simulation.

On the other hand, in the triple ?subject dct:subject ?object, ?subject became
the simulacrum and ?object (cleaned of the initial parts such as “National
Symbol of” or “Symbol of”) became the reality counterpart. The contexts, in
this second case were not extracted for these triples, therefore General or
Unknown was set as the context for every simulation. For instance, in the
triple dbr:Eagle dct:subject dbc:dbc:National_symbols_of_Liechtenstein , Eagle
became the simulacrum and Liechtenstein became the reality counterpart in a
Simulation with a General or Unknown context. Our DBpedia conversion yielded
3,727 simulations expressed in 47,696 triples.

#### WordNet conversion

From Wordnet, we extract Symbol and emblem synsets252525http://Wordnet-
rdf.princeton.edu/id/05773412-n and http://Wordnet-
rdf.princeton.edu/id/06893714-n respectively and their hyponyms, as well as
synsets that contain phrases such as symbol of or emblem of in their
definition. For each element, we extract its label and definition. The natural
language definition of each synset was processed to extract potential contexts
and symbolic meanings. For instance, the Penelope synset262626http://wordnet-
rdf.princeton.edu/id/09616318-n has "Penelope" as a label and "(Greek
mythology) the wife of Odysseus and a symbol of devotion and fidelity[…]" as
the definition. The label of the synset was converted into the simulacrum and
we extracted the context and reality counterparts from the definition. In this
case, two simulations were created from this synset, penelope-fidelity and
penelope-devotion, both with greekMythology as a context. The penelope
simulacrum was then linked to its original WordNet synset with the property
owl:sameAs. Our WordNet conversion process yielded 81 simulations captured in
1,191 triples.

#### Olderr’s Dictionary Conversion

We used the markup such as boldface in Olderr’s dictionary to distinguish
between different lemmas, contexts and variants and converted this to RDF. A
simulation URI was formed by joining the simulacrum and reality counterpart
labels with a hyphen. The style consistency in the original document made the
conversion process almost completely automatic. Some manual corrections had to
be done after the automatic conversion in the rare cases of mistakes in the
source.272727https://github.com/br0ast/simulationontology lists examples of
manual corrections A list of bigrams such as related to and protection from
were used to create Relatedness and Protection simulations, respectively.
37,647 simulations were generated from the dictionary captured in 450,679
triples.

For every converted simulation, the respective sources (either DBpedia,
Wordnet or Olderr’s dictionary) have been added using the prov:wasDerivedFrom
property.

Figure 4. Simulation Ontology - Classes and Properties

#### Quality Control

After the conversion, we performed several error analysis cycles to assess the
knowledge graph’s quality, we adjusted the algorithms, and reconverted the
knowledge graph where needed. The most common errors were instantiations of
some simulacra or reality counterparts that were not linked to any simulation.
Other errors occurred during automatic URI generation or in the labeling of
elements. Specifically for Olderr’s dictionary, some errors were due to
editing errors in the original source. In those cases, the txt version of the
dictionary was manually corrected.

Table 2 summarizes the number of resources that were generated by the
conversion.282828The totals are not the exact sum of the previous numbers
because some simulacra, reality counterparts, contexts and simulations are
shared by different sources. An evaluation of the conversion algorithms can be
found in Section 4.4.

### Ontology and knowledge graph release

The w3id service was used to obtain persistent URIs. The current version of
the Simulation Ontology is available at https://www.w3id.org/simulation/docs.
Its structure is shown in Figure 4.

Due to copyright issues, the public version of HyperReal contains only the
converted data from DBpedia and WordNet. The knowledge graph is available at
https://www.w3id.org/simulation/data/ and includes the import axiom for the
ontology schema.

Finally, https:www.w3id.org/simulation/development and
www.w3id.org/simulation/code are the persistent URIs that lead respectively to
the ontology development GitHub repository and the scripts used to create
HyperReal and our analyses (see the experiment in section 5) over it.

