Daniele Porello

Repairing Socially Aggregated Ontologies Using Axiom Weakening

Ontologies represent principled, formalised descriptions of agents’ conceptualisations of a domain. For a community of agents, these descriptions may significantly differ. We propose an aggregative view of the integration of ontologies based on Judgement Aggregation (JA). Agents may vote on statements of the ontologies, and we aim at constructing a collective, integrated ontology, that reflects the individual conceptualisations as much as possible. As several results in JA show, many attractive and widely used aggregation procedures are prone to return inconsistent collective ontologies. We propose to solve the possible inconsistencies in the collective ontology by applying suitable weakenings of axioms that cause inconsistencies.

Two Approaches to Ontology Aggregation Based on Axiom Weakening

Axiom weakening is a novel technique that allows for fine-grained repair of inconsistent ontologies. In a multi-agent setting, integrating ontologies corresponding to multiple agents may lead to inconsistencies. Such inconsistencies can be resolved after the integrated ontology has been built, or their generation can be prevented during ontology generation. We implement and compare these two approaches. First, we study how to repair an inconsistent ontology resulting from a voting-based aggregation of views of heterogeneous agents. Second, we prevent the generation of inconsistencies by letting the agents engage in a turn-based rational protocol about the axioms to be added to the integrated ontology. We instantiate the two approaches using real-world ontologies and compare them by measuring the levels of satisfaction of the agents w.r.t. the ontology obtained by the two procedures.

Endurant Types in Ontology-Driven Conceptual Modeling: Towards OntoUML 2.0

For over a decade now, a community of researchers has contributed to the development of the Unified Foundational Ontology (UFO) - aimed at providing foundations for all major conceptual modeling constructs. This ontology has led to the development of an Ontology-Driven Conceptual Modeling language dubbed OntoUML, reflecting the ontological micro-theories comprising UFO. Over the years, UFO and OntoUML have been successfully employed in a number of academic, industrial and governmental settings to create conceptual models in a variety of different domains. These experiences have pointed out to opportunities of improvement not only to the language itself but also to its underlying theory. In this paper, we take the first step in that direction by revising the theory of types in UFO in response to empirical evidence. The new version of this theory shows that many of the meta-types present in OntoUML (differentiating Kinds, Roles, Phases, Mixins, etc.) should be considered not as restricted to Substantial types but instead should be applied to model Endurant Types in general, including Relator types, Quality types and Mode types. We also contribute a formal characterization of this fragment of the theory, which is then used to advance a metamodel for OntoUML 2.0. Finally, we propose a computational support tool implementing this updated metamodel.

Logics for Modelling Collective Attitudes

We introduce a number of logics to reason about collective propositional attitudes that are defined by means of the majority rule. It is well known that majoritarian aggregation is subject to irrationality, as the results in social choice theory and judgment aggregation show. The proposed logics for modelling collective attitudes are based on a substructural propositional logic that allows for circumventing inconsistent outcomes. Individual and collective propositional attitudes, such as beliefs, desires, obligations, are then modelled by means of minimal modalities to ensure a number of basic principles. In this way, a viable consistent modelling of collective attitudes is obtained.

The interplay between models and observations

We propose a formal framework to examine the relationship between models and observations. To make our analysis precise, models are reduced to first-order theories that represent both terminological knowledge—e.g., the laws that are supposed to regulate the domain under analysis and that allow for explanations, predictions, and simulations—and assertional knowledge—e.g., information about specific entities in the domain of interest. Observations are introduced into the domain of quantification of a distinct first-order theory that describes their nature and their organization and takes track of the way they are experimentally acquired or intentionally elaborated. A model mainly represents the theoretical knowledge or hypotheses on a domain, while the theory of observations mainly represents the empirical knowledge and the given experimental practices. We propose a precise identity criterion for observations and we explore different links between models and observations by assuming a degree of independence between them. By exploiting some techniques developed in the field of social choice theory and judgment aggregation, we sketch some strategies to solve inconsistencies between a given set of observations and the assumed theoretical hypotheses. The solutions of these inconsistencies can impact both the observations—e.g., the theoretical knowledge and the analysis of the way observations are collected or produced may highlight some unreliable sources—and the models—e.g., empirical evidences may invalidate some theoretical laws.

Integrating Computer Vision Algorithms and Ontologies for Spectator Crowd Behavior Analysis

Abstract Capturing and understanding crowd dynamics is an important issue under diverse perspectives. From social, psychological, and political sciences to safety management, studying, modeling, and predicting the presence, behavior, and dynamics of crowds, possibly preventing dangerous activities, is absolutely crucial. In the literature, crowds have been classified under different categories depending on their size and focus of attention. This chapter focuses on spectator crowds, namely crowds formed by people whose behavior is constrained by a structured environment, whose focus of attention is mainly shared, directed to a specific event. We first propose the backbone of an ontology of spectator crowd behavior based on a foundational analysis of both related literature and S-Hock, a massive annotated video dataset on crowd behavior during hockey events. Then, we present a new methodological approach integrating ontological reasoning, performed with a new description logic-based temporal formalism, with computer vision algorithms, allowing for automatic recognition of events happening in the playground, based on the behavior of the crowd in the stands.

Towards a Cognitive Semantics of Types

Types are a crucial concept in conceptual modelling, logic, and knowledge representation as they are an ubiquitous device to understand and formalise the classification of objects. We propose a logical treatment of types based on a cognitively inspired modelling that accounts for the amount of information that is actually available to a certain agent in the task of classification. We develop a predicative modal logic whose semantics is based on conceptual spaces that model the actual information that a cognitive agent has about objects, types, and the classification of an object under a certain type. In particular, we account for possible failures in the classification, for the lack of sufficient information, and for some aspects related to vagueness.

Majoritarian Group Actions

In this paper, we introduce a logic to reason about group actions for groups that are defined by means of the majority rule. It is well known that majoritarian aggregation is subject to irrationality, as the results in social choice theory and judgment aggregation show. The logic of action that we use here for modelling group actions is based on a substructural propositional logic that allows for preventing inconsistent outcome. Agency is modeled by means of a “bringing-it-about” modal logic with coalitions. We show that, in this way, it is possible to obtain a consistent model of agency of groups that are defined in an aggregative manner.

Group Conflict as Social Contradiction

This paper is a contribution to the development of an ontology of conflict. In particular, we single out and study a peculiar notion of group conflict, that we suggestively label “social contradiction.” In order to do so, we shall introduce and discuss the methodology of social choice theory, since it allows for defining the notion of collective attitude that may emerge from a number of possibly divergent individual attitudes. We shall see how collective attitudes lead to define a specific notion of group and therefore a specific notion of group conflict. As a conclusion, we shall present our abstract analysis of group conflicts and we shall position social contradiction with respect to other types of conflicts.