Schema matching is the task of finding semantic correspondences between elements of two schemas. It takes two schemas as input and returns a mapping that identifies corresponding elements in the two schemas. Schema matching is an important and vital step in many schema and data translation and integration applications, such as integration of web data sources, data warehousing, XML message mappi...

Schema matching, the problem of finding mappings between the attributes of two semantically related database schemas, is an important aspect of many database applications such as schema integration, data warehousing, and electronic commerce. Unfortunately, schema matching remains largely a manual, labor-intensive process. Furthermore, the effort required is typically linear in the number of sch...

Schema matching, the problem of nding mappings between the attributes of two semantically related database schemas, is an important aspect of many database applications such as schema integration, data warehousing, and electronic commerce. Unfortunately, schema matching remains largely a manual, labor-intensive process. Furthermore, the eeort required is typically linear in the number of schema...

Automating schema matching is challenging. Previous approaches (e.g. [DDH01, RB01]) to automating schema matching focus on computing direct matches between two schemas. Schemas, however, rarely match directly. Thus, to complete the task of schema matching, we must also compute indirect matches. In this paper, we focus on recognizing expected values as a technique to discover many direct and ind...

Correctly generating a structured query (e.g., an XQuery or a SQL query) requires the user to have a full understanding of the database schema, which can be a daunting task. Alternative query models have been proposed to give users the ability to query the database without schema knowledge. Those models, including simple keyword search and labeled keyword search, aim to extract meaningful data ...

Ontology and schema matching are well established techniques, which have been applied in various integration scenarios, e.g., web service composition and database integration. Consequently, matching tools enabling automatic matching of various kinds of schemas are available. In the field of model-driven engineering, in contrast to schema and ontology integration, the integration of modeling lan...

This paper tends to provide an answer to a difficult problem: Matching large XML schemas. Scalable Matching acquires a long execution time other than decreasing the quality of matches. In this paper, we propose an XML schema decomposition approach as a solution for large schema matching problem. The presented approach identifies the common structures between and within XML schemas, and decompos...

Schema/ontology/classification matching is a critical problem in many application domains, such as, schema/ontology/classification integration, data warehouses, e-commerce, web services coordination, Semantic Web, semantic query processing, etc. We think of Match as an operator which takes two graph-like structures and produces a mapping between semantically related nodes. Semantic matching is ...

While there have been some evaluations and surveys of these evaluations, the overall effectiveness of currently available automatic schema matching systems is largely unclear. This is mainly because either the evaluations were conducted in diverse ways making it difficult to assess the effectiveness of each single system, or they were based on previously published information rather than on act...

In most suggested systems aiming to enable interoperability and collaboration among heterogeneous databases, schema matching and integration is performed manually. The SASMINT system introduced in this paper proposes a (semi-) automated approach to tackle the following: 1) identification of the syntactic/semantic/structural similarities between the donor and recipient schemas to resolve their h...