Synthesizing an integrated ontology

Beneventano, Domenico; Bergamaschi, Sonia; Guerra, Francesco; Vincini, Maurizio

doi:10.1109/mic.2003.1232517

Cited by 74 publications

(53 citation statements)

References 8 publications

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“…The first extensive survey appeared in 2001 [5] and proposed a solution taxonomy differentiating between schema-and instance-level, element-and structure-level, and linguistic and constraint-based matching, as well as non-reuse and reuse-based approaches. Furthermore, it distinguishes hybrid and composite approaches to combine multiple matchers and reviews several match prototypes, including Cupid [14], SemInt [18], LSD [19], DIKE [20], SimilarityFlooding [21], TranScm [59], and MOMIS [22,41,56]. [13] also surveys newer approaches and prototypes.…”

Section: Related Workmentioning

confidence: 99%

“…Further variations include the use of lexical dictionaries like WordNet for semantic relationships (e.g., in MOMIS [22,41], S-Match [42]), vocabularies for instance classification (e.g., county name recognition in LSD), schema corpora for additional match information (e.g., in [43]), and manually specified correspondences for training instance-based learners (e.g, in Autoplex [24], Automatch [25], LSD, Glue, iMap). COMA++ supports a new reuse approach focusing on existing mappings, which can be generalized for different reuse granularities, e.g., single element correspondences, or fragment-and schema-level match results.…”

Section: Previous Solutions Vs Coma++mentioning

confidence: 99%

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Matching large schemas: Approaches and evaluation

Rahm

2007

Information Systems

185

177

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Current schema matching approaches still have to improve for large and complex Schemas. The large search space increases the likelihood for false matches as well as execution times. Further difficulties for Schema matching are posed by the high expressive power and versatility of modern schema languages, in particular user-defined types and classes, component reuse capabilities, and support for distributed schemas and namespaces. To better assist the user in matching complex schemas, we have developed a new generic schema matching tool, COMA++, providing a library of individual matchers and a flexible infrastructure to combine the matchers and refine their results. Different match strategies can be applied including a new scalable approach to identify context-dependent correspondences between schemas with shared elements and a fragment-based match approach which decomposes a large match task into smaller tasks. We conducted a comprehensive evaluation of the match strategies using large e-Business standard schemas. Besides providing helpful insights for future match implementations, the evaluation demonstrated the practicability of our system for matching large schemas. r

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Section: Related Workmentioning

confidence: 99%

Section: Previous Solutions Vs Coma++mentioning

confidence: 99%

Matching large schemas: Approaches and evaluation

Rahm

2007

Information Systems

185

177

View full text Add to dashboard Cite

show abstract

“…the other WN meanings. For this purpose, we use the WNEditor tool to create/manage the new meaning and to set relationships between it and the WN ones [4]. The WNEditor automatically retrieves a list of candidate WN meanings sharing similarities with the new meaning.…”

Section: Inclusion Of the New Cn Meaning In Wn : The Insertion Of A Nmentioning

confidence: 99%

“…Our method is implemented in the MOMIS (Mediator envirOnment for Multiple Information Sources) system [4,2]. However, it may be applied in general in the context of schema mapping discovery, ontology merging and data integration system.…”

Section: Introductionmentioning

confidence: 99%

Schema Normalization for Improving Schema Matching

Sorrentino

Bergamaschi

Gawinecki

et al. 2009

Conceptual Modeling - ER 2009

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Abstract. Schema matching is the problem of finding relationships among concepts across heterogeneous data sources (heterogeneous in format and in structure). Starting from the "hidden meaning" associated to schema labels (i.e. class/attribute names) it is possible to discover relationships among the elements of different schemata. Lexical annotation (i.e. annotation w.r.t. a thesaurus/lexical resource) helps in associating a "meaning" to schema labels. However, accuracy of semi-automatic lexical annotation methods on real-world schemata suffers from the abundance of non-dictionary words such as compound nouns and word abbreviations. In this work, we address this problem by proposing a method to perform schema labels normalization which increases the number of comparable labels. Unlike other solutions, the method semi-automatically expands abbreviations and annotates compound terms, with a minimal manual effort. We empirically prove that our normalization method helps in the identification of similarities among schema elements of different data sources, thus improving schema matching accuracy.

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“…In (Beneventano et al, 2003), we proposed a partial solution to the semantic enrichment of a GVV by providing a semantic annotation of all the Global Classes of the GVV with respect to the WordNet lexical database 2 , and thus providing each term with a well-understood meaning. Relevant Values will semantically enrich a GVV, since they provide semantic information about the data sources the GVV refers to.…”

Section: Introductionmentioning

confidence: 99%

Relevant Values: New Metadata to Provide Insight on Attribute Values at Schema Level

Bergamaschi¹,

Orsini²,

Guerra³

et al. 2007

Proceedings of the Ninth International Conference on Enterprise Information Systems

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Abstract:Research on data integration has provided languages and systems able to guarantee an integrated intensional representation of a given set of data sources. A significant limitation common to most proposals is that only intensional knowledge is considered, with little or no consideration for extensional knowledge.In this paper we propose a technique to enrich the intension of an attribute with a new sort of metadata: the "relevant values", extracted from the attribute values. Relevant values enrich schemata with domain knowledge; moreover they can be exploited by a user in the interactive process of creating/refining a query. The technique, fully implemented in a prototype, is automatic, independent of the attribute domain and it is based on data mining clustering techniques and emerging semantics from data values. It is parametrized with various metrics for similarity measures and is a viable tool for dealing with frequently changing sources, as in the Semantic Web context.

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Synthesizing an integrated ontology

Cited by 74 publications

References 8 publications

Matching large schemas: Approaches and evaluation

Matching large schemas: Approaches and evaluation

Schema Normalization for Improving Schema Matching

Relevant Values: New Metadata to Provide Insight on Attribute Values at Schema Level

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