=Paper=
{{Paper
|id=Vol-3637/paper44
|storemode=property
|title=ESTHER: Ontology of Rhetorical Figures in English
|pdfUrl=https://ceur-ws.org/Vol-3637/paper44.pdf
|volume=Vol-3637
|authors=Ramona Kühn,Jelena Mitrović,Michael Granitzer
|dblpUrl=https://dblp.org/rec/conf/jowo/KuhnMG23
}}
==ESTHER: Ontology of Rhetorical Figures in English==
https://ceur-ws.org/Vol-3637/paper44.pdf
ESTHER: Ontology of Rhetorical Figures in English
Ramona Kühn1,∗ , Jelena Mitrović1,2 and Michael Granitzer1
1
University of Passau, Innstrasse 43, 94032 Passau, Germany
2
Institute for Artificial Intelligence Research and Development of Serbia, Fruškogorska 1 21000 Novi Sad, Serbia
Abstract
ESTHER, the English ontology of rhetorical figures, is a formal domain ontology of rhetorical figures
in the English language. Rhetorical figures convey a subtle meaning or sentiment that often cannot
be grasped by modern sentiment analyzers, machine translation systems, or text summarizers due to
the lack of annotated datasets and machine-readable formats. Our ontology is a formal documentation
of rhetorical figures to improve various natural language processing tasks. Existing ontologies in this
domain mainly focus on figures in other languages, such as Serbian or German. Our ESTHER ontology
fills this gap by modeling 85 English rhetorical figures. It aligns with existing ontologies to allow
interoperability with other languages. In addition, the ESTHER ontology includes hierarchical relations
and compositional features of figures to model their dependency relations. Besides identifying the most
important steps of the good practice of ontology modeling, we also provide an overview of current tools
and methods of ontological evaluation, validation, and documentation for the ESTHER ontology. The
ontology does not only serve as a knowledge base and documentation of rhetorical figures but also has a
practical use case. We developed a tool to show how the ontology helps annotators without linguistic
understanding to annotate instances of rhetorical figures.
Keywords
Rhetorical Figures, Ontology Modeling, Annotation, Natural Language Processing
1. Introduction
Language is a means of communication and has the power to transmit emotions, influence the
audience, or increase the memorability of a message. Non-literal language and rhetorical figures
play an essential role in this process. The rhetorical figure “metaphor”,1 for example, creates
images in the reader’s mind to explain complex issues with elements from known domains in a
non-literal way: “she has eagle eyes” or “he has a heart of gold”. However, to understand the
actual meaning, a certain amount of cultural and background knowledge is always required.
While it is easier for humans to spot rhetorical figures and grasp their meaning, it is difficult to
computationally detect them due to several reasons:
• First, there is a lack of annotated datasets that are large enough for training machine
learning models.
Ontology Showcase and Demonstrations Track, 9th Joint Ontology Workshops (JOWO 2023), co-located with FOIS 2023,
19-20 July, 2023, Sherbrooke, Québec, Canada.
∗
Corresponding author.
Envelope-Open ramona.kuehn@uni-passau.de (R. Kühn); jelena.mitrovici@uni-passau.de (J. Mitrović);
michael.granitzer@uni-passau.de (M. Granitzer)
Orcid 0000-0002-9750-0305 (R. Kühn); 0000-0003-3220-8749 (J. Mitrović); 0000-0003-3566-5507 (M. Granitzer)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
CEUR Workshop Proceedings (CEUR-WS.org)
Proceedings
http://ceur-ws.org
ISSN 1613-0073
1
A glossary of rhetorical figures and examples in natural language can be found in Appendix B.
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
• Second, there is a lack of formal descriptions of rhetorical figures as even the informal
definitions are heterogeneous, and the same figure can have different names.
• Third, there is a lack of knowledgeable human annotators and domain experts on the one
hand and a lack of formal rules for computer-assisted annotation on the other hand.
The detection of rhetorical figures is however relevant in the context of Natural Language
Processing (NLP), as each rhetorical figure has a function [1, 2], e.g., make text more memorable,
or fake news more credible. Understanding the function and meaning of rhetorical figures
and being able to detect them automatically contributes to the improvement of several NLP
tasks, such as sentiment analysis [3], argumentation mining [4], propaganda detection [5], hate
speech detection [6], or text summarization [7].
