=Paper=
{{Paper
|id=Vol-3849/forum9
|storemode=property
|title=The Ontology of Fields as a Foundation for Conceptual Modeling
|pdfUrl=https://ceur-ws.org/Vol-3849/forum9.pdf
|volume=Vol-3849
|authors=Arturo Castellanos,John W. Beard,Binny Samuel,Keng Siau,Carson Woo,Veda C. Storey,Roman Lukyanenko,Roger H.L. Chiang,Debra Vandermeer
|dblpUrl=https://dblp.org/rec/conf/er/CastellanosBSSW24
}}
==The Ontology of Fields as a Foundation for Conceptual Modeling==
https://ceur-ws.org/Vol-3849/forum9.pdf
The Ontology of Fields as a Foundation for Conceptual
Modeling
Arturo Castellanos1*, Roman Lukyanenko2, Binny M. Samuel3, Jon W. Beard4, Hsiang-Li
(Roger) Chiang3, Keng Siau5, Veda C. Storey6, Debra VanderMeer7, Carson Woo8
¹ William & Mary
2
University of Virginia, USA
3
University of Cincinnati, USA
4
Iowa State University, USA
5
Singapore Management University, Singapore
6
Georgia State University, USA
7
Florida International University, USA
8
University of British Columbia, Canada
1. Extended Abstract
As any discipline matures, its research communities reflect on the epistemological foundations that
support their research and discuss how the discipline can, and should, progress. This type of
introspection is typically observed in the natural sciences, where researchers question the nature of
their fields, the standards of rigor applied, and the impact of their work. As researchers in conceptual
modeling, we should continually reflect on, and discuss, the theories and practice of conceptual
modeling to ensure its ongoing relevance and consistency with the advancements in information
technologies.
Today there are novel advances in the current information technology (IT) landscape that
challenge our theories of conceptual modeling. Consider platforms such as Google and TikTok. These
and many other modern IT systems exhibit much less defined boundaries between digital and real-
world interactions than the traditional standalone systems such as an installed AutoCAD software,
or even an ERP system which operated within defined process limits. This boundary-less nature of
many systems makes it very difficult to understand, predict and control where their influence starts
and ends. These systems often extend beyond their primary functions – search and social media -
into areas such as advertising, e-commerce, and artificial intelligence, creating interconnected
ecosystems. These platforms are also integrated with in-house and third-party applications and
websites, blurring the lines of their operational scope. For example, Google has integrated search
into another one of its platforms known as Google Home (devices and services for a smart home).
Similarly, TikTok relies on CapCut, an external video editing app to ensure their videos have state
of the art effects. Last, these platforms user bases span global demographics, connecting with various
cultural, social, and economic spheres, complicating the identification of distinct boundaries.
Accurately modeling these systems and shaping the progression of their designs and impact are
becoming increasingly complex and difficult.
ER2024: Companion Proceedings of the 43rd International Conference on Conceptual Modeling: ER Forum, Special Topics,
Posters and Demos, October 28-31, 2024, Pittsburgh, Pennsylvania, USA
∗
Corresponding author.
Arturo Castellanos (A. Castellanos); romanl@virginia.edu (R. Lukyanenko); samuelby@ucmail.uc.edu (B. M. Samuel);
jwbeard@iastate.edu (J. W. Beard); roger.chiang@uc.edu (H.-L. (R.) Chiang); klsiau@smu.edu.sg (K. Siau);
vstorey@gsu.edu (V. C. Storey); vanderd@fiu.edu (D. VanderMeer); Carson.Woo@sauder.ubc.ca (C. Woo)
0000-0002-7477-7379 (A. Castellanos); 0000-0001-8125-5918 (R. Lukyanenko); 0000-0002-3223-4616 (B. M. Samuel);
0009-0005-5061-6211 (J. W. Beard); 0009-0007-3295-2355 (H.-L. (R.) Chiang); 0000-0001-8139-4467 (K. Siau); 0000-0002-
8735-1553 (V. C. Storey); 0000-0002-5930-6667 (D. VanderMeer); 0000-0001-9043-0052 (C. Woo)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Conceptual modeling scholarship has traditionally built its theoretical foundations from
ontology, a branch of philosophy [6, 17]. This includes a rich tradition of developing their own
ontologies and adopting and enhancing existing ontologies from other fields [6, 8, 18]. Two
ontologies developed by the conceptual modeling community include DOLCE [4] and The Unified
Foundational Ontology [7]. Prominent ontologies that have been imported and shaped conceptual
modeling include Bunge [2] and Searle [14]. However, none of these ontologies has offered us a
solution or way forward handle the landscape of IT described above.
Traditionally, conceptual modeling has followed the ontology of individual substances, which
suggests reality consists of independent substances, often described as objects, entities, individuals,
or things [1, 2, 9]. These substances possess properties or attributes and experience changes, leading
to events and processes. This perspective is best represented by Bunge’s ontology, which asserts that
the world is composed of "things" that are substantial individuals [2]. However, this perspective may
limit our ability to model and capture nuances represented in our world.
To continue aligning conceptual modeling with modern IT landscape, we consider an alternative
ontology. Although the traditional ontology of individual substances is useful in many situations, it
may encounter limitations when dealing with today’s IT. Notably, a similar challenge has been
identified in philosophy and the natural sciences. For example, advances in physics led to the
assertion that "there are no particles, only fields" [10:211; emphasis added]. This conclusion was
reached based on the accumulation of evidence from various scientific disciplines.
