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

Effective Retrieval of Resources in Folksonomies Using a New Tag Similarity Measure

Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. However, as tags are informally defined, continually changing, and ungoverned, it has often been criticised for lowering, rather than increasing, the efficiency of searching. To address this issue, a variety of approaches have been proposed that recommend users what tags to use, both when labeling and when looking for resources. These techniques work well in dense folksonomies, but they fail to do so when tag usage exhibits a power law distribution, as it often happens in real-life folksonomies. To tackle this issue, we propose an approach that induces the creation of a dense folksonomy, in a fully automatic and transparent way: when users label resources, an innovative tag similarity metric is deployed, so to enrich the chosen tag set with related tags already present in the folksonomy. The proposed metric, which represents the core of our approach, is based on the mutual reinforcement principle. Our experimental evaluation proves that the accuracy and coverage of searches guaranteed by our metric are higher than those achieved by applying classical metrics.Comment: 6 pages, 2 figures, CIKM 2011: 20th ACM Conference on Information and Knowledge Managemen

Risk of Guillain-Barr\ue9 syndrome after 2010-2011 influenza vaccination

Influenza vaccination has been implicated in Guillain Barr\ue9 Syndrome (GBS) although the evidence for this link is controversial. A case-control study was conducted between October 2010 and May 2011 in seven Italian Regions to explore the relation between influenza vaccination and GBS. The study included 176 GBS incident cases aged 6518 years from 86 neurological centers. Controls were selected among patients admitted for acute conditions to the Emergency Department of the same hospital as cases. Each control was matched to a case by sex, age, Region and admission date. Two different analyses were conducted: a matched case-control analysis and a self-controlled case series analysis (SCCS). Case-control analysis included 140 cases matched to 308 controls. The adjusted matched odds ratio (OR) for GBS occurrence within 6 weeks after influenza vaccination was 3.8 (95 % CI: 1.3, 10.5). A much stronger association with gastrointestinal infections (OR = 23.8; 95 % CI 7.3, 77.6) and influenza-like illness or upper respiratory tract infections (OR = 11.5; 95 % CI 5.6, 23.5) was highlighted. The SCCS analysis included all 176 GBS cases. Influenza vaccination was associated with GBS, with a relative risk of 2.1 (95 % CI 1.1, 3.9). According to these results the attributable risk in adults ranges from two to five GBS cases per 1,000,000 vaccinations