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 realworld 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.
Conceptual modeling scholarship has traditionally built its theoretical foundations from
ontology, a branch of philosophy [
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 [
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 [
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 [
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 [
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 [
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 [
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 [
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.