We modeled a formal domain ontology of rhetorical figures in the English language, called
ESTHER. It is a source of both human- and machine-readable knowledge. For example, it can
support the annotation of rhetorical figures in a text. Furthermore, it helps to derive rules from
the formal definitions, e.g., for the generation or rule-based detection of rhetorical figures. With
more datasets of rhetorical figures, classifiers can be trained, leading to a major improvement
of the above-mentioned NLP tasks.
Our methodology for the building process is guided by the good standards of ontology devel-
opment. We examined existing ontologies in the domain of rhetorical figures and decided to
align with the Serbian RetFig [8] and the German GRhOOT ontology [9] to guarantee interop-
erability between different languages. Additionally, we included the concept of hierarchical
relations between figures (e.g., every text that contains figure A always contains figure B) from
Harris et al. [10].
We also provide an overview of different methods and tools that are available for ontology
evaluation, validation, and documentation. Furthermore, we show the relevance of the ontology
in the context of NLP by developing a terminal-based tool called “FyF: Find your Figure!”. It is
based on the ontology and guides human annotators through the process of determining the
right name for a rhetorical figure.
The remainder of the paper is structured as follows: Section 2 gives an overview of relevant
ontologies in the same domain. In Section 3, the methodology, the structure of the ontology, and
its properties are described. Section 4 describes the annotation assistant tool that demonstrates
the practical use case of the created ontology. The conclusion in Section 5 summarizes our
work and gives ideas for future applications of the ontology. The ontology, the code, and
documentation are available online.2
2. Related Work
A common problem when working with rhetorical figures is their additional non-literal mean-
ing [3, 11] and their varying definitions. Ontologies can help here as they put an end to
“inconsistent descriptions”, and “ambiguous term definitions” from different sources, and allow
computational processing [12]. In the domain of rhetorical figures, different definitions, names,
2
https://github.com/kuehnram/ESTHER-Ontology
and categorizations for the same figures emerged over the years, leading to several inconsis-
tencies. A formal domain ontology unifies the descriptions and then allows computational
detection.
The first approach to modeling ontologies of rhetorical figures in the English language was
made by Harris and DiMarco [13], describing linguistic operations and cognitive effects of
figures. Later, Kelly et al. [14] extended this ontology within the scope of the project “RhetFig”
by adding formal linguistic and rhetorical classes that also serve as a basis for our ESTHER
ontology. Harris et al. [10] modeled an ontology focusing on cognitive effects and on the
hierarchical connectedness of different figures. They found out that some figures are co-located
with others, e.g., the figure anaphora is always a more specific form of the figure ploke. The OWL
ontology of O’Reilly et al. [15] focused on three rhetorical figures (gradatio, incrementum, and
climax). They also take into account that figures are connected or composed. We will formalize
those relations and also include them in our ontology. Wang et al. [16] developed an ontology
of the figure ploke. However, those ontologies only model a few figures. Ontologies that model
the most common figures in a language are the Serbian RetFig3 [8] modeling 98 figures and
the German GRhOOT ontology [9] with 110 figures common in the German language. The
RetFig ontology is actually based on the classification of Kelly et al. [14], whereas the GRhOOT
ontology is again based on the Serbian RetFig to guarantee interoperability between the two
languages. The rhetorical figures are modeled as instances, whereas the linguistic specificities
are defined as concepts. The authors expressed the desire to “build [...] an ontology of rhetorical
figures in English and other languages.” For figures of repetition, this was fulfilled by the
multilingual ontology [17] that combines the English Ploke ontology, the Serbian RetFig, and
the German GRhOOT.
Our ESTHER ontology aligns with the structure of RetFig and GRhOOT, additionally im-
plementing hierarchical relations as presented by Harris et al. [10] and O’Reilly et al. [15].
To show a practical application of the ontology, a decision tree for figures of repetition was
developed within the GRhOOT ontology. With our ESTHER ontology, we go a step further by
implementing a first version of the user interface envisioned by Kühn and Mitrović [18]. It is
realized by a terminal-based tool that assists human annotators in determining the names of
figures based on the English ESTHER ontology.
3. Modelling ESTHER
In this section, we describe the modeling methodology, the ontology design process, and the
structure of the ontology. The modeling of an ontology of rhetorical figures is a challenging
task because all the multiple names, spellings, and definitions have to be included. Also, the
categorization of figures is not consistent, but needs to be specified in the ontology. Figures can
belong to multiple categories and multiple linguistic elements (words, phrases, verses) can be
affected by a rhetorical figure. In addition, the relation between figures and the composition of
figures has to be considered.
