A survey of approaches to automatic schema matching

Rahm, Erhard; Bernstein, Philip A.

doi:10.1007/s007780100057

Cited by 2,489 publications

(1,691 citation statements)

References 34 publications

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“…To display a set of objects, possibly from disparate data sources, we perform run-time schema matching to select attributes that best match the visualization specifications. Schema matching has been studied extensively (see Rahm and Bernstein [33] for a survey). However, our problem domain, where visualization requirements motivate the reorganization of semi-structured data, poses several novel challenges.…”

Section: Related Workmentioning

confidence: 99%

Visualization of Heterogeneous Data

Cammarano

Dong

Chan

et al. 2007

IEEE Trans. Visual. Comput. Graphics

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Abstract-Both the Resource Description Framework (RDF), used in the semantic web, and Maya Viz u-forms represent data as a graph of objects connected by labeled edges. Existing systems for flexible visualization of this kind of data require manual specification of the possible visualization roles for each data attribute. When the schema is large and unfamiliar, this requirement inhibits exploratory visualization by requiring a costly up-front data integration step. To eliminate this step, we propose an automatic technique for mapping data attributes to visualization attributes. We formulate this as a schema matching problem, finding appropriate paths in the data model for each required visualization attribute in a visualization template.

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Section: Related Workmentioning

confidence: 99%

Visualization of Heterogeneous Data

Cammarano

Dong

Chan

et al. 2007

IEEE Trans. Visual. Comput. Graphics

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“…Approaches to schema matching are surveyed in [6] which categorizes approaches based on the type of information used to compute the match result. Such information may include linguistic information about the names of elements, constraint information such as keys and IS-A relationships, and structural information such as the set of component elements of a given element.…”

Section: Approaches To Aligning Ontologiesmentioning

confidence: 99%

“…Other algorithms also exploit instance information (e.g., [6,10]). In our study, the instances of anatomical classes correspond to the organs, tissues and cells of individual persons (e.g., this author's liver).…”

Section: Approaches To Aligning Ontologiesmentioning

confidence: 99%

Comparing two approaches for aligning representations of anatomy

Zhang

Mork

Bodenreider

et al. 2007

Artificial Intelligence in Medicine

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Objective-To analyze the comparison, through their results, of two distinct approaches applied to aligning two representations of anatomy.Materials-Both approaches use a combination of lexical and structural techniques. In addition, the first approach takes advantage of domain knowledge, while the second approach treats alignment as a special case of schema matching. The same versions of FMA and GALEN were aligned by each approach. 2199 concept matches were obtained by both approaches.Methods and results-For matches identified by one approach only (337 and 336 respectively), we analyzed the reasons that caused the other approach to fail. Conclusions-The first approach could be improved by addressing partial lexical matches and identifying matches based solely on structural similarity. The second approach may be improved by taking into account synonyms in FMA and identifying semantic mismatches. However, only 33% of the possible one-to-one matches among anatomical concepts were identified by the two approaches together. New directions need to be explored in order to handle more complex matches.

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“…We are thus planning to analyze, whether existing (semi-)automatic schema and ontology matching approaches (cf. [10,23]) could provide reasonable results in matching an existing relational schema to a target ontology. The expressiveness within the mapping process depends directly from the query language used, i.e.…”

Section: Combination Of Automatic and Manual Mappingsmentioning

confidence: 99%

Database to Semantic Web Mapping Using RDF Query Languages

Laborda

Conrad

2006

Conceptual Modeling - ER 2006

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Abstract. One of the main drawbacks of the Semantic Web is the lack of semantically rich data, since most of the information is still stored in relational databases. In this paper, we present an approach to map legacy data stored in relational databases into the Semantic Web using virtually any modern RDF query language, as long as it is closed within RDF. Consequently, a Semantic Web developer does not need to learn and adopt a new mapping language, but he may perform the mapping task using his preferred RDF query language. MotivationDespite the vision of a Semantic Web [6] and many efforts helping to realize it, the actual Semantic Web still lacks of enough semantic data. Most information is still modeled and stored in relational databases and thus out of reach for many Semantic Web applications. As a consequence, such applications need to create a corresponding mapping between the relational and the semantic models by themselves for being able to access relational data. Realizing this situation, some efforts have arisen to straighten out this deplorable situation. Most approaches translate relational data into a Semantic Web representation using a proprietary mapping language (cf. Section 2). In [19] we have introduced Relational.OWL, our technique to automatically transform relational data into a machine processable and understandable representation (cf. Section 3.1). Nevertheless, such a representation does not include real semantics, since it converts the schema of a database automatically into an ontology and the data items as its instances, i.e. the data is described as it was in the database. For many Semantic Web applications, this is a reasonable technique, since they are able to quickly access legacy data stored in a relational database using their own built-in functionality. However, such a representation could be inappropriate, if the data has to be processed for further reasoning tasks. In this paper we present how to map relational data into the Semantic Web using virtually any modern RDF query language, as long as the language is closed within RDF, i.e. it returns valid RDF graphs as query results. For this purpose, data and schema components of the original relational database are

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A survey of approaches to automatic schema matching

Cited by 2,489 publications

References 34 publications

Visualization of Heterogeneous Data

Visualization of Heterogeneous Data

Comparing two approaches for aligning representations of anatomy

Database to Semantic Web Mapping Using RDF Query Languages

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