Automatic Normalization and Annotation for Discovering Semantic Mappings

Schema matching is the problem of finding relationships among concepts across heterogeneous data sources (heterogeneous in format and in structure). Starting from the “hidden meaning” associated to schema labels (i.e. class/attribute names) it is possible to discover relationships among the elements of different schemata. Lexical annotation (i.e. annotation w.r.t. a thesaurus/lexical resource) helps in associating a “meaning” to schema labels. However, accuracy of semi-automatic lexical annotation methods on real-world schemata suffers from the abundance of non-dictionary words such as compound nouns and word abbreviations. In this work, we address this problem by proposing a method to perform schema labels normalization which increases the number of comparable labels. Unlike other solutions, the method semi-automatically expands abbreviations and annotates compound terms, without a minimal manual effort. We empirically prove that our normalization method helps in the identification of similarities among schema elements of different data sources, thus improving schema matching accuracy

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

Schema Label Normalization for Improving Schema Matching

Schema matching is the problem of finding relationships among concepts across heterogeneous data sources that are heterogeneous in format and in structure. Starting from the \u201chidden meaning\u201d associated with schema labels (i.e. class/attribute names) it is possible to discover relationships among the elements of different schemata. Lexical annotation (i.e. annotation w.r.t. a thesaurus/lexical resource) helps in associating a \u201cmeaning\u201d to schema labels.However, the performance of semi-automatic lexical annotation methods on real-world schemata suffers from the abundance of non-dictionary words such as compound nouns, abbreviations, and acronyms. We address this problem by proposing a method to perform schema label normalization which increases the number of comparable labels. The method semi-automatically expands abbreviations/acronyms and annotates compound nouns, with minimal manual effort. We empirically prove that our normalization method helps in the identification of similarities among schema elements of different data sources, thus improving schema matching results

Construcción de un operador de Matching Ontológico para los niveles de representación semántico y semiótico

Este trabajo describe el diseño de un operador matching ontológico aplicable en el modelo conceptual propuesto por Chavarro (Chavarro, 2012), un diseño de cuatro capas que permite la configuración del operador de diferentes tipos de técnicas tanto a nivel de elemento como a nivel de estructura. El objetivo de este diseño es la creación de matcher ontológico de alto nivel adaptables a las necesidades de las ontologías con las cuales opera. Este trabajo presenta las primeras fases de construcción del operador y sus resultados preliminares

Construcción de un operador de Matching Ontológico para los niveles de representación semántico y semiótico

Este trabajo describe el diseño de un operador matching ontológico aplicable en el modelo conceptual propuesto por Chavarro (Chavarro, 2012), un diseño de cuatro capas que permite la configuración del operador de diferentes tipos de técnicas tanto a nivel de elemento como a nivel de estructura. El objetivo de este diseño es la creación de matcher ontológico de alto nivel adaptables a las necesidades de las ontologías con las cuales opera. Este trabajo presenta las primeras fases de construcción del operador y sus resultados preliminares

Semantic matching with S-Match

We view matching as an operation that takes two graph-like structures (e.g., lightweight ontologies) and produces an alignment between the nodes of these graphs that correspond semantically to each other. Semantic matching is based on two ideas: (i) we discover an alignment by computing semantic relations (e.g., equivalence, more general); (ii) we determine semantic relations by analyzing the meaning (concepts, not labels) which is codied in the entities and the structures of ontologies. In this chapter we first overview the state of the art in the ontology matching eld. Then, we present basic and optimized algorithms for semantic matching as well as their implementation within the S-Match system. Finally, we evaluate S-Match against state of the art systems, thereby justifying empirically the strength of the approach. To appear in Roberto De Virgilio, Fausto Giunchiglia, and Letizia Tanca (eds.), "Semantic Web Information Management: a model based perspective", Springer, 2009. - http://www.springerlink.co