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Research

I have published circa 400 publications. More information about them can be found in

Below you will find some of my main research topics with a sample of papers associated to these areas, including:

 

  • Explanation, AI, and Ethics

  • Conceptual Modeling and OntoUML

  • Ontology and the Unified Foundational Ontology (UFO)

  • Foundations of Events and Processes

  • Foundations of Modeling

  • Legal Modeling and Reasoning

  • Semantics and Modeling in Risk and Security

  • Scientific Data Management and FAIR

  • Ontology Engineering and Knowledge Representation on the Web

 

At the bottom of the page, you will find my Research Vision Statement.

 

Explanation, AI, Ethics

For my view on the indissoluble relation between explanation, semantics and ontology, a method for explaining symbolic descriptions called "Ontological Unpacking" and how it relates Semantic Interoperability and to explainable AI, please see:

Guizzardi, G., Guarino, N., Explanation, Semantics, and Ontology, Data & Knowledge Engineering, Elsevier, 2024.

 

For the multiple roles of ontologies in AI explanation:

Confalonieri, R., Guizzardi, G., On the multiple roles of ontologies in explanations for neuro-symbolic AI, Neurosymbolic AI Journal, IOS Press, 2024.

For applications of "Ontological Unpacking" also with some empirical validation, please see:

​Bernasconi, A., Guizzardi, G., Pastor, O., Storey, V., Semantic interoperability: ontological unpacking of a viral conceptual model, BMC Bioinformatics, Vol. 23/11, Springer Nature, 2022.

García S., A., Bernasconi, A., Guizzardi, G., Pastor, O., Storey, V., Panach, I., Assessing the value of ontologically unpacking a conceptual model for human genomics, Information Systems, Vol. 118, Elsevier, 2023.

Moreover, in the following paper, we discuss the need for a theory-driven (in combination to a Data-Driven) approach to AI with connections to explanation and ethics:

Guizzardi, G., Pastor, O., Storey, V., Thinking Fast and Slow in Software Engineering, IEEE Software, IEEE Press, 2023.

and talking about Ethics and its relation to AI and to Sustainability:

Guizzardi, R., Amaral, G., Guizzardi, G., Mylopoulos, J., An Ontology-Based Approach to Engineering Ethicality Requirements, Software and Systems Modeling, 1-27, Springer, 2023.

Fumagalli, M., Ferrario, R., Guizzardi, G., A Teleological Approach to Information Systems Design, Minds & Machines, Springer, 2024.

Bork, D., David, I., España, S., Guizzardi, G., Proper, H.A., Reinhartz-Berger, The Role of Modeling in the Analysis and Design of Sustainable Systems: A Panel Report, Communications of the Association for Information Systems, Vol, 54. AIS, 2024.

and more on the interplay between theory-driven and data-driven AI in practice:

Ali, S.J., Bork, D., Guizzardi, G., Enabling Representation Learning in Ontology-Driven Conceptual Modeling using Graph Neural Networks, 35th International Conference on Advanced Information Systems Engineering (CAISE 2023).

Fumagalli, M. Sales, T.P., Baião, F., Guizzardi, G. Conceptual model visual simulation and the inductive learning of missing domain constraints, Data & Knowledge Engineering, Elsevier, 2022.

Amaral, G., Baião, F., Guizzardi, G., Foundational ontologies, ontology‐driven conceptual modeling, and their multiple benefits to data mining, WIREs Data Mining and Knowledge Discovery, Wiley, 2021.

 

Conceptual Modeling and OntoUML

If you are interested in the foundations of conceptual modeling and, in particular, the foundations grounding the design of the language OntoUML, the most complete theoretical reference is still the monograph "Ontological Foundations for Structural Conceptual Models" below.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

For more recent papers on the ontologically well-founded representation of types and taxonomic structures, events, relations, and on representing higher-order types and structures in the language, please see:

Guizzardi, G., Fonseca, C., Almeida, J.P., Sales, T.P., Benevides, A.B., Porello, D., Types and Taxonomic Structures in Conceptual Modeling: A Novel Ontological Theory and Engineering Support, Data & Knowledge Engineering, Elsevier, 2021.

