The Meaning of UML Models

The Unified Modelling Language (UML) is intended to express complex ideas in an intuitive and easily understood way. It is important because it is widely used in software engineering and other disciplines. Although an official definition document exists, there is much debate over the precise meaning of UML models. ¶ In response, the academic community have put forward many different proposals for formalising UML, but it is not at all obvious how to decide between them. Indeed, given that UML practitioners are inclined to reject formalisms as non-intuitive, it is not even obvious that the definition should be “formal” at all. Rather than searching for yet another formalisation of UML, our main aim is to determine what would constitute a good definition of UML. ¶ The first chapter sets the UML definition problem in a broad context, relating it to work in logic and the philosophy of science. ..

Economics and Engineering for Preserving Digital Content

Progress towards practical long-term preservation seems to be stalled. Preservationists cannot afford specially developed technology, but must exploit what is created for the marketplace. Economic and technical facts suggest that most preservation ork should be shifted from repository institutions to information producers and consumers. Prior publications describe solutions for all known conceptual challenges of preserving a single digital object, but do not deal with software development or scaling to large collections. Much of the document handling software needed is available. It has, however, not yet been selected, adapted, integrated, or deployed for digital preservation. The daily tools of both information producers and information consumers can be extended to embed preservation packaging without much burdening these users. We describe a practical strategy for detailed design and implementation. Document handling is intrinsically complicated because of human sensitivity to communication nuances. Our engineering section therefore starts by discussing how project managers can master the many pertinent details.

A compositional neural architecture for language

Hierarchical structure and compositionality imbue human language with unparalleled expressive power and set it apart from other perception–action systems. However, neither formal nor neurobiological models account for how these defining computational properties might arise in a physiological system. I attempt to reconcile hierarchy and compositionality with principles from cell assembly computation in neuroscience; the result is an emerging theory of how the brain could convert distributed perceptual representations into hierarchical structures across multiple timescales while representing interpretable incremental stages of (de) compositional meaning. The model's architecture—a multidimensional coordinate system based on neurophysiological models of sensory processing—proposes that a manifold of neural trajectories encodes sensory, motor, and abstract linguistic states. Gain modulation, including inhibition, tunes the path in the manifold in accordance with behavior and is how latent structure is inferred. As a consequence, predictive information about upcoming sensory input during production and comprehension is available without a separate operation. The proposed processing mechanism is synthesized from current models of neural entrainment to speech, concepts from systems neuroscience and category theory, and a symbolic-connectionist computational model that uses time and rhythm to structure information. I build on evidence from cognitive neuroscience and computational modeling that suggests a formal and mechanistic alignment between structure building and neural oscillations and moves toward unifying basic insights from linguistics and psycholinguistics with the currency of neural computation

Lenguajes austeros de modelado conceptual de datos basados en evidencias

Multiple logic-based reconstructions of UML class diagram, Entity Relationship diagrams, and Obect-Role Model diagrams exists. They mainly cover various fragments of these Conceptual Data Modelling Languages and none are formalised such that the logic applies simultaneously for the three language families as a unifying mechanism. This hampers interchangeability, interoperability, and tooling support. In addition, due to the lack of a systematic design process of the logic used for the formalisation, hidden choices permeate the formalisations that have rendered them incompatible. We aim to address these problems, first, by structuring the logic design process in a methodological way. We generalise and extend the DSL design process to logic language design. In particular, a new phase of ontological analysis of language features is included, to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, we specify minimal logic profiles availing of this extended process, including the ontological commitments embedded in the languages, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL). The profiles characterise the essential logic structure needed to handle the semantics of conceptual models, therewith enabling the development of interoperability tools. No known DL language matches exactly the features of those profiles and the common core is in the tractable DL ACJfl. Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models.Existen varias reconstrucciones basadas en lógica de lenguajes de modelado conceptual como EER, diagramas de clases UML y ORM. Principalmente cubren fragmentos de estos lenguajes, y sus formalizaciones no están hechas para que se apliquen simultáneamente a estas tres familias de lenguajes como un mecanismo de unificación. Este hecho atenta contra el intercambio y la interoperabilidad de los modelos y el desarrollo de herramientas de soporte. Además, dada la falta de un proceso sistemático de diseño, ciertas decisiones ocultas en la representación lógica hacen que las formalizaciones sean incompatibles. En este trabajo nos proponemos atacar este problema, proponiendo primero un proceso de diseño lógico que puede ser aplicado en forma metodológica. Se generaliza y extiende el proceso DSL para que se pueda aplicar al diseño de lenguajes lógicos en general, incorporando análisis ontológico de las características del lenguaje. Segundo, se especifican perfiles lógicos minimales que sacan provecho de este proceso extendido, incluyendo los compromisos ontológicos asumidos, de evidencia de uso de las características del lenguaje, y de los propiedades computacionales de las Lógicas Descriptivas (DL, description logics). Estos perfiles caracterizan la estructura lógica esencial que se necesita para manejar la semántica de los modelos conceptuales, habilitando el desarrollo de herramientas automáticas de interoperabilidad. No existe correspondencia exacta directa entre estos perfiles y fragmentos conocidos de lenguajes DL, y el núcleo común es pequeño (la lógica tratable ACNT). Aunque es muy poca la posibilidad de derivar inconsistencias dentro de estos perfiles, es prometedor su uso en modelos conceptuales dado su complejidad en tiempo escalable.Facultad de Informátic

