JOWO 2024 Tutorials

Events, Processes, and their Descriptions

Nicola Guarino

In this tutorial I will introduce a novel ontological theory of events published in a recent paper on Applied Ontology, whose central tenet is the Aristotelian distinction between the object that changes and the actual subject of change, which is what we call an individual quality. While in the Kimian tradition events are individuated by a triple <o, P, t>, where o is an object, P a property, and t an interval of time, for us the simplest events are qualitative changes, individuated by a triple <o, q, t>, where q is an individual quality inhering in o or in one of its parts. Detaching the individuation of events from the property they exemplify results in a fine-grained theory that keeps metaphysics and semantics clearly separate, and lies between the multiplicative and the unitarian approaches.

After presenting this ontological account of simple events as qualitative changes, I will focus on the event descriptions occurring in natural language, which usually refer to complex, cognitively relevant clusters of co-occurring simple events, which exhibit a synchronic structure depending on the way they are described. Contra Bennett, who famously argued that the semantics of event names ultimately depends on “local context and unprincipled intuitions”, I will show how the lexicon provides systematic principles for individuating such clusters and classifying them into kinds. In particular, this allows to clarify the semantics of verbal modifiers.

Finally, I will briefly discuss the difference between events and processes, which according to a very recent paper are conceived as variable embodiments of events, showing ho how these cognitive and linguistic mechanisms governing the way we describe events also work in the case of processes.

DOLCE in OWL: A tutorial with case studies in industrial engineering

Emilio Sanfilippo and Walter Terkaj

Foundational ontologies play a prominent role in ontology-based conceptual and data modeling by offering conceptually and logically well-founded top-level architectures that can be extended to meet specific application needs. Due to their complexity, it is common for both novices and experts in the field to seek theoretical knowledge about them and practical competencies regarding their use. The core objective of the tutorial is to balance these dimensions, introducing participants to various aspects of the theoretical background and practical use of foundational ontologies. In particular, the tutorial will focus on the foundational ontology DOLCE – Descriptive Ontology for Linguistic and Cognitive Engineering and its recent release in the Web Ontology Language (OWL). Attendees will gain introductory knowledge about DOLCE, as well as hands-on experience with its OWL release consisting of two modules: DOLCEbasic and DOLCEnaryRel. The first module includes the taxonomy of classes along with OWL axioms to characterize the extension of the classes. The second module, built upon the basic module, incorporates the reification of n-ary relationships to maintain the ability to represent temporalized relations. This modular architecture aims to facilitate the OWL extension of DOLCE for specific research and application purposes. Throughout the tutorial, we will introduce modeling examples from the field of industrial engineering, ontology patterns to represent them, as well as tools for manipulating datasets formalized according to DOLCE OWL using the SPARQL query/update language.

GitHub Applied Ontology Lab:
https://github.com/appliedontolab/DOLCE

Web Application OntoGuiWeb:
https://difactory.github.io/DF/tools/OGW_DOLCE.html

References:

*Porello, Vieu, Terkaj, Borgo, Compagno, Sanfilippo. DOLCE in OWL: The Core Theory,  presented at FOUST 2024
*Borgo, S., Ferrario, R., Gangemi, A., Guarino, N., Masolo, C., Porello, D., Sanfilippo, E.M. & Vieu, L. (2022). DOLCE: A descriptive ontology for linguistic and cognitive engineering. Applied ontology, 17(1), 45-69
*Terkaj, W., Borgo, S., & Sanfilippo, E. M. (2022). Ontology for industrial engineering: A DOLCE compliant approach. Formal Ontologies Meet Industry, 1-13
*Compagno, F., Borgo, S., Sanfilippo, E.M., and Terkaj, W. (2023). Manufacturing Resources, Capabilities, and Engineering Functions: Towards an Ontology-based Integration. FOIS 2023

Generating ontology conceptualization and pattern libraries with Chowlk

María Poveda-Villalón, Raúl García-Castro, Sergio Carulli-Pérez

Ontology conceptualization is a key activity as it drives the final implementation. Usually, developers generate graphical representations to carry out this activity as it is more convenient to provide an overall idea of the model, and it is a powerful tool to communicate with domain experts. While the conceptualization might be independent of the implementation language, it is advisable to use a notation as close as possible to the ontology implementation language to avoid ambiguity and reduce effort during the implementation. To this end, the Chowlk framework provides a UML-based notation (published at VOILA23) and a converter (published at ESWC22) in order to conceptualize and implement OWL ontologies graphically. This tutorial’s learning outcomes are: 1) to know the Chowlk framework resources available; 2) to know how to use the Chowlk notation to represent OWL ontologies and the converter to generate the ontology OWL code; and 3) to learn how to use draw.io to generate their own patterns libraries. The tutorial will be organized in 2 sessions, the first one dedicated to explain the resources available and how to use them (learning outcomes 1 and 2) and the second dedicated to the creation of ontology pattern libraries (learning outcome 3).

Knowledge Engineering for Hybrid Intelligence

Ilaria Tiddi, Victor de Boer, Stefan Schlobach

Hybrid Intelligence (HI) is a rapidly growing field aiming at creating collaborative systems where humans and intelligent machines synergetically cooperate in mixed teams towards shared goals. A clear characterization of the tasks and knowledge exchanged by the agents in HI applications is still missing, hampering both standardization and reuse when designing new HI systems. Knowledge Engineering (KE) methods have been used to solve such issue through the formalization of tasks and roles in knowledge-intensive processes, formerly often for Expert Systems. In this tutorial we will introduce how KE methods can be applied to HI scenarios, and specifically how common, reusable elements such as knowledge roles, tasks and subtasks can be identified in contexts where symbolic, subsymbolic and human-in-the-loop components are involved. In this tutorial we will first introduce the well-known CommonKADS methodology, and recent extensions to make it usable to hybrid scenarios. In a hands-on part, we will then use this methodology to analyze HI projects and identify common tasks.

More details about the tutorial can be found here.