Abstract not found

We propose a modular ontology of the dynamic features of reality. This amounts, on the one hand, to a purely spatial ontology supporting snapshot views of the world at successive instants of time and, on the other hand, to a purely spatiotemporal ontology of change and process. We argue that dynamic spatial ontology must combine these two distinct types of inventory of the entities and relationships in reality, and we provide characterizations of spatiotemporal reasoning in the light of the interconnections between them.

Common sense is on the one hand a certain set of processes of natural cognition – of speaking, reasoning, seeing, and so on. On the other hand common sense is a system of beliefs (of folk physics, folk psychology and so on). Over against both of these is the world of common sense, the world of objects to which the processes of natural cognition and the corresponding belief-contents standardly relate. What are the structures of this world? How does the scientific treatment of this world relate to traditional and contemporary metaphysics and formal ontology? Can we embrace a thesis of common-sense realism to the effect that the world of common sense exists uniquely? Or must we adopt instead a position of cultural relativism which would assign distinct worlds of common sense to each group and epoch? The present paper draws on recent work in computer science (especially in the fields of naive and qualitative physics), in perceptual and developmental psychology, and in cognitive anthropology, in order to consider in a new light these and related questions and to draw conclusions for the methodology and philosophical foundations of the cognitive sciences.

Data modelling languages are used in today’s information systems engineering environments. Many have a degree of hype surrounding their quality and applicability with narrow and specific justification often given in support of one over another. We want to more deeply understand the fundamental nature of data modelling languages. We thus propose a theory, based on ontology, that should allow us to understand, compare, evaluate, and strengthen data modelling languages. In this paper we present a method (conceptual evaluation) and its extension (conceptual comparison), as part of our theory. Our methods are largely independent of a specific ontology. We introduce Chisholm’s ontology and apply our methods to analyse some data modelling languages using it. We find a good degree of overlap between all of the data modelling languages analysed and the core concepts of Chisholm’s ontology, and conclude that the data modelling languages investigated reflect an ontology of commonsense-realism.

Every domain-specific ontology must use as a framework some upper-level ontology which describes the most general domain-independent categories of reality. In the present paper we sketch a new type of upper-level ontology, and we outline an associated knowledge modelling language called GOL – for: General Ontological Language. It turns out that the upper-level ontology underlying well-known standard modelling languages such as KIF, F-Logic and CycL is restricted to the ontology of sets. In a set theory which allows Urelements, however, there will be ontological relations between these Urelements which the set-theoretic machinery cannot capture. In contrast to standard modelling and representation formalisms, GOL provides a machinery for representing and analysing such ontologically basic relations. GOL is thus a genuine extension of KIF and of similar languages. In GOL entities are divided into sets and Urelements, the latter being divided in their turn into individuals and universals. Foremost among the individuals are things or substances, tropes or moments, and situoids: entities containing facts as components.

Every domain-specific ontology must use as a framework some upper-level ontology which describes the most general, domain-independent categories of reality. In the present paper we sketch a new type of upper-level ontology, which is intended to be the basis of a knowledge modelling language GOL (for: 'General Ontological Language'). It turns out that the upper- level ontology underlying standard modelling languages such as KIF, F-Logic and CycL is restricted to the ontology of sets. Set theory has considerable mathematical power and great flexibility as a framework for modelling different sorts of structures. At the same time it has the disadvantage that sets are abstract entities (entities existing outside the realm of time, space and causality), and thus a set-theoretical framework should be supplemented by some other machinery if it is to support applications in the ripe, messy world of concrete objects. In the present paper we partition the entities of the real world into sets and urelements, and then we introduce several new ontological relations between these urelements. In contrast to standard modelling and representation formalisms, the concepts of GOL provide a machinery for representing and analysing such ontologically basic relations.

Formal ontology, as the science of the formal relations that structure reality as a whole, aims at a theory of categories corresponding to the most general features of possible objects, whether existing or non-existing. The present paper is an attempt to summarise and extend recent research in analytical metaphysics in a formalised theory of objects. Existence is characterised as a formal property, suggesting that the use of quantifiers alone does not involve any existential assumptions about the objects quantified over. However, the only non-existing objects allowed for in the present account are real or objective possibilities. De re modalities as well as ontological dependence are defined on the basis of a counterpart-theoretic specification of possibilia. The present framework allows for necessary and non-relative identity as well as for a granular parthood relationship satisfying the thesis of composition as partial identity. The paper culminates in the formalisation of an Aristotelian four-category ontology allowing for universals and particulars, substances and particularised properties; in this context, the redundance of higher-order material universals as well as moderate haecceitism is argued for. After a short analysis of relationality and extrinsicness, a theory of spatial and temporal objects is sketched and a temporal counterpart theory is proposed as a solution to the problem of temporary intrinsics. The paper concludes with some general remarks on the relation between ontology and the theory of subjectivity, defending a modal approach to consciousness and a counterpart theoretic analysis of intentionality.

Abstract not found

I shall attempt in what follows to show how mereology, taken together with certain topological notions, can yield the basis for future investigations in formal ontology. 2 I shall attempt to show also how the mereological framework here advanced can allow the direct and natural formulation of a series of theses – for example pertaining to the concept of

There are many data modelling languages used in today’s information systems engineering environment. Some of the data modelling languages used have a degree of hype surrounding their quality and applicability. We would like to understand exactly what makes some data modelling languages successful and in some way suggest how useful data modelling languages will be in the context of an organisation and why. We are also interested in a theory capable of unifying the disparate range of languages. To do these things we select a theory based on ontology using which data modelling languages can be investigated. In this context theory should allow us to understand, compare, evaluate, and strengthen data modelling languages. The theory may also be used to suggest how useful various data modelling languages may be in an organisational setting. In this paper we present Chisholm’s ontology which we use to investigate data modelling languages. We show how Chisholm’s ontology can be used as a unifying theory of data models, develop methods for comparing data modelling languages based on this theory and summarise our findings. In conclusion, we evaluate the methods and the theory and examine avenues for future research. In this paper we present a deeper understanding of method together with analysis of new data modelling languages.