Structural and semantic aspects of similarity of Document Type Definitions and XML schemas

Wojnar, Aleš; Mlýnková, Irena; Dokulil, Jiri

doi:10.1016/j.ins.2009.12.024

Cited by 19 publications

(21 citation statements)

References 45 publications

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“…In compliance with Definition 1, the output of an XML Matcher could be a real number in [0, 1], stating the semantic similarity of the corresponding schema elements so that the higher this number the more likely they form a matching. Other authors [92,100], instead, conceive XML Matchers as tools to compute the similarity degree of two DTDs/XSDs; this degree is a numerical value stating to what extent the semantics captured by the two schemas overlap. A nice application of such a definition can be found in the BellFlower system [92].…”

Section: A Template To Classify Xml Matchersmentioning

confidence: 99%

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XML Matchers: Approaches and challenges

Agreste

Meo

Ferrara

et al. 2014

Knowledge-Based Systems

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Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take wellknown techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.To make data exchange easier, organizations like the World Wide Web Consortium (W3C) are increasingly committed to the definition of advanced languages to describe the structure and content of an XML data source. One of the first languages was XML DTD (Document Type Definition) [34]. Later, W3C developed a more complex language called XSD (XML Schema Definition), also known as WXS (W3C Schema Definition), in order to overcome some limitations of DTD [101] 1 .These languages are used to build a schema, i.e. a collection of metadata called schema components or schema elements. A schema specifies a set of rules an XML document must obey in order to be considered valid according to the schema itself. The availability of a schema significantly simplifies data exchange procedures [25]. For instance, there exist simple software programs that can check if a given document satisfies the constraints imposed by a given schema, and, in the positive case, allow for seamless data exchange among the interested parties.Despite the presence of powerful languages like DTD/XSD, the achievement of the full interoperability among applications based on XML data is often illusory. DTDs/XSDs, in fact, provide self-desc...

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Section: A Template To Classify Xml Matchersmentioning

confidence: 99%

“…In XClust [62] and similar methods [100], the IFD is a set of DTDs. Each schema element in the original DTD is mapped onto a tree node, whereas an edge models a relationship between an element and one of its sub-elements.…”

Section: The Pre-processing Function Mmentioning

confidence: 99%

XML Matchers: Approaches and challenges

Agreste

Meo

Ferrara

et al. 2014

Knowledge-Based Systems

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“…In the XML view we have already proposed several approaches dealing with related problems such as XML schema inference [36,37,35] and mapping of XML schemas [58,28] . However, in case of other views (data formats) the related problems will probably be quite different and need to be explored and studied with respect to the different context.…”

Section: Future Workmentioning

confidence: 99%

DaemonX

Polák

Nečaský

Holubová

2013

Proceedings of International Conference on Information Integration and Web-Based Applications &Amp; Services

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The most common applications of the today's IT world are information systems. The problems related to their design and implementation have sufficiently been solved. However, the true problems occur when an IS is already deployed and user requirements change. Currently this situation requires a skilled IT expert who knows all system components and, hence, is able to identify and modify all the affected parts. However, not always we have such an expert, whereas for complex systems it is a very hard and error-prone task. In this paper we introduce DaemonX -an evolution management framework, which enables to manage evolution of complex applications efficiently and correctly. Using the idea of plug-ins, it enables to model almost any kind of a data format (currently XML, UML, ER, and BPMN). Since it preserves relationships among the modeled constructs, it naturally supports propagation of changes to all related affected parts. We describe the general proposal of the framework and, then, its architecture and implementation.

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“…In this study, semantic advertising is a process to semantically analyze web pages and advertisements to properly understand and classify web pages and advertisements in order to ensure that the web pages contain the most appropriate advertisements. Recently, many researchers have explored the semantics of documents to improve the efficacy of applications, such as data mapping [30], XML search [18] and recommender systems [27]. Since an ontology is a standardized representation of knowledge as a set of concepts within a domain, numerous studies have been proposed to construct ontologies [7] and utilize ontologies for web crawling [35], document clustering [34,36], e-learning [3], and web search [13], etc.…”

Section: Which Has Been Acquired By Google For 750 Million Dollarsmentioning

confidence: 99%