Online research in philosophy

Increasingly, in data-intensive areas of the life sciences, experimental results are being described in algorithmically useful ways with the help of ontologies. Such ontologies are authored and maintained by scientists to support the retrieval, integration and analysis of their data. The proposition to be defended here is that ontologies of this type – the Gene Ontology (GO) being the most conspicuous example – are a part of science. Initial evidence for the truth of this proposition (which some will find self-evident) is the increasing recognition of the importance of empirically-based methods of evaluation to the ontology development work being undertaken in support of scientific research. Ontologies created by scientists must, of course, be associated with implementations satisfying the requirements of software engineering. But the ontologies are not themselves engineering artifacts, and to conceive them as such brings grievous consequences. Rather, ontologies such as the GO are in different respects comparable to scientific theories, to scientific databases, and to scientific journal publications. Such a view implies a new conception of what is involved in the authoring, maintenance and application of ontologies in scientific contexts, and therewith also a new approach to the evaluation of ontologies and to the training of ontologists.

In standard approaches, cognition is assumed to be a distinct functional module from action and interaction, on the one hand, and motivation, on the other hand. I argue that the apparent possibility of such modularization is itself a consequence of a false underlying presupposition concerning the nature of representation. An alternative model of representation is outlined, and it is shown that action and motivation emerge naturally and necessarily as aspects of one single underlying ontology of the interactive agent.

This paper describes the construction method of a legal application ontology. This method is based on the merging of micro-ontologies built from European community directives. The terminae construction method from texts enhanced by an alignment process with a core legal ontology is used for building micro-ontologies. A merging process allows constructing the legal ontology.

Human self-consciousness operates at different levels of complexity and at least comprises five different levels of representational processes. These five levels are nonconceptual representation, conceptual representation, sentential representation, meta-representation, and iterative meta-representation. These different levels of representation can be operationalized by taking a first-person-perspective that is involved in representational processes on different levels of complexity. We refer to experiments that operationalize a first-person-perspective on the level of conceptual and meta-representational self-consciousness. Interestingly, these experiments show converging evidence for a recruitment of medial cortical and parietal regions during taking a first-person-perspective, even when operating on different degrees of complexity. These data lend support for the speculative hypothesis, that there exist a neural signature for human self-consciousness that is recruited independent from the degree of representational complexity to be performed.

The distinction between reference ontologies and application ontologies crept rather unobtrusively into the recent literature on knowledge engineering. A lot of the discourse surrounding this distinction – notably, the one framing the workshop generating this collection of papers – suggests the two types of ontologies are in some sort of opposition to one another. Thus, Borge et al. [3] characterize reference ontologies (more recently, foundational ontologies) as rich, axiomatic theories whose focus is to clarify the intended meanings of terms used in specific domains. Application ontologies, by contrast, provide a minimal terminological structure to fit the needs of a specific community. Reflecting their minimal nature, Masolo et al. [7] refer to such ontologies as “lightweight” ontologies. An application ontology can be lightweight in a second respect as well, namely, that it may not necessarily take the form of fully-fledged axiomatic theory. Rather, it might only be a taxonomy of the relevant domain, a division of the domain into a salient collection of classes, perhaps ordered by the subclass relation. Importantly, though, for an application ontology to “fit the needs of a specific community” needn’t require representational accuracy. In the “worst” case (from a reference ontology perspective), to fit the needs of a community is just to represent uncritically what people in that community think about the ontology’s domain.

This article clarifies three principles that should guide the development of any cognitive ontology. First, that an adequate cognitive ontology depends essentially on an adequate task ontology; second, that the goal of developing a cognitive ontology is independent of the goal of finding neural implementations of the processes referred to in the ontology; and third, that cognitive ontologies are neutral regarding the metaphysical relationship between cognitive and neural processes.

The notion of levels has been widely used in discussions of cognitive science, especially in discussions of the relation of connectionism to symbolic modeling of cognition. I argue that many of the notions of levels employed are problematic for this purpose, and develop an alternative notion grounded in the framework of mechanistic explanation. By considering the source of the analogies underlying both symbolic modeling and connectionist modeling, I argue that neither is likely to provide an adequate analysis of processes at the level at which cognitive theories attempt to function: One is drawn from too low a level, the other from too high a level. If there is a distinctly cognitive level, then we still need to determine what are the basic organizational principles at that level.

Information and representation are thought to be intimately related. Representation, in fact, is commonly considered to be a special kind of information. It must be a _special_ kind, because otherwise all of the myriad instances of informational relationships in the universe would be representational -- some restrictions must be placed on informational relationships in order to refine the vast set into those that are truly representational. I will argue that information in this general sense is important to genuine agents, but that it is a blind alley with regard to the attempt to understand representation. On the other hand, I will also argue that a different, quite non-standard, form of information is central to genuine representation. First I turn to some of the reasons why information as usually considered is the wrong category for understanding representation; second to an alternative model of representation -- one that is naturally emergent in autonomous agents, and that does involve information, but not in any standard form; and third I return to standard notions of informational relationships and show what they are in fact useful for.

Connectionism and classicism, it generally appears, have at least this much in common: both place some notion of internal representation at the heart of a scientific study of mind. In recent years, however, a much more radical view has gained increasing popularity. This view calls into question the commitment to internal representation itself. More strikingly still, this new wave of anti-representationalism is rooted not in armchair theorizing but in practical attempts to model and understand intelligent, adaptive behavior. In this paper we first present, and then critically assess, a variety of recent anti-representationalist treatments. We suggest that so far, at least, the sceptical rhetoric outpaces both evidence and argument. Some probable causes of this premature scepticism are isolated. Nonetheless, the anti-representationalist challenge is shown to be both important and progressive insofar as it forces us to see beyond the bare representational/non-representational dichotomy and to recognize instead a rich continuum of degrees and types of representationality.

This article focuses on the problem of representational content. Accounting for representational content is the central issue in contemporary naturalism: it is the major remaining task facing a naturalistic conception of the world. Representational content is also the central barrier to contemporary cognitive science and artificial intelligence: it is not possible to understand representation in animals nor to construct machines with genuine representation given current (lack of) understanding of what representation is. An elaborated critique is offered to current approaches to representation, arguing that the basic underlying approach is, at root, logically incoherent, and, thus, that standard approaches are doomed to failure. An alternative model of representation - interactivism - is presented that avoids or solves the problems facing standard approaches. Interactivism is framed by a version of functionalism, and a naturalization of that functionalism completes an outline of a naturalization of representation and representational content.