3
Please note the spelling without “h” compared to the English RhetFig project [14].
3.1. State of the Art of Ontology Modeling
If the ontology development process follows a particular methodology, it has “normally higher
quality” [19]. We examined different approaches to ontological modeling (“The working ontolo-
gist” [20], “Understanding ontological engineering” [21], “Methontology” [22], “Methodology
for the Design and Evaluation of Ontologies” [23], “Ontology development 101” [24], “Ontol-
ogy: A practical guide” [25], “The enterprise ontology” [26], “eXtreme” [27, 28], “NeOn” [29],
“LOT” [30]) and found common approaches to building an ontology. We identified similar main
steps that are listed below and described in their respective sections.
1. Plan main tasks, define resources and tools (Section 3.2).
2. Define purpose, scope, and end-users (Section 3.3).
3. Formalize the conceptual model while reusing and integrating parts of existing ontologies
(Section 3.4).
4. Evaluate the ontology (Section 3.5).
5. Create documentation and maintain the ontology (Section 3.6).
Often, it is additionally recommended to use competency questions [20, 24, 27] that were
developed by Grüninger and Fox [23]. Those questions serve in the beginning as a specification
for the developers what the ontology should fulfill. They are specified by the users [27]. During
the development process or afterwards, the developers can test if the ontology contains enough
information by correctly answering those questions. However, drawbacks are that they cannot
cover every functionality of the ontology and that they are often not available, as the modeler
of the ontology already knows how the ontology will behave [20]. In our methodology, we
also include competency questions. For the integration of other ontologies in step 3, the
modeling approach of NeOn [29] helps by describing how existing ontologies can be reused or
included. This approach focuses rather on scenarios (building ontology from scratch, reusing,
re-engineering, merging) than on steps. For ontology evaluation, Gangemi et al. [31] suggest
three dimensions: the structural, functional, and usability profile dimension. For example, they
determine the depth of the ontology graph, cycle ratios, satisfaction of users, agreement of
domain experts, and many more. Unfortunately, the assessment appears to be rather subjective.
For ontology validation, SPARQL queries are often used in this domain [8, 16]. We consider
this to be rather an evaluation than a validation, as the queries are self-defined. However, there
are different tools available for formal validation. We identified different ontology validators,
such as the OOPS! ontology pitfall scanner [19], or the oQual ontology validator [31]. The
OnToology system [32] provides a validator for ontology syntax, similar to the RDF validation
of W3C.4
For the documentation of ontologies, different tools are available. For example, HTML
documentation tools, ontology browsers, and visualizations [33]. We identified the following
possible candidates: OnToology [32], LODE [34], Protégé [35], and Widoco [36], which is
actually a combination of LODE, WebVowl visualization [37], and OOPS! validation. OnToology
also provides documentation. In our case, the syntax checker did not work, and connecting it
to GitHub is more complicated than using Widoco. LODE combined with the WebVowl graph
is a good documentation approach.
4
https://www.w3.org/RDF/Validator/
We are aware that there is more than one solution to model a domain ontology [24], especially
in the domain of rhetorical figures with a quite diverse knowledge base. Also, the modeling
depends on how the ontology will be used later.
3.2. Main Tasks, Resources, and Tools
Our main task is to collect information about rhetorical figures in English and to structure
them. Furthermore, we need to build the ontology and validate it. We are using resources
that contain definitions and examples of rhetorical figures. The standard book in this field was
written by Lausberg [38]. However, as it is highly interlinked with numerous subsections and
mixes languages within entries (German, Greek, Latin, French, English), it is complicated to
fully understand it. Therefore, we identified the book of McGuigan [39], which aims to explain
rhetorical figures to students and therefore provides understandable definitions, explanations
of the functions of a figure, and examples. In addition, Plett [40] offers an overview of several
figures. Different online resources like the Silva Rhetoricae [41] and the RhetFig database5 with
its origins in the RhetFig project [14] are a great collection of definitions and examples. The
Silva Rhetoricae contains 433 rhetorical figures and provides a definition, related figures, and
examples of the usage for most of the figures. The RhetFig database contains 1489 figures and
their definitions, synonyms, etymology, linguistic domain, and examples. Most of the definitions
are rooted in the Silva Rhetoricae.