Almeida, J.P., Falbo, R., Guizzardi, G., Events as Entities in Ontology-Driven Conceptual Modeling, 38th International Conference on Conceptual Modeling (ER 2019), Salvador, Brazil.

Guizzardi, G., Guarino, G., Almeida, J.P., Ontological Considerations About the Representation of Events and Endurants in Business Models, 14th International Conference on Business Process Management (BPM 2016), Brazil.

 

Fonseca, C.M., Porello, D., Guizzardi, G., Almeida, J.P.A., Guarino, G., Relations in Ontology-Driven Conceptual Modeling, 38th International Conference on Conceptual Modeling (ER 2019), Salvador, Brazil.

Fonseca, C.M., Guizzardi, G., Almeida, J.P.A., Sales, T.P., Porello, D., Incorporating Types of Types in Ontology-Driven Conceptual Modeling, 41st International Conference on Conceptual Modeling (ER 2022), Hyderabad, India, Springer.

For a quick primer on OntoUML, please see this paper. Alternatively, you can watch this 30' talk given at NASA JPL in February 2024 about the language and its ecosystem. Here you can also find a 3 years tutorial as well as entire courses in English and Portuguese.

 

Ontology and the Unified Foundational Ontology (UFO)

If you are interested in the Unified Foundational Ontology (UFO), please read the following papers:

Guizzardi, G., Benevides, A.B., Fonseca, C., Porello, D., Almeida, J.P., Sales, T.P., UFO: Unified Foundational Ontology, Applied Ontology, IOS Press, 2022.

Guizzardi, G., Wagner, G., Almeida, J.P., Guizzardi, R.S.S., Towards ontological foundations for conceptual modeling: the unified foundational ontology (UFO) story, Applied ontology 10 (3-4), 259-271, 2015.

or the specification of its Semantic Web-oriented lightweight version termed gUFO (gentle UFO).

Still related to gUFO, a nice primer on the ontology is this keynote video by Prof. João Paulo Almeida (NEMO, Brazil).

If you are particularly interested in UFO-B, i.e., UFO's theory of events, please read the following paper:

Guizzardi, G., Wagner, G., Falbo, R.A., Guizzardi, R.S.S., Almeida, J.P., Towards ontological foundations for the conceptual modeling of events, 32nd International Conference on Conceptual Modeling (ER 2013), 327-341, Hong Kong, Springer.

or its extended version in

Benevides, A.B.. Bourguet, J.R., Guizzardi, G., Peñaloza, R., Almeida, J.P.A. Representing a reference foundational ontology of events in SROIQ, Applied Ontology 14 (3), 293-334

...and talking about Events and other Occurrences

I am very interested in the so-called category of perdurants or occurrences (events, processes).

Besides the UFO-B papers above and the papers on the modeling of events (see Conceptual Modeling and OntoUML), here is two more philosophical paper on the ontology of events and processes and how they differ, as well on the synchronic structure of events (i.e., the parts of event at a given point of time, as opposed to across time). Despite their philosophical nature, I think these papers have very interesting applications and implications to both linguistics and computer science (Conceptual Modeling, Process Science):

Guarino, N., Guizzardi, G., Processes as Variable Embodiments, Synthese, Springer, 2024.

Guarino, N., Baratella, R., Guizzardi, G., Events, their Names, and their Synchronic Structure, Applied Ontology, IOS Press, 2022. 

On how the interplay between Events, Logics and Image Schemas in Cognition, please see:

Hedblom, M., Kutz, O., Peñaloza, R., Guizzardi, G., Image schema combinations and complex events, Künstliche Intelligenz, Springer, 2019.

Foundations of Modeling

Besides the entire monograph "Ontological Foundations for Structure Conceptual Models" (see above), I have co-authored some recent papers on the topic of for domain modeling, including:

What is a Conceptual Model and how it relates to some other types of models:

Guarino, N., Guizzardi, G., Mylopoulos, J., On the Philosophical Foundations of Conceptual Models, 29th International Conference on Information Modelling and Knowledge Bases (EJC 2019), Finland, 2019.

