J. Neil Otte

Basic Formal Ontology (BFO) is a top-level ontology consisting of thirty-six classes, designed to support information integration, retrieval, and analysis across all domains of scientific investigation, presently employed in over 350 ontology projects around the world. BFO is a genuine top-level ontology, containing no terms particular to material domains, such as physics, medicine, or psychology. In this paper, we demonstrate how a series of cases illustrating common types of change may be represented by universals, defined classes, and relations employing the BFO framework. We provide discussion of these cases to provide a template for other ontologists using BFO, as well as to facilitate comparison with the strategies proposed by ontologists using different top-level ontologies.

Names and natural kind terms have long been a major focus of debates about meaning and reference. This article discusses some of the theories and arguments that have appeared in those debates. It is remarkably difficult to say what names are without making controversial theoretical assumptions. This article does not attempt to do so here. It instead relies on paradigm examples that nearly all theorists would agree are proper names, for instance, ‘Aristotle’, ‘Mark Twain’, ‘London’, ‘Venus’, and ‘Pegasus’. All of the proper names that are discussed in the article are singular nouns that have no syntactic structure. Most of them refer to objects, but some, such as ‘Pegasus’, apparently do not. The article begins with proper names and the question ‘What is the meaning of a proper name?’ It turns to natural kind terms later.

Progress in philosophy is difficult to achieve because our methods are evidentially and rhetorically weak. In the last two decades, experimental philosophers have begun to employ the methods of the social sciences to address philosophical questions. However, the adequacy of these methods has been called into question by repeated failures of replication. Experimental philosophers need to incorporate more robust methods to achieve a multi-modal perspective. In this chapter, we describe and showcase cutting-edge methods for data-mining and visualization. Big data is a useful investigatory tool for moral psychology, and it fits well with the Ramsification method the first author advances in a series of recent papers. The guiding insight of these papers is that we can infer the meaning and structure of concepts from patterns of assertions and inferential associations in natural language.