Online research in philosophy

This project investigates the possibility of variation in the semantic component, a new and dynamic area of study in formal approaches to semantics. Its particular focus is the effect on variation of language contact. The semantic status of classifier languages of South Asia, which have been described as marginal instances of this language type, is used to illustrate the nature of the investigation. Data from a small representative sample of such languages will be collected. The semantic system of these languages, which have been in contact with languages without classifiers, will be compared with the semantic system of core classifier languages such as Chinese and Japanese. The study will enrich the theoretical base for analyses of classifier systems by introducing the data from South Asian classifier languages, hitherto unknown in the semantic literature. It will shed light on current debates on the range of semantic variation permitted in natural language.

Summary. The purpose of this article is to serve as a clear introduction to the modeling languages of the three most widely used IDEF methods: IDEF0, IDEF1X, and IDEF3. Each language is presented in turn, beginning with a discussion of the underlying “ontology” the language purports to describe, followed by presentations of the syntax of the language — particularly the notion of a model for the language — and the semantical rules that determine how models are to be interpreted. The level of detail should be sufficient to enable the reader both to understand the intended areas of application of the languages and to read and construct simple models of each of the three types.

Over the last decade the modeling and the storage of biological data has been a topic of wide interest for scientists dealing with biological and biomedical research. Currently most data is still stored in text files which leads to data redundancies and file chaos.In this paper we show how to use relational modeling techniques and relational database technology for modeling and storing biological sequence data, i.e. for data maintained in collections like EMBL or SWISS-PROT to better serve the needs for these application domains.

Information modeling (also known as conceptual modeling or semantic data modeling) may be characterized as the formulation of a model in which information aspects of objective and subjective reality are presented (the application), independent of datasets and processes by which they may be realized (the system).A methodology for information modeling should incorporate a number of concepts which have appeared in the literature, but should also be formulated in terms of constructs which are understandable to and expressible by the system user as well as the system developer. This is particularly desirable in connection with certain intimate relationships, such as being the same as or being a part of.

Experimental activity is traditionally identified with testing the empirical implications or numerical simulations of models against data. In critical reaction to the ‘tribunal view’ on experiments, this essay will show the constructive contribution of experimental activity to the processes of modeling and simulating. Based on the analysis of a case in fluid mechanics, it will focus specifically on two aspects. The first is the controversial specification of the conditions in which the data are to be obtained. The second is conceptual clarification, with a redefinition of concepts central to the understanding of the phenomenon and the conditions of its occurrence.

he goal of this article is to formalize Object Role Modeling (ORM) using the DLR description logic. This would enable automated reasoning on the formal properties of ORM diagrams, such as detecting constraint contradictions and implications. In addition, the expressive, methodological, and graphical capabilities of ORM make it a good candidate for use as a graphical notation for most description logic languages. In this way, industrial experts who are not IT savvy will still be able to build and view axiomatized theories (such as ontologies, business rules, etc.) without needing to know the logic or reasoning foundations underpinning them. Our formalization in this paper is structured as 29 formalization rules, that map all ORM primitives and constraints into DLR, and 2 exceptions of complex cases. To this end, we illustrate the implementation of our formalization as an extension to DogmaModeler, which automatically maps ORM into DIG and uses Racer as a background reasoning engine to reason about ORM diagrams.

Clahsen's compelling evidence for the dual-mechanism model of the lexicon derives in part from the use of cross-linguistic data in psycholinguistic research. This approach reflects a growing (and positive) trend toward incorporating data from several languages when analyzing and modeling human language behavior. This perspective should be expanded to include data from typologically distinct languages to develop more explanatory models of language.

Development of ontology development tools and ontology-enhanced software applications requires thorough understanding of ontology languages in order to implement them according to their specification. We present a formal specification of the ontologies part of the Web Services Modeling Language WSML documentation as a conceptual model in ORM2. Such an approach abstracts the semantics about ontological constructs, axioms, and properties from their implementation in arbitrary formats, thereby making the model easily understandable and reusable. This formal model in ORM2, which is understandable by both logician and software developer, can be used as any other conceptual model to develop applications, thereby ensuring smooth transition from theory to implementations that are faithful to the theory.

The distinction between the modeling of information and the modeling of data in the creation of automated systems has historically been important because the development tools available to programmers have been wedded to machine oriented data types and processes. However, advances in software engineering, particularly the move toward data abstraction in software design, allow activities reasonably described as information modeling to be performed in the software creation process. An examination of the evolution of programming languages and development of general programming paradigms, including object-oriented design and implementation, suggests that while data modeling will necessarily continue to be a programmer's concern, more and more of the programming process itself is coming to be characterized by information modeling activities.

From the side of modelers and early-adopter industry, interest in reasoning over conceptual models and other online usage of conceptual models is growing. To obtain a more precise insight in the characteristics of the main conceptual modeling languages, we define the (semi-)standardized ORM, ORM2, UML, ER, and EER diagram languages in terms of the new generic conceptual data modeling language CMcom that is based on the DL language DLRifd. CMcom has the most expressive common denominator with these languages. CMcom advances prospects for automated, online, interoperability among diverse conceptual data models and ensures compatibility with and between industrygrade conceptual data modeling languages.