Proper, E., Guizzardi, Understanding the Variety of Domain Models: Views, Programs, Animations, and Other Models, SN Computer Science, Springer, 2024.

As an attempt to dissipate some of the terminological confusion (that still exists), this is how I think Ontology, ontologies, Conceptual Models and Metamodels are related:

Guizzardi, G., On Ontology, ontologies, Conceptualizations, Modeling Languages, and (Meta)Models, Frontiers in Artificial Intelligence and Applications, IOS Press, Vol. 155, 2007.

 

Scientific Data Management and FAIR

Guizzardi, G., Ontology, Ontologies and the “I” of FAIR, Data Intelligence, MIT Press, 2020.

Schultes, E., Roos, M., Silva Santos, LOB, Guizzardi, G., Bouwman, J., Hankemeier, T., Baak, A., Mons, B., Fair digital twins for data-intensive research, Frontiers on Big Data, 2022.

Bernasconi, A., García S., A, Guizzardi, G., Silva Santos, L.O.B., Storey, V., Ontological representation of FAIR principles: A blueprint for FAIRer data sources, 35th International Conference on Advanced Information Systems Engineering (CAiSE), Spain, Springer, 2023.

Silva Santos, L.O.B., Sales, T.P., Fonseca, C.M., Guizzardi, G., Towards a Conceptual Model for the FAIR Digital Object Framework,

Frontiers in Artificial Intelligence and Applications, Vol. 377: Formal Ontology in Information Systems (FOIS 2023), Canada, IOS Press.

Sales, T.P. et al., A FAIR catalog of ontology-driven conceptual models, Data & Knowledge Engineering, Vol. 147, Elsevier, 2023.

Jacobson, A. et al., FAIR Principles: Interpretations and Implementation Considerations, Data Intelligence (2020) 2 (1-2): 10–29. MIT Press.

 

Legal Modeling and Reasoning

Griffo, C., Almeida, J.P., Guizzardi, G., Service contract modeling in enterprise architecture: An ontology-based approach, Information Systems, Elsevier, 2021.

Griffo, C., Almeida, J.P., Lima, J.O., Sales, T.P., Guizzardi, G., Legal powers, subjections, disabilities, and immunities: Ontological analysis and modeling patterns, Data & Knowledge Engineering, Elsevier, 2023.

Lima, J.O., Griffo, C., Almeida, J.A., Guizzardi, G., Aranha, M.I., Casting the Light of the Theory of Opposition onto Hohfeld’s Fundamental Legal Concepts, Legal Theory, Cambridge University Press, 2021.

 

Semantics and Modeling in Risk, Security and Resilience

Oliveira, I., Sales, T.P., Almeida, J.P., Baratella, R., Fumagalli, M., Guizzardi, G., Ontology-based security modeling in ArchiMate, Software & Systems Modeling, Springer, 2024.

Barcelos, P.P.F. et al., Ontological Foundations of Resilience, 43th International Conference on Conceptual Modeling (ER 2024), Pittsburgh, USA, Springer, 2024.

Oliveira, I., Engelberg., G., Barcelos, P.P.F., Sales, T.P., Fumagalli, M., Baratella, R., Klein, D., Guizzardi, G., Boosting D3FEND: Ontological analysis and recommendations, 13th International Conference on Formal Ontology in Information Systems (FOIS 2023), Canada.

Fumagalli, M. et al., On the Semantics of Risk Propagation, 16th International Conference on Research Challenges in Information Science, RCIS 2023, Guimarães, Portugal, Springer, 2024.

Salse, T.P., Baião, F., Guizzardi, G., Almeida, J.P.A., Guarino, N., Mylopoulos, J., The Common Ontology of Value and Risk, 37th International Conference on Conceptual Modeling (ER 2018), Xi'an, China, Springer.

Ontology Engineering and Knowledge Representation on the Web

Trojan, C., Vieira, R., Schmidt, D., Pease, A., Guizzardi, G., Foundational ontologies meet ontology matching: A survey, Semantic Web Journal, IOS Press, 2022.

Dadalto, A., Almeida, J.P., Fonseca, C.M., Guizzardi, G., Evidence of Large-Scale Conceptual Disarray in Multi-Level Taxonomies in Wikidata, Semantic Web Journal, IOS Press, 2024.