Evidence-based lean logic profiles for conceptual data modelling languages

Multiple logic-based reconstruction of conceptual data modelling languages such as EER, UML Class Diagrams, and ORM exists. They mainly cover various fragments of the languages and none are formalised such that the logic applies simultaneously for all three modelling language families as unifying mechanism. This hampers interchangeability, interoperability, and tooling support. In addition, due to the lack of a systematic design process of the logic used for the formalisation, hidden choices permeate the formalisations that have rendered them incompatible. We aim to address these problems, first, by structuring the logic design process in a methodological way. We generalise and extend the DSL design process to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, availing of this extended process, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL), we specify logic profiles taking into account the ontological commitments embedded in the languages. The profiles characterise the minimum logic structure needed to handle the semantics of conceptual models, enabling the development of interoperability tools. There is no known DL language that matches exactly the features of those profiles and the common core is small (in the tractable ALNI). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models

Evidence-based lean conceptual data modelling languages

Multiple logic-based reconstructions of conceptual data modelling languages such as EER, UML Class Diagrams, and ORM exist. They mainly cover various fragments of the languages and none are formalised such that the logic applies simultaneously for all three modelling language families as unifying mechanism. This hampers interchangeability, interoperability, and tooling support. In addition, due to the lack of a systematic design process of the logic used for the formalisation, hidden choices permeate the formalisations that have rendered them incompatible. We aim to address these problems, first, by structuring the logic design process in a methodological way. We generalise and extend the DSL design process to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, we specify minimal logic profiles availing of this extended process, including the ontological commitments embedded in the languages, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL). The profiles characterise the essential logic structure needed to handle the semantics of conceptual models, therewith enabling the development of interoperability tools. There is no known DL language that matches exactly the features of those profiles and the common core is small (in the tractable DL ALNI). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models

Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

A Framework for Exploring Finite Models

This thesis presents a framework for understanding first-order theories by investigating their models. A common application is to help users, who are not necessarily experts in formal methods, analyze software artifacts, such as access-control policies, system configurations, protocol specifications, and software designs. The framework suggests a strategy for exploring the space of finite models of a theory via augmentation. Also, it introduces a notion of provenance information for understanding the elements and facts in models with respect to the statements of the theory. The primary mathematical tool is an information-preserving preorder, induced by the homomorphism on models, defining paths along which models are explored. The central algorithmic ideas consists of a controlled construction of the Herbrand base of the input theory followed by utilizing SMT-solving for generating models that are minimal under the homomorphism preorder. Our framework for model-exploration is realized in Razor, a model-finding assistant that provides the user with a read-eval-print loop for investigating models

The Nature and Implementation of Representation in Biological Systems

I defend a theory of mental representation that satisfies naturalistic constraints. Briefly, we begin by distinguishing (i) what makes something a representation from (ii) given that a thing is a representation, what determines what it represents. Representations are states of biological organisms, so we should expect a unified theoretical framework for explaining both what it is to be a representation as well as what it is to be a heart or a kidney. I follow Millikan in explaining (i) in terms of teleofunction, explicated in terms of natural selection. To explain (ii), we begin by recognizing that representational states do not have content, that is, they are neither true nor false except insofar as they both “point to” or “refer” to something, as well as “say” something regarding whatever it is they are about. To distinguish veridical from false representations, there must be a way for these separate aspects to come apart; hence, we explain (ii) by providing independent theories of what I call f-reference and f-predication (the ‘f’ simply connotes ‘fundamental’, to distinguish these things from their natural language counterparts). Causal theories of representation typically founder on error, or on what Fodor has called the disjunction problem. Resemblance or isomorphism theories typically founder on what I’ve called the non-uniqueness problem, which is that isomorphisms and resemblance are practically unconstrained and so representational content cannot be uniquely determined. These traditional problems provide the motivation for my theory, the structural preservation theory, as follows. F-reference, like reference, is a specific, asymmetric relation, as is causation. F-predication, like predication, is a non-specific relation, as predicates typically apply to many things, just as many relational systems can be isomorphic to any given relational system. Putting these observations together, a promising strategy is to explain f-reference via causal history and f-predication via something like isomorphism between relational systems. This dissertation should be conceptualized as having three parts. After motivating and characterizing the problem in chapter 1, the first part is the negative project, where I review and critique Dretske’s, Fodor’s, and Millikan’s theories in chapters 2-4. Second, I construct my theory about the nature of representation in chapter 5 and defend it from objections in chapter 6. In chapters 7-8, which constitute the third and final part, I address the question of how representation is implemented in biological systems. In chapter 7 I argue that single-cell intracortical recordings taken from awake Macaque monkeys performing a cognitive task provide empirical evidence for structural preservation theory, and in chapter 8 I use the empirical results to illustrate, clarify, and refine the theory