2,452 research outputs found
XML Matchers: approaches and challenges
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 well-known
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.Comment: 34 pages, 8 tables, 7 figure
Bioinformatics service reconciliation by heterogeneous schema transformation
This paper focuses on the problem of bioinformatics service reconciliation in a generic and scalable manner so as to enhance interoperability in a highly evolving field. Using XML as a common representation format, but also supporting existing flat-file representation formats, we propose an approach for the scalable semi-automatic reconciliation of services, possibly invoked from within a scientific workflows tool. Service reconciliation may use the AutoMed heterogeneous data integration system as an intermediary service, or may use AutoMed to produce services that mediate between services. We discuss the application of our approach for the reconciliation of services in an example bioinformatics workflow. The main contribution of this research is an architecture for the scalable reconciliation of bioinformatics services
Taming Data Explosion in Probabilistic Information Integration
Data integration has been a challenging problem for decades. In an ambient environment, where many autonomous devices have their own information sources and network connectivity is ad hoc and peer-to-peer, it even becomes a serious bottleneck. To enable devices to exchange information without the need for interaction with a user at data integration time and without the need for extensive semantic annotations, a probabilistic approach seems rather promising. It simply teaches the device how to cope with the uncertainty occurring during data integration. Unfortunately, without any kind of world knowledge, almost everything becomes uncertain, hence maintaining all possibilities produces huge integrated information sources. In this paper, we claim that only very simple and generic rules are enough world knowledge to drastically reduce the amount of uncertainty, hence to tame the data explosion to a manageable size
MeLinDa: an interlinking framework for the web of data
The web of data consists of data published on the web in such a way that they
can be interpreted and connected together. It is thus critical to establish
links between these data, both for the web of data and for the semantic web
that it contributes to feed. We consider here the various techniques developed
for that purpose and analyze their commonalities and differences. We propose a
general framework and show how the diverse techniques fit in the framework.
From this framework we consider the relation between data interlinking and
ontology matching. Although, they can be considered similar at a certain level
(they both relate formal entities), they serve different purposes, but would
find a mutual benefit at collaborating. We thus present a scheme under which it
is possible for data linking tools to take advantage of ontology alignments.Comment: N° RR-7691 (2011
An information retrieval approach to ontology mapping
In this paper, we present a heuristic mapping method and a prototype mapping system that support the process of semi-automatic ontology mapping for the purpose of improving semantic interoperability in heterogeneous systems. The approach is based on the idea of semantic enrichment, i.e., using instance information of the ontology to enrich the original ontology and calculate similarities between concepts in two ontologies. The functional settings for the mapping system are discussed and the evaluation of the prototype implementation of the approach is reported. \ud
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Managing uncertainty of XML schema matching
Despite of advances in machine learning technologies, a schema matching result between two database schemas (e.g., those derived from COMA++) is likely to be imprecise. In particular, numerous instances of "possible mappings" between the schemas may be derived from the matching result. In this paper, we study the problem of managing possible mappings between two heterogeneous XML schemas. We observe that for XML schemas, their possible mappings have a high degree of overlap. We hence propose a novel data structure, called the block tree, to capture the commonalities among possible mappings. The block tree is useful for representing the possible mappings in a compact manner, and can be generated efficiently. Moreover, it supports the evaluation of probabilistic twig query (PTQ), which returns the probability of portions of an XML document that match the query pattern. For users who are interested only in answers with k-highest probabilities, we also propose the top-k PTQ, and present an efficient solution for it. The second challenge we have tackled is to efficiently generate possible mappings for a given schema matching. While this problem can be solved by existing algorithms, we show how to improve the performance of the solution by using a divide-andconquer approach. An extensive evaluation on realistic datasets show that our approaches significantly improve the efficiency of generating, storing, and querying possible mappings. © 2010 IEEE.published_or_final_versionThe IEEE 26th International Conference on Data Engineering (ICDE 2010), Long Beach, CA., 1-6 March 2010. In International Conference on Data Engineering. Proceedings, 2010, p. 297-30
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