Expressions designating collectives, such as “the committee” or “the ships in the port”, may be interpreted de re or de dicto, depending on context, according as they pick out collectives defined by their members or collectives defined by some criterion for membership. We call these E-collectives and I-collectives respectively, and in this paper we explore in depth the relationship between these two categories. In particular, we identify important respects in which they differ, regarding the nature of the dependence of the (...) collective on its members, the nature of the parthood relation in which the members stand to the whole, and, from an application perspective, the different methods used for identifying the two forms of collective from large spatio-temporal data-sets. (shrink)

GUM is a linguistically-motivated ontology originally developed to support natural language processing systems by offering a level of representation intermediate between linguistic forms and domain knowledge. Whereas modeling decisions for individual domains may need to be responsive to domain-specific criteria, a linguistically-motivated ontology offers a characterization that generalizes across domains because its design criteria are derived independently both of domain and of application. With respect to this mediating role, the use of GUM resembles the adoption of upper ontologies as tools (...) for mediating across domains and for supporting domain modeling. This paper briefly introduces the ontology, setting out its origins, design principles and applications. The example cases for this special issue are then described, illustrating particularly some of the principal differences and similarities of GUM to non-linguistically motivated upper ontologies. (shrink)