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A system for flexible combination of schema matching approaches

A system for flexible combination of schema matching approaches 2026

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The document presents COMA, a flexible schema matching system developed by Hong-Hai Do and Erhard Rahm at the University of Leipzig. It addresses the challenges of schema matching by combining various match algorithms to improve accuracy in identifying semantic correspondences between different schemas, such as XML and relational schemas. The system supports both automatic and interactive matching processes, allowing users to provide feedback and refine results. COMA includes a library of matchers, including novel reuse-oriented approaches that leverage previous match results to enhance efficiency. The paper evaluates the effectiveness of different matcher combinations and strategies, demonstrating that composite approaches significantly improve match quality compared to single techniques.

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Click ‘Get Form’ to open it in the editor.
Begin by reviewing the introduction section, which outlines the purpose of the COMA system and its capabilities in schema matching.
Fill in your details in the provided fields, ensuring accuracy as this information is crucial for processing your schema matching tasks.
Navigate through each section of the form, paying close attention to specific match algorithms you wish to utilize. Select from simple, hybrid, or reuse-oriented matchers based on your needs.
Utilize the user feedback options to refine your matches iteratively. This allows you to confirm or reject proposed matches effectively.
Once all fields are completed and reviewed, submit the form for processing. You can track progress and results directly within our platform.

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What is a schema mismatch?

A schema defines the structure of a database or dataset, including tables, columns, data types, and relationships between tables. A schema mismatch occurs when there is a discrepancy between the expected schema and the actual data.

What is the difference between mapping and schema?

For this article, we differentiate the two as follows: schema matching is the process of identifying that two objects are semantically related (scope of this article) while mapping refers to the transformations between the objects. For example, in the two schemas DB1. Student (Name, SSN, Level, Major, Marks) and DB2.

How to do schema matching?

Schema matching starts with trying to identify columns that contain the same type of information. Most existing schema matchers do this by computing a number of different distance measures for each possible pair of columns and then applying some rule to aggregate these into a single score for each column pair.

What is schema matching?

Definition. Schema matching is the problem of finding potential associations between elements (most often attributes or relations) of two schemas. Given two schemas S 1 and S 2, a solution to the schema matching problem, called a schema matching (or more often a matching), is a set of matches.

What is schema matching in data integration?

Schema matching in knowledge graphs is the process of identifying and aligning elements from different data schemas to enable integration or interoperability. A schema defines the structure of a knowledge graph, including entity types, relationships, and attributes.

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What is a schema in simple words?

Schemas are mental models found in long-term memory. The brain utilizes such models to organize information about the world. Schemas are essentially built from our memories of our unique experiences.

A system for flexible combination of schema matching approaches 2026

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What is a schema mismatch?

What is the difference between mapping and schema?

How to do schema matching?

What is schema matching?

What is schema matching in data integration?

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What is a schema in simple words?

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