http://www.bibsonomy.org/burst/user/flint63/matchingBibSonomy BuRST Feed for /user/flint63/matching2008-08-21T05:20:22+02:00http://www.bibsonomy.org/bibtex/224255e55ce76a6fb52a4303a323475fb/flint63flint632008-07-07T13:47:29+02:00xml information ontology v0805 knowledge book ai matching engineering data <span style="color:#555555;">J'er^ome <a href="http://www.bibsonomy.org/author/Euzenat">Euzenat</a> and Pavel <a href="http://www.bibsonomy.org/author/Shvaiko">Shvaiko</a> </span><em>Springer, </em><em>Berlin, Heidelberg, </em>(<em>2007</em>)Mon Jul 07 13:47:29 CEST 2008Berlin, HeidelbergOntology Matching2007xml information ontology v0805 knowledge book ai matching engineering data Ontologies are viewed as the silver bullet for many applications: database integration, peer-to-peer systems, e-commerce, semantic web services, social networks and more. However, in open or evolving systems, different parties can adopt different ontologies. Instead of reducing heterogeneity, this raises heterogeneity problems to a higher level. This book proposes ontology matching as a solution to the problem of semantic heterogeneity. Ontology matching seeks correspondences between semantically related entities of different ontologies. In addition to equivalence, it explores relations such as consequence, subsumption, or disjointness between ontology entities. The book also describes a variety of matching tools: databases, information systems, artificial intelligence. Ontology Matching offers researchers and practitioners a reference to currently available work in a uniform framework. In particular, the techniques presented in this book can equally be applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting the state of the art, and presenting the latest research results in ontology matching by providing a logical account of matching techniques and matching systems from theoretical, practical and application perspectives.http://www.bibsonomy.org/bibtex/260f707ac25ad7153a633943f97ca04db/flint63flint632008-07-07T13:47:29+02:00v0805 book knowledge ontology data ai xml engineering alogorithm information matching <span style="color:#555555;">Hong-Hai <a href="http://www.bibsonomy.org/author/Do">Do</a> </span><em>Department of Computer Science, Universit&#228;t Leipzig, </em><em>Leipzig, Germany, </em>(<em>2006</em>)Mon Jul 07 13:47:29 CEST 2008Leipzig, GermanySchema Matching and Mapping-based Data IntegrationDissertation2006v0805 book knowledge ontology data ai xml engineering alogorithm information matching Abstract Schema matching aims at identifying semantic correspondences between elements of two schemas, e.g., database schemas, ontologies, and XML message formats. It is needed in many database applications, such as integration of web data sources, data warehouse loading and XML message mapping. In today's systems, schema matching is manual; a time-consuming, tedious, and error-prone process, which becomes increasingly impractical with a higher number of schemas and data sources to be dealt with. To reduce the amount of manual effort as much as possible, approaches to semi-automatically determine element correspondences are required. We start by surveying the existing approaches and prototypes for schema matching and explain their common features and applicability using a previously proposed taxonomy. We further identify the major criteria that influence the effectiveness of a match approach. We use these criteria to compare the evaluation of various recent prototypes and discuss the issues that need to be addressed in future evaluations. Besides helping us to develop and test our own system, the surveys of match approaches and of evaluations aim at guiding future implementations, so that they can be documented better, their result be more reproducible, and a comparison between different systems and approaches be easier. Based on the insights about the state of the art, we have developed Coma (Combining Matchers) and further extended it to Coma++, both representing generic and customizable systems for semi-automatic schema matching. In particular, Coma++ offers a platform for flexible combination of different match algorithms. It provides a large spectrum of individual matchers, including a novel approach reusing results from previous match operations, and various mechanisms to combine and refine matcher results. Based on this flexible infrastructure, match processing is supported as a workflow, allowing to divide and successively solve a match task in multiple stages. In particular, we implement specific workflows (i.e., strategies) for context-dependent matching of schemas with shared elements and fragment-based matching of very large schemas. With the flexibility to customize matchers and match strategies, Coma++ also represents a platform for comparative evaluation of match approaches. In fact, we performed comprehensive evaluations using real-world schemas found on the web and ontologies from a published ontology alignment contest. In particular, the E-business message standards involved in our evaluations are among the largest and most complex test schemas as compared to previous evaluations. Coma++ has shown high quality and fast execution time for both the schemas and ontologies, proving the practicability of our generic solution for different domains. Especially, the quality of Coma++ in the ontology alignment contest is comparable to that of the best performing participants. Due to the systematic evaluation, we obtain important insights on the performance of different match strategies and the impact of many factors, such as schema size, the choice of matchers and combination strategies, and the reuse of previous match results. We believe that our insights can be of valuable help for the development and evaluation of further match algorithms. Building on the same idea of reusing previous match results, we have developed Genmapper (Generic Mapper), a new approach for integrating heterogeneous web data sources. It utilizes mappings between sources and utilizes correspondences between their objects, i.e., at the instance level. We focus on the bioinformatics domain with hundreds of publicly accessible, highly cross-referenced web data sources managing annotations and correspondences for various types of molecular-biological objects, such as genes and proteins. Genmapper explicitly captures existing relationships between objects to drive data integration and combine annotation knowledge from different sources. A generic schema is used to uniformly represent object data and correspondences, making it easy to integrate new data sources and to update existing ones. To serve specific analysis needs, powerful operators are provided to derive tailored views from the generic data representation. Genmapper has been successfully used for large-scale functional profiling of genes and proteins.http://www.bibsonomy.org/bibtex/2ab6448cb3a5204cca67ed599ce4cea31/flint63flint632008-07-07T13:47:29+02:00xml ontology information ai knowledge alogorithm v0805 matching data book engineering <span style="color:#555555;">Hong-Hai <a href="http://www.bibsonomy.org/author/Do">Do</a> </span><em>Vdm Verlag Dr. M&#252;ller, </em><em>Saarbr&#252;cken, </em>(<em>2006</em>)Mon Jul 07 13:47:29 CEST 2008SaarbrückenSchema Matching and Mapping-based Data Integration: Architecture, Approaches and Evaluation2006xml ontology information ai knowledge alogorithm v0805 matching data book engineering he book focuses on Schema Matching, the task of (semi-)automatically identifying semantic correspondences between elements of metadata structures, such as, database schemas, ontologies, and XML message formats. It is of key importance for interoperability and data integration in numerous applications, such as data warehousing, integration of web-sources, message mapping in E-business, and ontology alignment on the Semantic Web. However, in today's systems, schema matching is still manual; a time-consuming, tedious, and error-prone process, which becomes increasingly impractical considering the high complexity and number of schemas and data sources to be dealt with. In this book, the author Do Hong Hai describes the architecture, functionality, and evaluation of the schema matching system COMA++ (Combining Matchers), which was developed by himself in his Ph.d thesis. COMA++ represents a generic and customizable system for semi-automatic schema matching, which can combine different match algorithms in a flexible way. In comprehensive evaluations using large real-world schemas and ontologies, COMA++ has shown high quality as compared to the state of the art, proving its practicability for different domains. In addition, the book describes a new data integration approach, GenMapper (Generic Mapper), which utilizes instance-level correspondences between objects of data sources.