Schema matching is one of the main challenges in different information system integration contexts. Over the past 20 years, different schema matching methods have been proposed and shown to be successful in various situations. Although numerous advanced matching algorithms have emerged, schema matching research remains a critical issue. Different algorithms are implemented to resolve different ...

To date, the principal use case for schema matching research has been as a precursor for code generation, i.e., constructing mappings between schema elements with the end goal of data transfer. In this paper, we argue that schema matching plays valuable roles independent of mapping construction, especially as schemata grow to industrial scales. Specifically, in large enterprises human decision ...

Over the years, many schema matching approaches have been developed to discover correspondences between schemas. Although this task is crucial in data integration, its evaluation, both in terms of matching quality and time performance, is still manually performed. Indeed, there is no common platform which gathers a collection of schema matching datasets to fulfil this goal. Another problem deal...

Schema mapping that provides a unified view to the users is necessary to manage schema heterogeneity among different data sources. Schema matching is a required task for schema mapping that finds semantic correspondences between entity pairs of schemas. Semi-automatic schema matching systems were developed to overcome manual works for schema mapping. However, such approaches require a high manu...

In this paper, we classify, review, and experimentally compare major methods that are exploited in the definition, adoption, and utilization of element similarity measures in the context of XML schema matching. We aim at presenting a unified view which is useful when developing a new element similarity measure, when implementing an XML schema matching component, when using an XML schema matchin...

ACM Trans. Database Syst. 30(2), 2005, 624-660. PDF, Jayant Madhavan, Philip A. Bernstein, Erhard Rahm Generic Schema Matching with Cupid VLDB'01 fragment research considering keith fixed jayant survey EVALUATION application 425 van 77–94 key matching printed christian errorm molina likelihood dynamic following arrow parent data madhavan heuristic strategie staab quantitative yanni recall 1000 ...

Schema matching, the process of matching between concepts describing the meaning of data in heterogeneous, distributed data sources (e.g. database schemata, XML DTDs, HTML form tags, etc.) is one of the basic operations required by the process of data integration. Recently, several algorithms for automatic schema matching have been proposed and evaluated in the database community. While in many...

Schema matching is the process of developing semantic matches between two or more schemas. The purpose of schema matching is generally either to merge two or more databases, or to enable queries on multiple, heterogeneous databases to be formulated on a single schema (Doan and Halevy 2005). This paper develops a taxonomy of schema matching approaches, classifying them as being based on a combin...

Schema matching is one of the key challenges in the process of integrating heterogeneous databases which identifies the correspondences among different elements of databases’ schemas. There are several semi-automatic schema matching algorithms, however these algorithms do not exploit most of the available information related to schemas during the process of schema matching which affects on the ...

This paper describes a matching algorithm that can find accurate matches and scales to large XML Schemas with hundreds of nodes. We model XML Schemas as labeled, unordered and rooted trees, and turn the schema matching problem into a tree matching problem. We develop a tree matching algorithm based on the concept of Approximate Common Structures. Compared with the tree edit-distance algorithm a...