Taxonomy for Humans or Computers? Cognitive Pragmatics for Big Data

Criticism of big data has focused on showing that more is not necessarily better, in the sense that data may lose their value when taken out of context and aggregated together. The next step is to incorporate an awareness of pitfalls for aggregation into the design of data infrastructure and institutions. A common strategy minimizes aggregation errors by increasing the precision of our conventions for identifying and classifying data. As a counterpoint, we argue that there are pragmatic trade-offs between precision and ambiguity that are key to designing effective solutions for generating big data about biodiversity. We focus on the importance of theory-dependence as a source of ambiguity in taxonomic nomenclature and hence a persistent challenge for implementing a single, long-term solution to storing and accessing meaningful sets of biological specimens. We argue that ambiguity does have a positive role to play in scientific progress as a tool for efficiently symbolizing multiple aspects of taxa and mediating between conflicting hypotheses about their nature. Pursuing a deeper understanding of the trade-offs and synthesis of precision and ambiguity as virtues of scientific language and communication systems then offers a productive next step for realizing sound, big biodiversity data services

Some , And Possibly All, Scalar Inferences Are Not Delayed: Evidence For Immediate Pragmatic Enrichment

Scalar inferences are commonly generated when a speaker uses a weaker expression rather than a stronger alternative, e.g., John ate some of the apples implies that he did not eat them all. This article describes a visual-world study investigating how and when perceivers compute these inferences. Participants followed spoken instructions containing the scalar quantifier some directing them to interact with one of several referential targets (e.g., Click on the girl who has some of the balloons). Participants fixated on the target compatible with the implicated meaning of some and avoided a competitor compatible with the literal meaning prior to a disambiguating noun. Further, convergence on the target was as fast for some as for the non-scalar quantifiers none and all. These findings indicate that the scalar inference is computed immediately and is not delayed relative to the literal interpretation of some. It is argued that previous demonstrations that scalar inferences increase processing time are not necessarily due to delays in generating the inference itself, but rather arise because integrating the interpretation of the inference with relevant information in the context may require additional time. With sufficient contextual support, processing delays disappear

Guidelines to Study Differences in Expressiveness between Ontology Specification Languages: A Case Of Study

We focus on our experiences on translating ontologies between two ontology languages, FLogic and Ontolingua, in the framework of Methontology and ODE. Rather than building "ad hoc" translators between languages or using KIF, our option consists of translating through ODE intermediate representations. So, we have built direct translators from ODE intermediate representations to Ontolingua and FLogic, and we have also built reverse translators from these two languages to ODE intermediate representations. Expressiveness of the target languages is the main feature to analyse when automatically generating ontologies from ODE intermediate representations. Therefore, we analyse the expressiveness of Ontolingua and FLogic for creating classes, instances, relations, functions and axioms, which are the essential components in ontologies. The motivation for this analysis can be found in the (KA)² initiative and can be easily extended to any other domains and languages

The inheritance of dynamic and deontic integrity constraints or: Does the boss have more rights?

In [18,23], we presented a language for the specification of static, dynamic and deontic integrity constraints (IC's) for conceptual models (CM's). An important problem not discussed in that paper is how IC's are inherited in a taxonomic network of types. For example, if students are permitted to perform certain actions under certain preconditions, must we repeat these preconditions when specializing this action for the subtype of graduate students, or are they inherited, and if so, how? For static constraints, this problem is relatively trivial, but for dynamic and deontic constraints, it will turn out that it contains numerous pitfalls, caused by the fact that common sense supplies presuppositions about the structure of IC inheritance that are not warranted by logic. In this paper, we unravel some of these presuppositions and show how to avoid the pitfalls. We first formulate a number of general theorems about the inheritance of necessary and/or sufficient conditions and show that for upward inheritance, a closure assumption is needed. We apply this to dynamic and deontic IC's, where conditions arepreconditions of actions, and show that our common sense is sometimes mistaken about the logical implications of what we have specified. We also show the connection of necessary and sufficient preconditions of actions with the specification of weakest preconditions in programming logic. Finally, we argue that information analysts usually assume constraint completion in the specification of (pre)conditions analogous to predicate completion in Prolog and circumscription in non-monotonic logic. The results are illustrated with numerous examples and compared with other approaches in the literature

Linguistics in the Study and Teaching of Literature

Literary texts include linguistic form, as well as specialized literary forms (some of which also involve language). Linguistics can offer to literary studies an understanding of these kinds of form, and the ways by which a text is used to communicate meaning. In order to cope with the great variety of creative uses of language in literature, linguistics must acknowledge that some texts are assigned structure by non-linguistic means, but the boundaries between linguistic and non-linguistic explanations for literary language are not clearly drawn. The article concludes with discussion of what kinds and level of linguistics might usefully be taught in a literature classroom, and offers practical suggestions for the application of linguistics to literature teaching

Thoughts about disordered thinking: measuring and quantifying the laws of order and disorder

Peer ReviewedPostprint (author's final draft