Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

Outcome after cardiac arrest and cardiopulmonary resuscitation is dependent on critical interventions, particularly early defibrillation, effective chest compressions, and advanced life support. Utstein-style definitions and reporting templates have been used extensively in published studies of cardiac arrest, which has led to greater understanding of the elements of resuscitation practice and progress toward international consensus on science and resuscitation guidelines. Despite the development of Utstein templates to standardize research reports of cardiac arrest, international registries have yet to be developed. In April 2002, a task force of the International Liaison Committee on Resuscitation (ILCOR) met in Melbourne, Australia, to review worldwide experience with the Utstein definitions and reporting templates. The task force revised the core reporting template and definitions by consensus. Care was taken to build on previous definitions, changing data elements and operational definitions only on the basis of published data and experience derived from those registries that have used Utstein-style reporting. Attention was focused on decreasing the complexity of the existing templates and addressing logistical difficulties in collecting specific core and supplementary (ie, essential and desirable) data elements recommended by previous Utstein consensus conferences. Inconsistencies in terminology between in-hospital and out-of-hospital Utstein templates were also addressed. The task force produced a reporting tool for essential data that can be used for both quality improvement (registries) and research reports and that should be applicable to both adults and children. The revised and simplified template includes practical and succinct operational definitions. It is anticipated that the revised template will enable better and more accurate completion of all reports of cardiac arrest and resuscitation attempts. Problems with data definition, collection, linkage, confidentiality, management, and registry implementation are acknowledged and potential solutions offered. Uniform collection and tracking of registry data should enable better continuous quality improvement within every hospital, emergency medical services system, and community.

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Cardiac arrest and cardiopulmonary resuscitation outcome reports: update and simplification of the Utstein templates for resuscitation registries. A statement for healthcare professionals from a task force of the international liaison committee on resuscitation (American Heart Association, European Resuscitation Council, Australian Resuscitation Council, New Zealand Resuscitation Council, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Council of Southern Africa)

Often, in the real world, entities have two or more representations in databases. Duplicate records do not share a common key and/or they contain errors that make duplicate matching a difficult task. Errors are introduced as the result of transcription errors, incomplete information, lack of standard formats, or any combination of these factors. In this paper, we present a thorough analysis of the literature on duplicate record detection. We cover similarity metrics that are commonly used to detect similar field entries, and we present an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database. We also cover multiple techniques for improving the efficiency and scalability of approximate duplicate detection algorithms. We conclude with coverage of existing tools and with a brief discussion of the big open problems in the area

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Duplicate Record Detection: A Survey

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Using an open-source, Java toolkit of name-matching methods, we experimentally compare string distance metrics on the task of matching entity names We investigate a number of different metrics proposed by different communities, including edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods Overall, the best-performing method is a hybrid scheme combining a TFIDF weighting scheme, which is widely used in information retrieval, with the Jaro-Winkler string-distance scheme, which was developed in the probabilistic record linkage community

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A comparison of string distance metrics for name-matching tasks

A test census of Tampa, Florida and an independent postenumeration survey (PES) were conducted by the U.S. Census Bureau in 1985. The PES was a stratified block sample with heavy emphasis placed on hard-to-count population groups. Matching the individuals in the census to the individuals in the PES is an important aspect of census coverage evaluation and consequently a very important process for any census adjustment operations that might be planned. For such an adjustment to be feasible, record-linkage software had to be developed that could perform matches with a high degree of accuracy and that was based on an underlying mathematical theory. A principal purpose of the PES was to provide an opportunity to evaluate the newly implemented record-linkage system and associated methodology. This article discusses the theoretical and practical issues encountered in conducting the matching operation and presents the results of that operation. A review of the theoretical background of the record-linkage...

Advances in Record-Linkage Methodology as Applied to Matching the 1985 Census of Tampa, Florida

Probabilistic linkage technology makes it feasible and efficient to link large public health databases in a statistically justifiable manner. The problem addressed by the methodology is that of matching two files of individual data under conditions of uncertainty. Each field is subject to error which is measured by the probability that the field agrees given a record pair matches (called the m probability) and probabilities of chance agreement of its value states (called the u probability). Fellegi and Sunter pioneered record linkage theory. Advances in methodology include use of an EM algorithm for parameter estimation, optimization of matches by means of a linear sum assignment program, and more recently, a probability model that addresses both m and u probabilities for all value states of a field. This provides a means for obtaining greater precision from non-uniformly distributed fields, without the theoretical complications arising from frequency-based matching alone. The model includes an interative parameter estimation procedure that is more robust than pre-match estimation techniques. The methodology was originally developed and tested by the author at the U.S. Census Bureau for census undercount estimation. The more recent advances and a new generalized software system were tested and validated by linking highway crashes to Emergency Medical Service (EMS) reports and to hospital admission records for the National Highway Traffic Safety Administration (NHTSA).

Matthew A. Jaro

Papers

Advances in Record-Linkage Methodology as Applied to Matching the 1985 Census of Tampa, Florida

Probabilistic linkage of large public health data files