1,434 research outputs found
Using Ontologies for the Design of Data Warehouses
Obtaining an implementation of a data warehouse is a complex task that forces
designers to acquire wide knowledge of the domain, thus requiring a high level
of expertise and becoming it a prone-to-fail task. Based on our experience, we
have detected a set of situations we have faced up with in real-world projects
in which we believe that the use of ontologies will improve several aspects of
the design of data warehouses. The aim of this article is to describe several
shortcomings of current data warehouse design approaches and discuss the
benefit of using ontologies to overcome them. This work is a starting point for
discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure
A Comparison of Two-Level and Multi-level Modelling for Cloud-Based Applications
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-21151-0_2The Cloud Modelling Framework (CloudMF) is an approach to apply model-driven engineering principles to the specification and execution of cloud-based applications. It comprises a domain-specific language to model the deployment topology of multi-cloud applications, along with a models@run-time environment to facilitate reasoning and adaptation of these applications at run-time. This paper reports on some challenges encountered during the design of CloudMF, related to the adoption of the two-level modelling approach and especially the type-instance pattern. Moreover, it proposes the adoption of an alternative, multi-level modelling approach to tackle these challenges, and provides a set of criteria to compare both approaches.The research leading to these results has received funding from the European Commission’s Seventh Framework Programme (FP7/2007-2013) under grant agreement numbers 317715 (PaaSage), 318392 (Broker@Cloud), and 611125 (MONDO), the Spanish Ministry under project Go Lite (TIN2011-24139), and the Madrid Region under project SICOMORO (S2013/ICE-3006)
Logical operators for ontological modeling
We show that logic has more to offer to ontologists than standard first order
and modal operators. We first describe some operators of linear logic which we
believe are particularly suitable for ontological modeling, and suggest how to interpret
them within an ontological framework. After showing how they can coexist
with those of classical logic, we analyze three notions of artifact from the literature
to conclude that these linear operators allow for reducing the ontological commitment
needed for their formalization, and even simplify their logical formulation
Semantics and Validation of Shapes Schemas for RDF
We present a formal semantics and proof of soundness for shapes schemas, an
expressive schema language for RDF graphs that is the foundation of Shape
Expressions Language 2.0. It can be used to describe the vocabulary and the
structure of an RDF graph, and to constrain the admissible properties and
values for nodes in that graph. The language defines a typing mechanism called
shapes against which nodes of the graph can be checked. It includes an
algebraic grouping operator, a choice operator and cardinality constraints for
the number of allowed occurrences of a property. Shapes can be combined using
Boolean operators, and can use possibly recursive references to other shapes.
We describe the syntax of the language and define its semantics. The
semantics is proven to be well-defined for schemas that satisfy a reasonable
syntactic restriction, namely stratified use of negation and recursion. We
present two algorithms for the validation of an RDF graph against a shapes
schema. The first algorithm is a direct implementation of the semantics,
whereas the second is a non-trivial improvement. We also briefly give
implementation guidelines
A formalisation of deep metamodelling
The final publication is available at Springer via http://dx.doi.org/10.1007/s00165-014-0307-xMetamodelling is one of the pillars of model-driven engineering, used for language engineering and domain modelling. Even though metamodelling is traditionally based on a two-metalevel approach, several researchers have pointed out limitations of this solution and proposed an alternative deep (also called multi-level) approach to obtain simpler system specifications. However, this approach currently lacks a formalisation that can be used to explain fundamental concepts such as deep characterisation, double linguistic/ontological typing and linguistic extension. This paper provides such a formalisation based on the Diagram Predicate Framework, and discusses its practical realisation in the metaDepth tool.This work was partially funded by the SpanishMinistry of Economy and Competitiveness (project “Go Lite” TIN2011-
24139)
Abstract Representation of Music: A Type-Based Knowledge Representation Framework
The wholesale efficacy of computer-based music research is contingent on the sharing and reuse of information and analysis methods amongst researchers across the constituent disciplines. However, computer systems for the analysis and manipulation of musical data are generally not interoperable. Knowledge representation has been extensively used in the domain of music to harness the benefits of formal conceptual modelling combined with logic based automated inference. However, the available knowledge representation languages lack sufficient logical expressivity to support sophisticated musicological concepts. In this thesis we present a type-based framework for abstract representation of musical knowledge. The core of the framework is a multiple-hierarchical information model called a constituent structure, which accommodates diverse kinds of