Reified Context Models

A classic tension exists between exact inference in a simple model and approximate inference in a complex model. The latter offers expressivity and thus accuracy, but the former provides coverage of the space, an important property for confidence estimation and learning with indirect supervision. In this work, we introduce a new approach, reified context models, to reconcile this tension. Specifically, we let the amount of context (the arity of the factors in a graphical model) be chosen "at run-time" by reifying it---that is, letting this choice itself be a random variable inside the model. Empirically, we show that our approach obtains expressivity and coverage on three natural language tasks

On the Reification of Global Constraints

We introduce a simple idea for deriving reified global constraints in a systematic way. It is based on the observation that most global constraints can be reformulated as a conjunction of pure functional dependency constraints together with a constraint that can be easily reified. We first show how the core constraints of the Global Constraint Catalogue can be reified and we then identify several reification categories that apply to at least 82% of the constraints in the Global Constraint Catalogue

Examining the five‐stage e‐moderating model: Designed and emergent practice in the learning technology profession

This paper highlights the need for learning technologists to establish their ‘academic legitimacy’ within the complexities of online learning and teaching practice. Frameworks such as the ‘five stage e‐moderating model’ can be useful in developing the knowledge base but there are dangers in them becoming too reified within an increasingly commodified higher education (HE) environment. The paper calls for greater professional reflexivity and contestation within learning technology practice and concludes by inviting the Alt‐J readership to engage in a critical debate with regard to these issues

Transformation Techniques for OCL Constraints

Constraints play a key role in the definition of conceptual schemas. In the UML, constraints are usually specified by means of invariants written in the OCL. However, due to the high expressiveness of the OCL, the designer has different syntactic alternatives to express each constraint. The techniques presented in this paper assist the designer during the definition of the constraints by means of generating equivalent alternatives for the initially defined ones. Moreover, in the context of the MDA, transformations between these different alternatives are required as part of the PIM-to-PIM, PIM-to-PSM or PIM-to-code transformations of the original conceptual schema

Pattern Reification as the Basis for Description-Driven Systems

One of the main factors driving object-oriented software development for information systems is the requirement for systems to be tolerant to change. To address this issue in designing systems, this paper proposes a pattern-based, object-oriented, description-driven system (DDS) architecture as an extension to the standard UML four-layer meta-model. A DDS architecture is proposed in which aspects of both static and dynamic systems behavior can be captured via descriptive models and meta-models. The proposed architecture embodies four main elements - firstly, the adoption of a multi-layered meta-modeling architecture and reflective meta-level architecture, secondly the identification of four data modeling relationships that can be made explicit such that they can be modified dynamically, thirdly the identification of five design patterns which have emerged from practice and have proved essential in providing reusable building blocks for data management, and fourthly the encoding of the structural properties of the five design patterns by means of one fundamental pattern, the Graph pattern. A practical example of this philosophy, the CRISTAL project, is used to demonstrate the use of description-driven data objects to handle system evolution.Comment: 20 pages, 10 figure

Constraint-based sequence mining using constraint programming

The goal of constraint-based sequence mining is to find sequences of symbols that are included in a large number of input sequences and that satisfy some constraints specified by the user. Many constraints have been proposed in the literature, but a general framework is still missing. We investigate the use of constraint programming as general framework for this task. We first identify four categories of constraints that are applicable to sequence mining. We then propose two constraint programming formulations. The first formulation introduces a new global constraint called exists-embedding. This formulation is the most efficient but does not support one type of constraint. To support such constraints, we develop a second formulation that is more general but incurs more overhead. Both formulations can use the projected database technique used in specialised algorithms. Experiments demonstrate the flexibility towards constraint-based settings and compare the approach to existing methods.Comment: In Integration of AI and OR Techniques in Constraint Programming (CPAIOR), 201

Reification and Truthmaking Patterns

Reification is a standard technique in conceptual modeling, which consists of including in the domain of discourse entities that may otherwise be hidden or implicit. However, deciding what should be rei- fied is not always easy. Recent work on formal ontology offers us a simple answer: put in the domain of discourse those entities that are responsible for the (alleged) truth of our propositions. These are called truthmakers. Re-visiting previous work, we propose in this paper a systematic analysis of truthmaking patterns for properties and relations based on the ontolog- ical nature of their truthmakers. Truthmaking patterns will be presented as generalization of reification patterns, accounting for the fact that, in some cases, we do not reify a property or a relationship directly, but we rather reify its truthmakers