 

Research Vision Statement (or what my research program is about)

​​

Meaningful Computing = Value-Based Semantic Computing = Building Systems we can Understand and Trust

Let us start with some some (quite uncontroversial) premises:
 

Computers do one thing and one thing only: they manipulate symbols

Computers are Artifacts, i.e., they are built for a purpose. In particular, the symbols they manipulate are not arbitrary, they are representations of things that ultimately have meaning (both in the sense of semantics, as well as in the sense of significance) to people. In other words, these symbols represent things in some actual (or counterfactual) reality (e.g., people, products, relationships, events, etc) aimed at satisfying our human and social goals.

Connecting the Machine to People's Conception of Reality

So, a computer is a symbol manipulation automated artifact that serves as bridge between people's conceptions of reality.

 

For example, when you go to an online store, you are exchanging speech acts about real products and services that create real binding relations in the world. So, you (and the online store) are using a device to create and change social reality!

 

Now, on a very different example, think of a basic software that controls a robot, an airplane or a bridge. This system reads symbols from certain sensors and writes symbols in records of certain actuators. The former represent properties of things in the world (e.g., temperature, spatial location) and the latter means a certain type of behavior that it intends to enact to ultimately satisfy some (human) goal.

Why is this important?

Computer Science has always put a lot of emphasis on problems related to symbol manipulation. Don't get me wrong: finding scalable, efficient, secure ways of manipulating (including transmitting) symbols is absolutely fundamental!

However, it is equally fundamental that we understand how to precisely and accurately ascribe meaning (semantics) to these symbols.

 

In fact, this is becoming more and more fundamental because all software systems that we build nowadays are built by putting other software systems that have been created by different people, in different locations and at different times. In order to guarantee that these systems operate together (i.e, interoperate) in a safe way, we need to guarantee that the meaning of symbols in one system is preserved as it connects to symbols in other systems.

This is called the Semantic Interoperability problem and I believe is one of the most fundamental problems we need to solve as society. This is because all relevant questions we need to have answered in science, in organizations, in government can only be answered if we put together symbols that are created and manipulated in these autonomously developed silos.

 

Furthermore, we should understand that the value of (as well as risk posed by) these symbolic structures and the systems that manipulate them can only be assessed in terms of human values, i.e., the meaning (significance, purpose) that humans and their institutions attach to them.

How can we get there?
 

We need an engineering discipline that helps us to:

 

understand how people form their different conceptions about reality. In order to do that, we need to really understand what are even our most basic conceptions about what does it mean for something to be the thing it is (e.g., an object, a property, a relationship, an event,...).

 

understand how we can translate human values into symbolic representations, and how we can create and manipulate our inter-subjective/institutional digital reality with support of these representations

understand how we can create precise and efficient representations of these different notions such that they can be manipulated by people and computers


The first two problems cannot be addressed without the support of a discipline called Applied Ontology . Moreover, since we are interested in how the world interacts by human cognition, this discipline should incorporate state of the art theories from, besides analytical philosophy, linguistics and cognitive science. In additional, we need to fully understand notions such as value, risk, trust, power, commitment, etc. For that we need the support of disciplines such as Ethics, Economics and Legal Theory.


Regarding the latter problem, since we need to produce symbolic representations that are very precise so that we can translate to these blind symbol manipulation machines, we need the help of the discipline of Logics. However, we need more than that, we need to be sure that what these symbolic structures are "saying on our behalf" is what we intend them to say! (this is problem related to formal validation and cognitive tractability of representations).

 

Moreover, we need that to remain the case even when as a community we connect these independently constructed symbolic structures. In a nutshell, we need these symbolic structures to act as Fully-Transparent Meaning Contracts that tell the world precisely what is our worldview about a particular portion of reality. In order to achieve this, we need proper methodological and computational tools to engineer and manage these multiple structures.

 

This engineering discipline is focused on two senses of Meaning that are relevant to us, human beings: that of Semantics (and, hence, Semantic Computing) and that of Significance (and, hence, Value-Based Computing). That is why I call it MEANINGFUL Computing.

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