##  4\. Evaluation

In this section, we discuss the evaluation of the ontology via competency
questions and an internal consistency check as well as the evaluation of the
automatic conversion of Olderr’s dictionary.

###  4.1. Competency Question evaluation

The ontology requirements were expressed through competency questions
mentioned in Section 3. For each iteration of the ontology, we executed unit
tests to verify that the results of the SPARQL query that formalizes the
competency question matched the expected results of those questions in a toy
dataset. All unit tests are available from our Github repository in the form
of Jupyter notebooks to promote the reproducibility of our results.292929The
jupyter notebooks for testing the CQs are available for each SAMOD iteration,
respectively in: 1, 2, 3.

All competency questions matched with their expected results. For instance, in
CQ2.2 “What are the simulations and respective reality counterparts and
sources that have the same simulacrum but a different source?” the expected
results for the toy dataset are the simulations (i) olive-fertility with
fertility as a reality counterpart and dictionaryOfSymbols1 as a source and
(ii) olive-immortality with immortality as a reality counterpart and
dictionaryOfSymbols2 as a source. Listing 1 shows the formalized query in
SPARQL. The retrieved results from this query match the expected results.

⬇

PREFIX ex: <https://example.org/>

PREFIX sim: <https://w3id.org/simulation/ontology/>

PREFIX prov:<http://www.w3.org/ns/prov#>

SELECT distinct ?simulation ?rc ?source WHERE {

?simulation1 sim:hasSimulacrum ?simulacrum ;

prov:wasDerivedFrom ?source1 .

?simulation2 sim:hasSimulacrum ?simulacrum ;

prov:wasDerivedFrom ?source2 .

filter (?simulation1 != ?simulation2 &&

?source1 != ?source2) .

?simulacrum sim:isSimulacrumOf ?simulation .

?simulation sim:hasRealityCounterpart ?rc ;

prov:wasDerivedFrom ?source .}

Listing 1: CQ2.2 Formalization in SPARQL Element | Precision | Recall | F1
---|---|---|---
Simulation | 0.96 | 0.97 | 0.97
Simulacrum | 0.97 | 0.98 | 0.98
Reality counterpart | 0.96 | 0.97 | 0.97
Context | 0.97 | 0.98 | 0.98
Type of Simulation | 0.94 | 0.95 | 0.94
Variant | 0.97 | 0.98 | 0.98
Average | 0.96 | 0.97 | 0.97
Table 3. Conversion algorithms evaluation metrics (reporting micro averages)

###  4.2. Automatic ontology evaluation

Foops! (Garijo et al., 2021) is a web application that evaluates ontologies by
verifying that they comply with FAIR principles (Garijo and Poveda-Villalón,
2020). According to Foops!, our ontology scores 77%. The tool highlighted the
ontology has not been inserted in a public ontology metadata registry of
ontology such as LOV (Vandenbussche et al., 2017) and that it currently lacks
versioning information. Both of these aspects will be addressed in future
releases.

The ontology’s syntax was evaluated through the online RDF validator offered
by w3.303030https://www.w3.org/RDF/Validator/ The tool highlighted no pitfalls
in syntax.

###  4.3. Reasoning over the knowledge graph

The entire knowledge graph was analyzed by the HermiT Reasoner (Glimm et al.,
2014) to check inconsistencies. The reasoner showed no inconsistencies. The
OWL Profile Checker tool (V1.1.0)313131https://github.com/stain/profilechecker
positions the knowledge graph in the OWL2-DL profile.323232The description
logic complexity of the entire knowledge graph is ALCHOIQ (role hierarchies,
nominals, inverse properties, qualified cardinality restrictions).

###  4.4. Conversion Evaluation

To evaluate the quality of the conversion of the algorithms, we manually
annotated 112 simulations sampled from some lines of Olderr’s dictionary and
compared it to the automatic conversion of those same lines using precision,
recall and F1 measure of simulacra, reality counterparts, variants, contexts
and simulations and specific types of simulations retrieved.