We also rely on existing ontologies from the domain of rhetorical figures. For the Serbian
RetFig ontology that contains 98 figures, the authors retrieved information by searching novels,
poems, and journal texts to find examples of the figures in the Serbian language. However, they
do not state how they found the figures in the first step or resources for the Serbian definitions.
The authors of the GRhOOT ontology indicate that they used a variety of German resources
such as Lausberg’s Handbook [38], and literature from Plett [42], besides others. In our ESTHER
ontology, we want to focus only on the most common figures for now. Therefore, we rely on
the 110 figures from the GRhOOT ontology and searched for English equivalents. As ontology
development is an ongoing process, we want to add as many figures as possible to the ontology,
to make resources like the Silva Rhetoricae or the RhetFig machine-readable.
As tool for modeling, we use the Protégé ontology modeling framework to build the ontology
in combination with a text editor, as Pease recommends [25]. To validate the competency
questions, we will use SPARQL queries, similar to the validation of other ontologies in the same
domain [8, 9, 16]. The tools used for the validation and evaluation of the ontology are presented
in Section 3.5.
3.3. Purpose, Scope, Users, and Competency Questions
The purpose of the ontology is to create a formal machine-readable model of the most common
rhetorical figures in the English language. The scope of our ontology are the most known
figures but the ultimate goal is to include as many figures as possible. The ontology can then
assist its users in determining the proper name for a figure in the process of annotation. In
5
http://rhetfig.appspot.com/
Figure 1: How the ontology helps with overcoming the lack of annotated datasets.
addition, it is possible to derive rules from the formal model that can be used for rule-based
detection techniques or the generation of rhetorical figures.
Users of the ontology are people with linguistic or computational background, and people
without any linguistic background. For those, we want to make the ontology accessible via
a terminal-based tool called FyF! for determining figures (Section 4). Figure 1 illustrates the
workflow of our vision of how the ontology can help annotate datasets, thus overcoming the
problem of small datasets of rhetorical figures, and therefore enabling the training of classifiers
or the fine-tuning of large language models (LLM). The process starts with textual data that
could be political speeches or text from social media or messenger platforms. Together with the
developed tool that is based on the ESTHER ontology, human annotators can reliably identify
rhetorical figures in those texts, thus creating an annotated dataset. This dataset is then used
to train a machine learning classifier or to train LLMs. With the help of those algorithms,
hate speech, biased or populistic opinions or persuasive arguments can be better identified
and revealed. This can be important in a context to warn users to read certain texts more
attentively as it contains bias or persuasion methods. However, we want to highlight that the
ontology is not only built to train a classifier. It also serves as a source of carefully hand-crafted
knowledge about rhetorical figures and the first machine-readable format of them. Furthermore,
the ontology reveals dependencies and similarities between different figures that can help
in their future detection. For example, detection mechanisms for symploke can be used for
anaphoras, as every symploke has also features of an anaphora.
We formulated competency questions as examples of what the ontology should be able to
deliver in the end. Furthermore, we use them for consistency and sanity checks. For example,
question three is expected to deliver the same results as question four as it is a negation of all
other elements.
Q1: Which rhetorical figures have their defining element in a word?
Q2: In which rhetorical figures is a letter omitted?
Q3: What are figures that are neither figures of construction, nor figures of speech, nor figures
of thought?
Q4: Which figures belong to the rhetorical group of tropes? (Note: same output as Q3 expected)
Q5: What is an example sentence for the figure alliteration?
Q6: What is the definition of the figure parallelism?
Q7: Which figures of speech or figures of thought have their defining element in the beginning?
Q8: Which figures repeat an element in the same form?
Q9: In which figures is a word in the same form repeated?
Q10: Which figures are the opposite of other figures?
3.4. Formalization of the Model
In ontology modeling, reusing parts from existing ontologies is considered good practice [20, 22].
Therefore, we align with the RetFig and GRhOOT ontologies to guarantee cross-language
compatibility between Serbian, German, and English. Those ontologies suit our needs as they
also model rhetorical figures. In the following, we describe the structure of the ESTHER ontology
(Section 3.4.1) and the integration of hierarchical relations in Section 3.4.2.