musical information. The framework includes a specification logic for expressing formal descriptions of the components of the representation. We give a formal specification for the framework in the Calculus of Inductive Constructions, an expressive logical language which lends itself to the abstract specification of data types and information structures. We give an implementation of our framework using Semantic Web ontologies and JavaScript. The ontologies capture the core structural aspects of the representation, while the JavaScript tools implement the functionality of the abstract specification. We describe how our framework supports three music analysis tasks: pattern search and discovery, paradigmatic analysis and hierarchical set-class analysis, detailing how constituent structures are used to represent both the input and output of these analyses including sophisticated structural annotations. We present a simple demonstrator application, built with the JavaScript tools, which performs simple analysis and visualisation of linked data documents structured by the ontologies. We conclude with a summary of the contributions of the thesis and a discussion of the type-based approach to knowledge representation, as well as a number of avenues for future work in this area
Enhancing the correctness of BPMN models
While some of the OMG's metamodels include a formal specification of well-formedness rules, using OCL, the BPMN metamodel specification only includes those rules in natural language. Although several BPMN tools claim to support, at least partly, the OMG's BPMN specification, we found that the mainstream of BPMN tools do not enforce most of the prescribed BPMN rules. Furthermore, the verification of BPMN process models publicly available showed that a relevant percentage of those BPMN process models fail in complying with the well-formedness rules of the BPMN specification. The enforcement of process model's correctness is relevant for the sake of better quality of process modeling and to attain models amenable of being enacted. In this chapter we propose supplement the BPMN metamodel with well-formedness rules expressed as OCL invariants in order to enforce BPMN models' correctness.info:eu-repo/semantics/acceptedVersio
Combining quantitative and qualitative reasoning in defeasible argumentation
Labeled Deductive Systems (LDS) were developed as a rigorous but exible method- ology to formalize complex logical systems, such as temporal logics, database query languages and defeasible reasoning systems.
LDSAR is a LDS-based framework for defeasible argumentation which subsumes di erent existing argumentation frameworks, providing a testbed for the study of dif- ferent relevant features (such as logical properties and ontological aspects, among others).
This paper presents LDS AR, an extension of LDSAR that incorporates the ability to combine quantitative and qualitative features within a uni ed argumentative setting.
Our approach involves the assignment of certainty factors to formulas in the knowl- edge base. These values are propagated when performing argumentative inference, o ering an alternative source of information for evaluating the strength of arguments in the dialectical analysis. We will also discuss some emerging logical properties of the resulting framework.Eje: Lógica e Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI
Shape Expressions Schemas
We present Shape Expressions (ShEx), an expressive schema language for RDF
designed to provide a high-level, user friendly syntax with intuitive
semantics. ShEx allows to describe the vocabulary and the structure of an RDF
graph, and to constrain the allowed values for the properties of a node. It
includes an algebraic grouping operator, a choice operator, cardinalitiy
constraints for the number of allowed occurrences of a property, and negation.
We define the semantics of the language and illustrate it with examples. We
then present a validation algorithm that, given a node in an RDF graph and a
constraint defined by the ShEx schema, allows to check whether the node
satisfies that constraint. The algorithm outputs a proof that contains
trivially verifiable associations of nodes and the constraints that they
satisfy. The structure can be used for complex post-processing tasks, such as
transforming the RDF graph to other graph or tree structures, verifying more
complex constraints, or debugging (w.r.t. the schema). We also show the
inherent difficulty of error identification of ShEx
Combining quantitative and qualitative reasoning in defeasible argumentation
Labeled Deductive Systems (LDS) were developed as a rigorous but exible method- ology to formalize complex logical systems, such as temporal logics, database query languages and defeasible reasoning systems.
LDSAR is a LDS-based framework for defeasible argumentation which subsumes di erent existing argumentation frameworks, providing a testbed for the study of dif- ferent relevant features (such as logical properties and ontological aspects, among others).
This paper presents LDS AR, an extension of LDSAR that incorporates the ability to combine quantitative and qualitative features within a uni ed argumentative setting.
Our approach involves the assignment of certainty factors to formulas in the knowl- edge base. These values are propagated when performing argumentative inference, o ering an alternative source of information for evaluating the strength of arguments in the dialectical analysis. We will also discuss some emerging logical properties of the resulting framework.Eje: Lógica e Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI
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