On average, the conversion achieves a performance of 97% of precision (micro),
recall (micro) and F1 (micro). Table 3 summarizes the metrics for this
evaluation. Errors in the conversion stem from deviations from the dictionary
structure, for example when two reality counterparts are provided instead of
one.

Figure 5. Distribution of coloured simulacra across the symbolic meanings of a
white rose

##  5\. Case study: white roses

In this use case, we show how the knowledge graph can be used to investigate
the different meanings of a symbol. We investigate whether there is a
correlation between the symbolic meaning of a simulacrum and its colour. We
chose white roses as our symbol as they occur across many cultures and in
different contexts.

We extract the symbolic meanings of a white rose from the knowledge graph
through the SPARQL query shown in Listing 2. With another series of queries
(available on Github), we extract all simulacra that share a symbolic meaning
with a white rose that are associated with the colours red, green, black,
white, gold, blue, and purple. We created nine sets; one for each symbolic
meaning of the white rose, and the coloured extracted simulacra were placed in
the set they share the symbolic meaning with (in case of more than one
matching of symbolic meaning they were placed in more than one set). For
instance, one of the symbolic meanings of a white rose is purity. golden hair,
white knight, white swan share the same symbolic meaning so they were put in
the purity set. Only the simulacra’s colours were kept, leaving each set with
the frequency of colours of simulacra that had the same symbolic meaning.

Figure 5 shows the distribution of coloured simulacra across the different
symbolic meanings of a white rose we could extract from our knowledge graph.
Every bar represents a symbolic meaning of the white rose. The length of each
colour in a bar is proportional to the number of simulacra of that colour that
share the same symbolic meaning. As the figure indicates, the white colour is
present in almost all symbolic meanings, with a high peak in purity and
innocence. This suggests that the white colour itself brings these symbolic
values to white objects. Moreover, apart from the charm symbolic meaning,
white has the highest percentage of colour in all meanings of white rose
except in the meaning for faith which is half white and half blue.

⬇

PREFIX kb: <https://w3id.org/simulation/data/>

PREFIX sim: <https://w3id.org/simulation/ontology/>

SELECT ?rc WHERE { kb:whiteRose

sim:isSimulacrumOf+/sim:hasRealityCounterpart ?rc .}

Listing 2: White rose symbolic meaning SPARQL query

##  6\. Conclusions and Future Work

In this paper, we present a new ontology and knowledge graph that fill an
existing gap in the domain of symbolic knowledge. Our ontology and knowledge
graph bring together knowledge from semiotics, mediation, symbolism, and
semantic web. We hope this resource will foster research on new quantitative
approaches to symbolism that were not possible beforehand.

In future iterations, the knowledge graph could be further enriched by adding
information from additional resources describing symbolic information such as
Iconclass (Couprie, 1983). A SKOS (Baker et al., 2013) taxonomy could be
generated not only to link simulacra and reality counterparts to their
variants, but also in between contexts, to enable more complex queries
regarding similar, broader or narrower contexts of simulations. Whilst our
ontology and knowledge graph focus on symbolism, metaphorical meanings are
partly related to symbolic meaning (Coulson and Matlock, 2001), especially
when cultural symbols have less conventional, specific meaning. We aim to link
our ontology and knowledge graph to MetaNet(Stickles et al., 2016), an online
repository of metaphors and frames. Further integration with external general
knowledge graphs such as Wikidata or Framester(Gangemi et al., 2016) would
allow interoperability between heterogeneous sources in the context of
symbolism, extending the reach of structured symbolic knowledge. This same
knowledge could be used to enrich broad knowledge graphs, such as ArCo
(Carriero et al., 2019), a knowledge graph of Italian cultural heritage. By
automatically assigning potential simulations and symbolic meanings to ArCo
objects, scholars and cultural heritage professionals could get access to rich
symbolic knowledge, allowing for the discovery of symbolic links between
objects that were previously hidden.

###### Acknowledgements.

This work has been partially funded from the Emilia Romagna Region (grant
agreement no. 462 25/03/2019).We thank the anonymous reviewers for their
constructive feedback.

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