1,934 research outputs found
A UML/OCL framework for the analysis of fraph transformation rules
In this paper we present an approach for the analysis of graph transformation rules based on an intermediate OCL representation. We translate different rule semantics into OCL, together with the properties of interest (like rule applicability, conflicts or independence). The intermediate representation serves three purposes: (i) it allows the seamless integration of graph transformation rules with the MOF and OCL standards, and enables taking the meta-model and its OCL constraints (i.e. well-formedness rules) into account when verifying the correctness of the rules; (ii) it permits the interoperability of graph transformation concepts with a number of standards-based model-driven development tools; and (iii) it makes available a plethora of OCL tools to actually perform the rule analysis. This approach is especially useful to analyse the operational semantics of Domain Specific Visual Languages. We have automated these ideas by providing designers with tools for the graphical specification and analysis of graph transformation rules, including a backannotation mechanism that presents the analysis results in terms of the original language notation
Acquiring Word-Meaning Mappings for Natural Language Interfaces
This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted
Examples), that acquires a semantic lexicon from a corpus of sentences paired
with semantic representations. The lexicon learned consists of phrases paired
with meaning representations. WOLFIE is part of an integrated system that
learns to transform sentences into representations such as logical database
queries. Experimental results are presented demonstrating WOLFIE's ability to
learn useful lexicons for a database interface in four different natural
languages. The usefulness of the lexicons learned by WOLFIE are compared to
those acquired by a similar system, with results favorable to WOLFIE. A second
set of experiments demonstrates WOLFIE's ability to scale to larger and more
difficult, albeit artificially generated, corpora. In natural language
acquisition, it is difficult to gather the annotated data needed for supervised
learning; however, unannotated data is fairly plentiful. Active learning
methods attempt to select for annotation and training only the most informative
examples, and therefore are potentially very useful in natural language
applications. However, most results to date for active learning have only
considered standard classification tasks. To reduce annotation effort while
maintaining accuracy, we apply active learning to semantic lexicons. We show
that active learning can significantly reduce the number of annotated examples
required to achieve a given level of performance
A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge
We present the architecture and the evaluation of a new system for
recognizing textual entailment (RTE). In RTE we want to identify automatically
the type of a logical relation between two input texts. In particular, we are
interested in proving the existence of an entailment between them. We conceive
our system as a modular environment allowing for a high-coverage syntactic and
semantic text analysis combined with logical inference. For the syntactic and
semantic analysis we combine a deep semantic analysis with a shallow one
supported by statistical models in order to increase the quality and the
accuracy of results. For RTE we use logical inference of first-order employing
model-theoretic techniques and automated reasoning tools. The inference is
supported with problem-relevant background knowledge extracted automatically
and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or
other, more experimental sources with, e.g., manually defined presupposition
resolutions, or with axiomatized general and common sense knowledge. The
results show that fine-grained and consistent knowledge coming from diverse
sources is a necessary condition determining the correctness and traceability
of results.Comment: 25 pages, 10 figure
Verifying context-sensitive treebanks and heuristic parses in polynomial time
Proceedings of the 17th Nordic Conference of Computational Linguistics
NODALIDA 2009.
Editors: Kristiina Jokinen and Eckhard Bick.
NEALT Proceedings Series, Vol. 4 (2009), 190-197.
© 2009 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/9206
Controlling Reuse in Pattern-Based Model-to-Model Transformations
Model-to-model transformation is a central activity in Model-Driven Engineering that consists of transforming models from a source to a target language. Pattern-based model-to-model transformation is our approach for specifying transformations in a declarative, relational and formal style. The approach relies on patterns describing allowed or forbidden relations between two models. These patterns are compiled into operational mechanisms to perform forward and backward transformations. Inspired by QVT-Relations, in this paper we incorporate into our framework the so-called check-before-enforce semantics, which checks the existence of suitable elements before creating them (i.e. it promotes reuse). Moreover, we enable the use of keys in order to describe when two elements are considered equal. The presented techniques are illustrated with a bidirectional transformation between Web Services Description Language and Enterprise Java Beans models.Work partially supported by the Spanish Ministry of Science
and Innovation, with projects METEORIC (TIN2008-02081) and FORMALISM
(TIN2007-66523), and the R&D program of the Community of Madrid
(S2009/TIC-1650, project “e-Madrid”). Moreover, part of this work was done
during a post-doctoral stay of the first author at the University of York, and sabbatical
leaves of the second and third authors to the University of York and TU Berlin respectively, all with financial support from the Spanish Ministry of Science
and Innovation (grant refs. JC2009-00015, PR2009-0019 and PR2008-0185).Publicad
Algebraic optimization of recursive queries
Over the past few years, much attention has been paid to deductive databases. They offer a logic-based interface, and allow formulation of complex recursive queries. However, they do not offer appropriate update facilities, and do not support existing applications. To overcome these problems an SQL-like interface is required besides a logic-based interface.\ud
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In the PRISMA project we have developed a tightly-coupled distributed database, on a multiprocessor machine, with two user interfaces: SQL and PRISMAlog. Query optimization is localized in one component: the relational query optimizer. Therefore, we have defined an eXtended Relational Algebra that allows recursive query formulation and can also be used for expressing executable schedules, and we have developed algebraic optimization strategies for recursive queries. In this paper we describe an optimization strategy that rewrites regular (in the context of formal grammars) mutually recursive queries into standard Relational Algebra and transitive closure operations. We also describe how to push selections into the resulting transitive closure operations.\ud
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The reason we focus on algebraic optimization is that, in our opinion, the new generation of advanced database systems will be built starting from existing state-of-the-art relational technology, instead of building a completely new class of systems
A Model Driven Approach to Model Transformations
The OMG's Model Driven Architecture (MDA) initiative has been the focus of much attention in both academia and industry, due to its promise of more rapid and consistent software development through the increased use of models. In order for MDA to reach its full potential, the ability to manipulate and transform models { most obviously from the Platform Independent Model (PIM) to the Platform Specific Models (PSM) { is vital. Recognizing this need, the OMG issued a Request For Proposals (RFP) largely concerned with finding a suitable mechanism for trans-
forming models. This paper outlines the relevant background material, summarizes the approach taken by the QVT-Partners (to whom the authors belong), presents a non-trivial example using the QVT-Partners approach, and finally sketches out what the future holds for model transformations
Security Applications of Formal Language Theory
We present an approach to improving the security of complex, composed systems based on formal language theory, and show how this approach leads to advances in input validation, security modeling, attack surface reduction, and ultimately, software design and programming methodology. We cite examples based on real-world security flaws in common protocols representing different classes of protocol complexity. We also introduce a formalization of an exploit development technique, the parse tree differential attack, made possible by our conception of the role of formal grammars in security. These insights make possible future advances in software auditing techniques applicable to static and dynamic binary analysis, fuzzing, and general reverse-engineering and exploit development.
Our work provides a foundation for verifying critical implementation components with considerably less burden to developers than is offered by the current state of the art. It additionally offers a rich basis for further exploration in the areas of offensive analysis and, conversely, automated defense tools and techniques.
This report is divided into two parts. In Part I we address the formalisms and their applications; in Part II we discuss the general implications and recommendations for protocol and software design that follow from our formal analysis
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