3.4.1. Structure
We reuse the structure of the RetFig and GRhOOT ontology (which is actually based on RetFig).
The linguistic and rhetorical entities are modeled as concepts/classes whereas the rhetorical
figure itself is modeled as individual. We kept this architecture to guarantee interoperability
between the English, Serbian, and German ontology. This means that applications like the
FyF!-tool (see Section 4) can be easily adapted also to search the RetFig and GRhOOT ontologies.
However, we are aware that there is more than one solution how to model such an ontology [24].
An ontology where the figures are modeled as classes and the example sentences as individuals
would have also been possible, but we wanted to focus more on the aspect of interoperability.
We remove naming inconsistencies from RetFig and GRhOOT by strictly using lower Camel-
Case; a naming convention where words are joined together with a capitalized letter of each
word, but the first letter is lowercase (e.g., isRhetoricalFigure, isOmitted).6 As the terminology
in the domain of rhetorical figures is quite diverse and some names originate from Greek, Latin,
or are vernacular [43], we show other spellings, synonyms, and names in other languages such
as German, Serbian, or Latin in the seeAlso property, also indicating the language. Fig. 2 shows
an example of the entity graph in Protégé for the figure “isocolon”, illustrating the connection
between the classes and properties. The additional relations of the implied figure parallelism
are left out for clarity.
Like in RetFig and GRhOOT, we model the textual definition of the figures in natural language
as rdfs:comment , a property that provides a human-readable description of a resource [44].
Also, we use the self-defined data property called isExample with the RDF-datatype string to
provide at least one example sentence for each figure. In addition to previous ontologies in this
domain, we introduce hierarchical relations that are presented in the following Section 3.4.2.
3.4.2. Hierarchical Relations
Harris and DiMarco [1] mention “compound” figures. These are figures that are produced by
the combination of other figures, e.g., a symploce (aka symploke) is the combination of an
epanaphora (aka anaphora) and epistrophe (aka epiphora). In addition, hierarchical relations
6
RetFig and GRhOOT use camel case (but switch between first letter lower- and uppercase) and hyphens to connect
words.
Figure 2: Entity graph in Protégé for the figure isocolon.
can exist such that some figures are a more specific form of other figures. Also, the combination
of different figures yields the presence of other figures, as described by [15]. We tried to identify
those relations and formally modeled them as the object property implies . The relation is
similar to a subclass relation. However, as figures are modeled as individuals in both the Serbian
RetFig and the German GRhOOT, we wanted to maintain the same structure to guarantee
interoperability. That is the reason why the new property implies was introduced. The
implication ∀𝑅𝐹𝐴 ⊑ 𝑅𝐹𝐵 expresses that every figure 𝑅𝐹𝐴 also contains figure 𝑅𝐹𝐵 , as the figure
𝑅𝐹𝐴 includes figure 𝑅𝐹𝐵 . For example, each symploke is also an anaphora, while all figures with
repetitions of the same form are a ploke (a figure of repetition without further constraints on
the position):
symploke ⊑ anaphora and anaphora ⊑ ploke , respectively.
Compound figures are also modeled with the implies relation. If a figure implies multiple
other figures, it is a compound figure. For example, a symploke is the combination of anaphora
and epiphora:
symploke ⊑ anaphora ⊔ epiphora .
The figure climax is produced by a combination of following figures:
climax ⊑ gradatio ⊔ incrementum [15, 45].
We also modeled the relation of opposition between figures with the symmetric property
isOppositeFigure , e.g., anaphora isOppositeFigure epiphora, and vice versa respectively
(repetition at the beginning of consecutive sentences vs. repetition at the end of consecutive
sentences).
3.5. Evaluation and Validation
To evaluate ESTHER, we translate the ten competency questions from Section 3.3 into SPARQL
queries and execute them on the ontology to see if it behaves as expected. As mentioned, we
use some of the queries of the RetFig [8] and GRhOOT [9] ontology to make it comparable. The
results differ only for figures that either have different properties in the English language, that
do not exist, or are not yet modeled in ESTHER. Otherwise, the results fulfill our expectations.
For example, competency question Q4 delivers all figures that belong to the rhetorical group of
tropes. Its translation to SPARQL is shown below.
cq4 = """
SELECT DISTINCT ?Figure
WHERE {
?Figure rdf:type esther:RhetoricalFigure ;
esther:isRhetoricalGroup esther:Trope .
