Catálogo de publicaciones - libros

Many problems in Information and Data Management require a semantic account of a database schema. At its best, such an account consists of formulas expressing the relationship (“mapping”) between the schema and a formal conceptual model or ontology (CM) of the domain. In this paper we describe the underlying principles, algorithms, and a prototype tool that finds such semantic mappings from relational tables to ontologies, when given as input simple correspondences from columns of the tables to datatype properties of classes in an ontology. Although the algorithm presented is necessarily heuristic, we offer formal results showing that the answers returned by the tool are “correct” for relational schemas designed according to standard Entity-Relationship techniques. To evaluate its usefulness and effectiveness, we have applied the tool to a number of public domain schemas and ontologies. Our experience shows that significant effort is saved when using it to build semantic mappings from relational tables to ontologies.

This paper describes two different ways of understanding operation contracts in conceptual modeling: the strict and extended interpretations. The main difference between them lies in the way operation postconditions and integrity constraints are guaranteed, which has an impact on the desirable properties of operation contracts according to recommended good practice for requirements specification. Both interpretations are formalized and compared in terms of these properties. We find that the strict interpretation provides several advantages over the extended one. This conclusion is supported by evidence from a case study. The results of the case study also indicate that the strict interpretation significantly facilitates the specification task.

W3C’s Semantic Web provides a common framework that allows data to be shared and reused across application and enterprise. As the Semantic Web shapes the future of the Web, it becomes more and more important in software engineering and enterprise application development. While existing ontology engineering tools provide a stack of ontology management support and are used successfully in certain domains, there still remains a gap between the ontology engineering tools and the traditional software engineering. For several decades, software engineering has been established on different modeling languages and methodologies such as Unified Modeling Language (UML). The differences in modeling languages and methodologies cause difficulties in enterprise application development involving the Semantic Web technologies. The existing ontology engineering tools provide only an ad hoc approach to bridging this gap with limited functionality and performance. The primary objective of our work is to bridge this gap between two different, but complementary engineering disciplines with a systematic approach. Our approach leverages Model-Driven Architecture (MDA) and Ontology Definition Metamodel (ODM), which enable model transformation. This approach allows seamlessly supporting existing models in UML and other languages in Semantic Web-based software development. In addition, it allows exploiting the availability and features of UML tools for creation of vocabularies and ontologies. Furthermore, MDA enables code generation and facilitates software tool development. This paper presents an MDA-based system for ontology engineering. In addition, it presents the entire stack of individual components of the developed ontology engineering tool.

Knowledge representation languages that combine rules with object-oriented features akin to frame systems have recently attracted a lot of research interest, and F-logic is widely seen as a basis to achieve this integration. In this paper we extend the original F-logic formalism with an array of salient features that are essential for representing and reasoning with commonsense knowledge. In particular, we extend the syntax and semantics of F-logic to incorporate nonmonotonic multiple inheritance of class and instance methods in the presentence of class hierarchies defined via rules. The new semantics is completely model-theoretic and is free of the defects that caused the original F-logic to produce unintuitive results due to the unusual interaction between default inheritance and inference via rules. Moreover, we provide a computational framework for the new F-logic semantics which can be implemented by inference engines using either forward or backward chaining mechanisms.

The problem of identifying objects in databases that refer to the same real world entity, is known, among others, as duplicate detection or record linkage. Objects may be duplicates, even though they are not identical due to errors and missing data. Typical current methods require deep understanding of the application domain or a good representative training set, which entails significant costs. In this paper we present an unsupervised, domain independent approach to duplicate detection that starts with a broad alignment of potential duplicates, and analyses the distribution of observed similarity values among these potential duplicates and among representative sample non-duplicates to improve the initial alignment. Additionally, the presented approach is not only able to align flat records, but makes also use of related objects, which may significantly increase the alignment accuracy. Evaluations show that our approach supersedes other unsupervised approaches and reaches almost the same accuracy as even fully supervised, domain dependent approaches.

XML-based databases have become a major area of interest in database research. Abstractly speaking they can be considered as a resurrection of complex-value databases using constructors for records, lists, unions plus optionality and references. XQuery has become the standard query language for XML. As XQuery is a declarative query language, the problem of query optimisation arises. In this paper an algebraic approach to query optimisation is introduced. This is based on a translation of XQuery into a query algebra for rational tree types. The algebra uses simple operations on types and structural recursion for lists. The translation exploits linguistic reflection for the type-safe expansion of path expressions. The availability of an algebraic representation of queries permits query rewriting, which in combination with cost heuristics permits queries to be rewritten and thus optimised.

In the last two decades, images are quite produced in increasing amounts in several application domains. In medicine, for instance, a large number of images of various imaging modalities (e.g. computer tomography, magnetic resonance, nuclear imaging, etc.) are produced daily to support clinical decision-making. Thereby, a fully functional Image Management System becomes a requirement to the end-users. In spite of current researches, the practice has proved that the problem of image management is highly related to image representation. This paper contribution is twofold in facilitating the representation of images and the extraction of its content and context descriptors. In fact, we introduce an expressiveness and extendable XML-based meta-model able to capture the metadata and content-based of images. We also propose an information extraction approach to provide automatic description of image content using related metadata. It automatically generates XML instances, which mark up metadata and salient objects matched by extraction patterns. In this paper, we illustrate our proposal by using the medical domain of lungs x-rays and we show our first experimental results.

There has been a rapid growth in the use of semistructured data in both web applications and database systems. Consequently, the design of a good semistructured data model is essential. In the relational database community, algorithms have been defined to transform a relational schema from one normal form to a more suitable normal form. These algorithms have been shown to preserve certain semantics during the transformation. The work presented in this paper is the first step towards representing such algorithms for semistructured data, namely formally defining the semantics necessary for achieving this goal. Formal semantics and automated reasoning tools enable us to reveal the inconsistencies in a semistructured data model and its instances. The Object Relationship Attribute model for Semistructured data (ORA-SS) is a graphical notation for designing and representing semistructured data. This paper presents a methodology of encoding the semantics of the ORA-SS notation into the Web Ontology Language (OWL) and automatically verifying the semistructured data design using the OWL reasoning tools. Our methodology provides automated consistency checking of an ORA-SS data model at both the schema and instance levels.

Recently researchers have tried to apply ontology to the product information domain. From a practical point of view, a key problem to streamline this trend is how to make a product ontology database operational. Technical solutions should consider the characteristics that a pragmatic product ontology database contains; first, the database size is quite huge, and second, ontological manipulation and utilization should be realistically feasible. We recently engaged in a project to build an operational product ontology system. The system is designed to serve as a product ontology knowledge base, not only for the design and construction of product databases but also for the search and discovery of products. From the insights gained through this project, we believe that ontological modeling and its implementation on an operational database, as well as the building applications which exploit ontological benefits, are the most important facets towards the successful deployment of a practical product ontology system. As such, searching techniques should take into account the features of an underlying ontological model especially with product searching being one of the most popular applications within product information systems. In this paper, we present these two issues; product ontology modeling and searching techniques. Although our work presented herein may not be the only way to build an operational product ontology database, it may serve as an important reference model for similar projects in future.