One novel direction for conceptual modeling would be to consider an ontology of fields. While
this ontology has been recognized in modern philosophy [13], it has not found its footing in
conceptual modeling yet. In consideration of this potential opportunity, we describe aspects of the
ontology of fields in order to consider it as a new foundational basis for conceptual modeling.
A unique aspect of the ontology of fields is the notion of a field. The concept of a field is central
in both modern science and philosophy, offering a valuable model for understanding reality. A field
is defined as any physical or conceptual entity that displays varying values across space and time,
driven by the oscillations that form and maintain these fields. By embracing the scientific notion of
a field, we can conceptualize a wide range of aspects of existence.
Fields provide powerful flexibility during analysis. Fields can be analyzed by studying properties
at specific points within them, and also considered more broadly as an entity with global or even
emergent properties in and of itself. For instance, a particular point in the sky modeled as a field
might have attributes like its chemical composition (e.g., concentration of oxygen or helium
molecules). However, when considered collectively, other properties of interest emerge such as air
temperature and humidity. These properties might also be considered compared to other points in
the sky. That is fields have multiple properties which can be represented in a hyperplane -- a
conceptual space with numerous dimensions reflecting different types of properties. Variations in
these properties appear as patterns, such as peaks, plateaus, and valleys, where peaks typically
signify areas with high concentrations of mass, energy, or charge. Further, these latter properties of
the sky can be considered against another field such as a mountain. By integrating the concept of
fields from physics and geography, the ontology of fields provides new perspectives for conceptual
modeling which we consider next.
First, if conceptual modeling were to include the ontology of fields, we would now have a
conceptual basis to consider different levels of abstractions for modeling that are layered together.
Some conceptual models, such as data flow diagrams [15], already provide this mechanism, albeit
absent of any theoretical basis. Similarly, use cases often encourage modeling as cloud, kite, sea, fish,
and clam levels. Further, different types of conceptual models now have a basis from which to be
explored as multiple models are often used in practice [3]. This idea of abstraction also has efficacy
for data analytics. As data is pooled together, we now have a mechanism for how its levels or layers
of abstraction might impact the results of prediction models.
Second, the notion of shifting modeling perspectives from individual objects to fields, may result
in new conceptual modeling constructs. It could be that some properties depicted in conceptual
models are only relevant as we move from the individual object focus to the focus on fields. Similarly,
when one considers conceptual models at the level of the objects themselves, properties about fields
may become less pertinent. Finding the right level of abstraction to present a conceptual model is
still an open debate in the field. However, one approach might be to consider field properties for
conceptual models that can be represented in the abstraction of individual objects much like, for
example, a derived attribute in an entity relationship (ER) diagram. Another example of emergent
properties found in conceptual models is the idea of attributes on relationships in the ER diagram.
While some research has proscribed this on the basis of lack of ontological clarity [5], with the
ontology of fields we can now permit emergent properties, such as attributes on ER diagram
relationship constructs, as they would be ontological clear with respect to the ontology of fields.
Third, novel conceptual modeling approaches can be undertaken with the ontology of fields. From
a cognitive perspective, it has long been recognized there different classification theories can be used
to create conceptual models [12]. One work, initiated by Lakoff [11], indicates how humans can
classify things in seemingly non-intuitive ways. From a conceptual modeling perspective, similar
objects have traditionally been modeled together. However, with the ontology of fields, we open up
an entirely new possibility. For example, in ER diagrams, entities that have different properties can
be collectively modeled together in the same entity type when we use the ontology of fields. Just as
different individual objects can be contained in a field, now there is not a push to maintain uniformity
in entities of entity types of the ER diagram. Indeed, novel conceptual modeling approaches are
starting to emerge that recognize the power of this flexibility [12]. In practice, we are observing more
heterogeneity and flexibility of data collected and collocated in NoSQL and data lake data storage
technologies.
Fourth, it is recognized that fields can change over time. At one minute, a mountain might exist.
However, after a sudden and violent earthquake occurs, that very mountain might be leveled to
rubble. Unfortunately, conceptual models themselves have remained somewhat static and stagnant
after their creation, often leaving those who consult them wonder if they are current [19]. The
ontology of fields explicitly encourages conceptual modeling to consider how some aspects of a
model might change over time. As the pace of IT and systems are changing at unprecedented rates,
we need a better way to model dynamic and complex systems. This includes, as we described earlier,
how to integrate platforms together with ill-defined boundaries, even as the services offered in the
platform might continue to evolve and change themselves. This goes beyond just modeling of the
architecture of the systems, but instead the data that might be collected through the use of various
platforms.
We are excited to continue to explore how the ontology of fields can lead to new ways of thinking
about and designing conceptual models and their subsequent systems. The ontology of fields
introduces new vocabulary and conceptual tools that correspond with modern IT. We also posit that
both current and emerging technologies will benefit from the ontology of fields, as it offers a
comprehensive understanding of dynamics and interactions. We intend the ontology of fields to
supplement the existing ontologies while opening up new exciting possibilities and approaches to
conceptual modeling [16].
Applying field theory to conceptual modeling encourages a holistic view, considering the entire
ecosystem of interactions and dynamics rather than primarily isolated components. Modeling fields
with characteristics such as peaks (analogous to mountains) and valleys could help us better
represent and understand the dynamic nature of IT today. As many modern applications are
inherently field-like, with ill-defined boundaries, viewing these IT through the lens of a field
ontology acknowledges their interconnected nature. The ontology of fields is intended to
additionally address the need for flexible representations.
Although still in its early development stages, the ontology of fields could potentially become a
foundation for significant conceptual modeling research. Future research will need to expand and
apply the ontology of fields to demonstrate its feasibility and effectiveness.
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