}
"""
Unfortunately, the oQual validator could not be found online. OnToology is a holistic system,
but its usage requires additional effort with steps that are prone to failure, e.g., granting access to
the GitHub repository. The OOPS! scanner is easy to use as users can provide a link or directly
input the ontology to a text field on the website. It categorizes the errors into three levels
of severity. The validator found only two minor and one medium issue within our ontology.
The medium issue is that OOPS! suggests some classes or properties to be declared disjoint.
According to the explanation of Noy et al. [24], we do not consider this issue, as e.g., an operation
for a rhetorical figure can be both adding and omitting an element. The consistency of our
ontology was verified by the HermiT reasoner version 1.4.3 [46].
3.6. Documentation and Maintenance
The last step is maintaining the ontology, which includes changes and updates as ontology
development is an “iterative process” [24]. We want to iteratively extend the ontology with
further rhetorical figures, and add examples to the existing figures. For code management, we
made the ontology available in a version control system [25], namely GitHub,7 which allows
tracking and documenting all changes. This repository contains the LODE documentation, the
OWL file of the ontology, the competency questions and the code of the FyF!-tool.
4. FyF: Find your Figure!
Identifying rhetorical figures e.g., in social media posts or political discussions, can help deter-
mine the intended meaning or to spot hidden notions. The ESTHER ontology can be used to
support human annotators in determining the right name of a rhetorical figure and revealing its
functions based on its properties. For example, a human can easily spot intentional repetitions
of words or more complex structures, e.g., the semantic repetition of a word in a different
form. However, it is difficult to name those figures. We present the ongoing work on our
terminal-based tool written in Python. The tool asks the users to enter the characteristics they
recognize in a text in which they suspect rhetorical figures. A SPARQL query is then built
automatically that queries the ESTHER ontology for figures that fulfill those properties. If users
are unsure, they can enter a “?” as blank property. An exemplary procedure is shown in Fig. 3
in Appendix A. If there is a match, the names of the figures, their definitions, and examples are
shown to the users. If no figure matches their description, the users have the possibility to enter
7
https://github.com/kuehnram/ESTHER-Ontology
comma-separated keywords as free text. We set up a small experiment to prove that the tool
facilitates the annotation process for persons without any linguistic background. Our hypothesis
is that a person using this tool is more likely to find the correct name for the rhetorical figure.
We presented five sentences to three persons without any linguistic background, but with a
proficient level of English. One person is allowed to use the FyF!-tool, one person uses a regular
search engine on the Internet, and the other one uses ChatGPT [47], a large language model
that provides an answer in the form of a conversation. The following five sentences with figures
from all four linguistic categories were used (figure of speech: Sentence 1 and 5, trope: Sentence
4, figure of thought: Sentence 3, figure of construction/scheme: Sentence 2).
1. When there is talk of hatred, let us stand up and talk against it. When there is talk of
violence, let us stand up and talk against it.
2. ’xactly!
3. The dog is black, the cat is white.
4. Boom! Meow! Woof!
5. When I was a child, I liked to watch TV. When I was a child, my mother loved me. when
I was a child, I liked to watch TV. When I went to school, my mother went to work.
Table 1
Comparison of the annotation results. Green/means correct, red/× indicates false, and orange/∼ is
not entirely correct.
Sentence Correct Figure ChaptGPT FyF! Search Engine
1 Symploke Anaphora ∼ Symploke Symploke
2 Aphaeresis Exclamatory Emphasis × Aphaeresis Aphaeresis
3 Parallelism Parallelism Irony (??) × Antithesis ∼
4 Onomatopoeia Onomatopoeia Ellipsis ∼ Onomatopoeia
5 Anaphora Parallelism ∼ Anaphora Anaphora
Table 1 shows the results of the experiment. The cells in red with a ×-symbol show where the
answer was wrong. A green cell with a shows a correct answer. Orange cells with ∼ show
answers that are at least partly correct. For example, annotating a symploke with “anaphora”
is partly right, as symploke is a compound figure consisting of both anaphora and epiphora.
The experiment confirmed our hypothesis. It showed that ChatGPT is powerful and fast for
annotation, but does not spot compound figures and figures that are more salient for humans
(cf. Sentence 5, where the dominant figure is anaphora, and parallelism is not so salient). Using
the tool is slower than ChatGPT, but more precise. Still, it is faster than using a search engine
(around 13 min. vs. around 20 min.). Using the tool also helps with avoiding spelling mistakes
or using different spellings (cf. Sentence 2).
We are aware of the limitations in the functionality of the terminal-based tool, as it is
still ongoing work. From the remarks of the FyF!-user, we found out that it is more difficult
to detect tropes. Furthermore, we received input on how to improve the tool in the future.
Nevertheless, we have shown that the ontology is helpful for annotators to determine rhetorical
figures without extensive training. Researchers will benefit from more annotated datasets as
they can be used for the training of algorithms or to improve language models.
5. Conclusion
We modeled a formal domain ontology of the most common rhetorical figures in the English
language. We described our methodology, which is based on other approaches. Within this pro-
cess, we aligned with the existing Serbian RetFig and German GRhOOT ontologies for rhetorical
figures. In addition to resolving inconsistencies, we added hierarchical relationships between
figures and defined compound figures. The ontology was evaluated to ensure consistency and
sanity. We provided an overview of available ontology validation and documentation tools
and used them for our ontology. The usage scenarios of this ontology are manifold. It serves
as a knowledge base but it can also assist humans without prior knowledge in the process of
annotating rhetorical figures. We proved this by developing a terminal-based annotation tool
that helped to find the right names for figures. Furthermore, rules can be derived from the
ontology for either detecting or generating examples of rhetorical figures. This is important as
large annotated datasets lack in this domain.
Future work will include the extension of the ontology by modeling additional figures. We
want to extend the terminal-based tool and build a graphical user interface for it, improving
the application’s usability. We want to distribute it and gather as many annotated examples
as possible. Detecting rhetorical figures and generating rules based on the ontology is also
considered future work. With more annotated data, models can be trained, which will then
increase the efficiency and precision of various NLP tasks.
Acknowledgments
The project on which this report is based was funded by the German Federal Ministry of
Education and Research (BMBF) under the funding code 01|S20049. The author is responsible
for the content of this publication.
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A. Find your Figure User Interfaces
Fig. 3 shows how users can interact with the terminal-based tool to find the names of rhetorical
figures, based on certain properties.
Figure 3: User specifies properties (at the end of the line in green).
B. Glossary
alliteration Repetition of same sound of nearby words/within a group of words. Example:
“Mail. Message. More” (Advertisement). 6
anadiplosis Anadiplosis is the repetition of a word/phrase of the end of a sentence at the
beginning of the next sentence. Example: “Strength through purity, purity through faith.”
(Chancellor Adam Susan, V for Vendetta). 15
anaphora Phrase/word repetition at the beginning of multiple sentences [39]. Example: “I
have a dream that one day […]. I have a dream […]” (Martin Luther King). 3, 6–8, 15
climax Generally, the arrangement of words, phrases, or clauses in an order of increasing
importance, often in parallel structure.[41]. Example: “First we lost the village, then the
city, then the world”. 3, 8
epiphora Repetition of a word/phrase at the end of multiple sentences [39]. Example: “My
name is Bond. James Bond.” (James Bond). 7, 8, 15
gradatio A Gradatio is a series whose members overlap but need not possess the same qual-
ity[48]. Gradatio is a progressive anadiplosis [38]. Example: “He is my friend, my angel,
my god” (Friedrich Schiller: Die Raeuber). 3, 8
incrementum A series of words in the same domain where subsequent words mark an increase
on a semantic scale [15]. Example: “Thus a proud man is called Lucifer, a drunkard a
swine, an angry man mad”. 3, 8
isocolon A series of similarly structured elements having the same length. A kind of parallelism
[41]. Example: “No pain, no gain”. 8
metaphor A metaphor compares two things from a different domain. It can convey literal,
emotional, or psychological truths [39]. Example: “Flood of refugees”, “Heart of gold”. 1
parallelism Parallelism uses the same structure for multiple parts of a sentence to link them.
It makes a text easier to read and keeps long lists understandable. Often, parallelism
refers to the syntax of the elements, but not necessarily [39]. Example: “In the name of
the father, in the name of the son, and in the name of the holy ghost”. 6
ploke Perfect lexical repetition of a word/phrase. 3, 8
symploke Combination of anaphora and epiphora. Example: “All through industry, all for
industry” (Claude-Henry des Saint-Simon). 6–8